netajik/oppia<gh_stars>0 # coding: utf-8 # # Copyright 2014 The Oppia Authors. All Rights\",\n \"elif activity_type == constants.ACTIVITY_TYPE_COLLECTION: return get_collection_rights(activity_id, strict=False) else: raise Exception( 'Cannot get activity\",\n \"}] if new_status != ACTIVITY_STATUS_PRIVATE: activity_rights.viewer_ids = [] if activity_rights.first_published_msec is None: activity_rights.first_published_msec\",\n \"delete given activity. Args: user: UserActionsInfo. Object having user_id, role and actions for\",\n \"list(dict). A list of commands describing what kind of commit was done. \\\"\\\"\\\"\",\n \"throw an exception from _publish_activity. \\\"\\\"\\\" _publish_activity( committer, exploration_id, constants.ACTIVITY_TYPE_EXPLORATION) def unpublish_exploration(committer, exploration_id):\"\n]","isConversational":false}}},{"rowIdx":2574,"cells":{"ngram":{"kind":"list like","value":["work around this, mypy allows the typing import to be behind a False-y","= [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the typing module is","use - uses the `typing` module, which provides core functionality fundamental to mypy's","a curious maintainer can reach here to read this. In packaging, all static-typing","a no-op at runtime and does not have any run-time overhead by design.","typing in packaging. `mypy` - the static type analysis tool we use -","does not have any run-time overhead by design. As it turns out, `typing`","\"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the typing","be used to remove the need for the types to be accessible directly","in a nicely named fashion so that a curious maintainer can reach here","not have any run-time overhead by design. As it turns out, `typing` is","expect it to be present. To work around this, mypy allows the typing","constant defined by the typing module is False at runtime # but True","functioning. Generally, `typing` would be imported at runtime and used in that fashion","# we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in","`typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a block that","in packaging. `mypy` - the static type analysis tool we use - uses","typing's cast syntax requires calling typing.cast at runtime, but we don't # want","around this, mypy allows the typing import to be behind a False-y optional","as `cast` and re-implement typing.cast's # runtime behavior in a block that is","to remove the need for the types to be accessible directly during runtime.","imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from","The TYPE_CHECKING constant defined by the typing module is False at runtime #","turns out, `typing` is not vendorable - it uses separate sources for Python","from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The","codebase can not expect it to be present. To work around this, mypy","maintainer can reach here to read this. In packaging, all static-typing related imports","neatly implementing static typing in packaging. `mypy` - the static type analysis tool","Thus, this codebase can not expect it to be present. To work around","to import typing at runtime. Here, we inform the type checkers that #","import to be behind a False-y optional to prevent it from running at","so that a curious maintainer can reach here to read this. In packaging,","at runtime and does not have any run-time overhead by design. As it","provides core functionality fundamental to mypy's functioning. Generally, `typing` would be imported at","\"\"\"For neatly implementing static typing in packaging. `mypy` - the static type analysis","it acts as a no-op at runtime and does not have any run-time","`typing` module, which provides core functionality fundamental to mypy's functioning. Generally, `typing` would","this, mypy allows the typing import to be behind a False-y optional to","guard in a nicely named fashion so that a curious maintainer can reach","a block that is ignored by type checkers. if TYPE_CHECKING: # pragma: no","behavior in a block that is ignored by type checkers. if TYPE_CHECKING: #","remove the need for the types to be accessible directly during runtime. This","- uses the `typing` module, which provides core functionality fundamental to mypy's functioning.","the False-y guard in a nicely named fashion so that a curious maintainer","and does not have any run-time overhead by design. As it turns out,","have any run-time overhead by design. As it turns out, `typing` is not","runtime and used in that fashion - it acts as a no-op at","typing.cast at runtime, but we don't # want to import typing at runtime.","want to import typing at runtime. Here, we inform the type checkers that","checkers. if TYPE_CHECKING: # pragma: no cover # not executed at runtime from","type analysis tool we use - uses the `typing` module, which provides core","be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import","it uses separate sources for Python 2/Python 3. Thus, this codebase can not","the typing import to be behind a False-y optional to prevent it from","at runtime. Here, we inform the type checkers that # we're importing `typing.cast`","module provides the False-y guard in a nicely named fashion so that a","runtime # but True while type checking. TYPE_CHECKING = False # pragma: no","core functionality fundamental to mypy's functioning. Generally, `typing` would be imported at runtime","imported at runtime and used in that fashion - it acts as a","be present. To work around this, mypy allows the typing import to be","2/Python 3. Thus, this codebase can not expect it to be present. To","fundamental to mypy's functioning. Generally, `typing` would be imported at runtime and used","To work around this, mypy allows the typing import to be behind a","if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"]","allows the typing import to be behind a False-y optional to prevent it","runtime from typing import cast else: # executed at runtime def cast(type_, value):","fashion - it acts as a no-op at runtime and does not have","calling typing.cast at runtime, but we don't # want to import typing at","syntax requires calling typing.cast at runtime, but we don't # want to import","inform the type checkers that # we're importing `typing.cast` as `cast` and re-implement","is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover # not","`mypy` - the static type analysis tool we use - uses the `typing`","provides the False-y guard in a nicely named fashion so that a curious","follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216","TYPE_CHECKING: # pragma: no cover # not executed at runtime from typing import","re-implement typing.cast's # runtime behavior in a block that is ignored by type","used in that fashion - it acts as a no-op at runtime and","acts as a no-op at runtime and does not have any run-time overhead","not expect it to be present. To work around this, mypy allows the","at runtime, but we don't # want to import typing at runtime. Here,","we inform the type checkers that # we're importing `typing.cast` as `cast` and","this. In packaging, all static-typing related imports should be guarded as follows: from","implementing static typing in packaging. `mypy` - the static type analysis tool we","related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING:","type-comments can be used to remove the need for the types to be","static-typing related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if","executed at runtime from typing import cast else: # executed at runtime def","accessible directly during runtime. This module provides the False-y guard in a nicely","packaging. `mypy` - the static type analysis tool we use - uses the","Generally, `typing` would be imported at runtime and used in that fashion -","be behind a False-y optional to prevent it from running at runtime and","not executed at runtime from typing import cast else: # executed at runtime","https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the","that a curious maintainer can reach here to read this. In packaging, all","as a no-op at runtime and does not have any run-time overhead by","mypy allows the typing import to be behind a False-y optional to prevent","it to be present. To work around this, mypy allows the typing import","to read this. In packaging, all static-typing related imports should be guarded as","behind a False-y optional to prevent it from running at runtime and type-comments","we don't # want to import typing at runtime. Here, we inform the","`typing` would be imported at runtime and used in that fashion - it","by the typing module is False at runtime # but True while type","at runtime and used in that fashion - it acts as a no-op","typing import to be behind a False-y optional to prevent it from running","- it acts as a no-op at runtime and does not have any","and used in that fashion - it acts as a no-op at runtime","runtime. Here, we inform the type checkers that # we're importing `typing.cast` as","but True while type checking. TYPE_CHECKING = False # pragma: no cover #","- the static type analysis tool we use - uses the `typing` module,","typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING","runtime behavior in a block that is ignored by type checkers. if TYPE_CHECKING:","from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\"","__all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the typing module","runtime, but we don't # want to import typing at runtime. Here, we","# runtime behavior in a block that is ignored by type checkers. if","pragma: no cover # not executed at runtime from typing import cast else:","mypy's functioning. Generally, `typing` would be imported at runtime and used in that","# want to import typing at runtime. Here, we inform the type checkers","type checking. TYPE_CHECKING = False # pragma: no cover # typing's cast syntax","not vendorable - it uses separate sources for Python 2/Python 3. Thus, this","import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ =","need for the types to be accessible directly during runtime. This module provides","sources for Python 2/Python 3. Thus, this codebase can not expect it to","be imported at runtime and used in that fashion - it acts as","TYPE_CHECKING constant defined by the typing module is False at runtime # but","at runtime from typing import cast else: # executed at runtime def cast(type_,","cover # typing's cast syntax requires calling typing.cast at runtime, but we don't","packaging, all static-typing related imports should be guarded as follows: from packaging._typing import","# not executed at runtime from typing import cast else: # executed at","can reach here to read this. In packaging, all static-typing related imports should","False-y optional to prevent it from running at runtime and type-comments can be","running at runtime and type-comments can be used to remove the need for","TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\",","TYPE_CHECKING = False # pragma: no cover # typing's cast syntax requires calling","present. To work around this, mypy allows the typing import to be behind","and type-comments can be used to remove the need for the types to","that # we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior","block that is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover","to be present. To work around this, mypy allows the typing import to","# The TYPE_CHECKING constant defined by the typing module is False at runtime","we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a","True while type checking. TYPE_CHECKING = False # pragma: no cover # typing's","static typing in packaging. `mypy` - the static type analysis tool we use","as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref:","cast syntax requires calling typing.cast at runtime, but we don't # want to","typing at runtime. Here, we inform the type checkers that # we're importing","the static type analysis tool we use - uses the `typing` module, which","to mypy's functioning. Generally, `typing` would be imported at runtime and used in","- it uses separate sources for Python 2/Python 3. Thus, this codebase can","import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant","defined by the typing module is False at runtime # but True while","no cover # typing's cast syntax requires calling typing.cast at runtime, but we","import typing at runtime. Here, we inform the type checkers that # we're","functionality fundamental to mypy's functioning. Generally, `typing` would be imported at runtime and","types to be accessible directly during runtime. This module provides the False-y guard","runtime and does not have any run-time overhead by design. As it turns","from running at runtime and type-comments can be used to remove the need","# typing's cast syntax requires calling typing.cast at runtime, but we don't #","# but True while type checking. TYPE_CHECKING = False # pragma: no cover","don't # want to import typing at runtime. Here, we inform the type","the type checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's","the `typing` module, which provides core functionality fundamental to mypy's functioning. Generally, `typing`","uses separate sources for Python 2/Python 3. Thus, this codebase can not expect","This module provides the False-y guard in a nicely named fashion so that","while type checking. TYPE_CHECKING = False # pragma: no cover # typing's cast","\"cast\"] # The TYPE_CHECKING constant defined by the typing module is False at","it turns out, `typing` is not vendorable - it uses separate sources for","guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ...","in that fashion - it acts as a no-op at runtime and does","# pragma: no cover # typing's cast syntax requires calling typing.cast at runtime,","out, `typing` is not vendorable - it uses separate sources for Python 2/Python","checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime","As it turns out, `typing` is not vendorable - it uses separate sources","optional to prevent it from running at runtime and type-comments can be used","tool we use - uses the `typing` module, which provides core functionality fundamental","typing.cast's # runtime behavior in a block that is ignored by type checkers.","we use - uses the `typing` module, which provides core functionality fundamental to","to prevent it from running at runtime and type-comments can be used to","read this. In packaging, all static-typing related imports should be guarded as follows:","that fashion - it acts as a no-op at runtime and does not","by type checkers. if TYPE_CHECKING: # pragma: no cover # not executed at","any run-time overhead by design. As it turns out, `typing` is not vendorable","at runtime and type-comments can be used to remove the need for the","... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined","in a block that is ignored by type checkers. if TYPE_CHECKING: # pragma:","3. Thus, this codebase can not expect it to be present. To work","`cast` and re-implement typing.cast's # runtime behavior in a block that is ignored","is False at runtime # but True while type checking. TYPE_CHECKING = False","# pragma: no cover # not executed at runtime from typing import cast","but we don't # want to import typing at runtime. Here, we inform","In packaging, all static-typing related imports should be guarded as follows: from packaging._typing","typing import cast else: # executed at runtime def cast(type_, value): # noqa","by design. As it turns out, `typing` is not vendorable - it uses","= False # pragma: no cover # typing's cast syntax requires calling typing.cast","the typing module is False at runtime # but True while type checking.","no-op at runtime and does not have any run-time overhead by design. As","requires calling typing.cast at runtime, but we don't # want to import typing","for the types to be accessible directly during runtime. This module provides the","analysis tool we use - uses the `typing` module, which provides core functionality","cast else: # executed at runtime def cast(type_, value): # noqa return value","is not vendorable - it uses separate sources for Python 2/Python 3. Thus,","separate sources for Python 2/Python 3. Thus, this codebase can not expect it","and re-implement typing.cast's # runtime behavior in a block that is ignored by","from typing import cast else: # executed at runtime def cast(type_, value): #","module, which provides core functionality fundamental to mypy's functioning. Generally, `typing` would be","uses the `typing` module, which provides core functionality fundamental to mypy's functioning. Generally,","named fashion so that a curious maintainer can reach here to read this.","here to read this. In packaging, all static-typing related imports should be guarded","all static-typing related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING","would be imported at runtime and used in that fashion - it acts","ignored by type checkers. if TYPE_CHECKING: # pragma: no cover # not executed","checking. TYPE_CHECKING = False # pragma: no cover # typing's cast syntax requires","fashion so that a curious maintainer can reach here to read this. In","cover # not executed at runtime from typing import cast else: # executed","run-time overhead by design. As it turns out, `typing` is not vendorable -","TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] #","static type analysis tool we use - uses the `typing` module, which provides","for Python 2/Python 3. Thus, this codebase can not expect it to be","typing module is False at runtime # but True while type checking. TYPE_CHECKING","module is False at runtime # but True while type checking. TYPE_CHECKING =","packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__","the need for the types to be accessible directly during runtime. This module","it from running at runtime and type-comments can be used to remove the","can not expect it to be present. To work around this, mypy allows","that is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover #","during runtime. This module provides the False-y guard in a nicely named fashion","reach here to read this. In packaging, all static-typing related imports should be","curious maintainer can reach here to read this. In packaging, all static-typing related","to be behind a False-y optional to prevent it from running at runtime","a nicely named fashion so that a curious maintainer can reach here to","at runtime # but True while type checking. TYPE_CHECKING = False # pragma:","the types to be accessible directly during runtime. This module provides the False-y","pragma: no cover # typing's cast syntax requires calling typing.cast at runtime, but","importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a block","Python 2/Python 3. Thus, this codebase can not expect it to be present.","which provides core functionality fundamental to mypy's functioning. Generally, `typing` would be imported","overhead by design. As it turns out, `typing` is not vendorable - it","this codebase can not expect it to be present. To work around this,","should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing","import cast else: # executed at runtime def cast(type_, value): # noqa return","vendorable - it uses separate sources for Python 2/Python 3. Thus, this codebase","a False-y optional to prevent it from running at runtime and type-comments can","type checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's #","prevent it from running at runtime and type-comments can be used to remove","type checkers. if TYPE_CHECKING: # pragma: no cover # not executed at runtime","be accessible directly during runtime. This module provides the False-y guard in a","used to remove the need for the types to be accessible directly during","runtime. This module provides the False-y guard in a nicely named fashion so","no cover # not executed at runtime from typing import cast else: #","runtime and type-comments can be used to remove the need for the types","False at runtime # but True while type checking. TYPE_CHECKING = False #","design. As it turns out, `typing` is not vendorable - it uses separate","False-y guard in a nicely named fashion so that a curious maintainer can","`typing` is not vendorable - it uses separate sources for Python 2/Python 3.","nicely named fashion so that a curious maintainer can reach here to read","Here, we inform the type checkers that # we're importing `typing.cast` as `cast`","directly during runtime. This module provides the False-y guard in a nicely named","[\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the typing module is False","Ref: https://github.com/python/mypy/issues/3216 \"\"\" __all__ = [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by","False # pragma: no cover # typing's cast syntax requires calling typing.cast at","if TYPE_CHECKING: # pragma: no cover # not executed at runtime from typing","can be used to remove the need for the types to be accessible","to be accessible directly during runtime. This module provides the False-y guard in"],"string":"[\n \"work around this, mypy allows the typing import to be behind a False-y\",\n \"= [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by the typing module is\",\n \"use - uses the `typing` module, which provides core functionality fundamental to mypy's\",\n \"a curious maintainer can reach here to read this. In packaging, all static-typing\",\n \"a no-op at runtime and does not have any run-time overhead by design.\",\n \"typing in packaging. `mypy` - the static type analysis tool we use -\",\n \"does not have any run-time overhead by design. As it turns out, `typing`\",\n \"\\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by the typing\",\n \"be used to remove the need for the types to be accessible directly\",\n \"in a nicely named fashion so that a curious maintainer can reach here\",\n \"not have any run-time overhead by design. As it turns out, `typing` is\",\n \"expect it to be present. To work around this, mypy allows the typing\",\n \"constant defined by the typing module is False at runtime # but True\",\n \"functioning. Generally, `typing` would be imported at runtime and used in that fashion\",\n \"# we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in\",\n \"`typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a block that\",\n \"in packaging. `mypy` - the static type analysis tool we use - uses\",\n \"typing's cast syntax requires calling typing.cast at runtime, but we don't # want\",\n \"around this, mypy allows the typing import to be behind a False-y optional\",\n \"as `cast` and re-implement typing.cast's # runtime behavior in a block that is\",\n \"to remove the need for the types to be accessible directly during runtime.\",\n \"imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from\",\n \"The TYPE_CHECKING constant defined by the typing module is False at runtime #\",\n \"turns out, `typing` is not vendorable - it uses separate sources for Python\",\n \"from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The\",\n \"codebase can not expect it to be present. To work around this, mypy\",\n \"maintainer can reach here to read this. In packaging, all static-typing related imports\",\n \"neatly implementing static typing in packaging. `mypy` - the static type analysis tool\",\n \"Thus, this codebase can not expect it to be present. To work around\",\n \"to import typing at runtime. Here, we inform the type checkers that #\",\n \"import to be behind a False-y optional to prevent it from running at\",\n \"so that a curious maintainer can reach here to read this. In packaging,\",\n \"at runtime and does not have any run-time overhead by design. As it\",\n \"provides core functionality fundamental to mypy's functioning. Generally, `typing` would be imported at\",\n \"\\\"\\\"\\\"For neatly implementing static typing in packaging. `mypy` - the static type analysis\",\n \"it acts as a no-op at runtime and does not have any run-time\",\n \"`typing` module, which provides core functionality fundamental to mypy's functioning. Generally, `typing` would\",\n \"this, mypy allows the typing import to be behind a False-y optional to\",\n \"guard in a nicely named fashion so that a curious maintainer can reach\",\n \"a block that is ignored by type checkers. if TYPE_CHECKING: # pragma: no\",\n \"behavior in a block that is ignored by type checkers. if TYPE_CHECKING: #\",\n \"remove the need for the types to be accessible directly during runtime. This\",\n \"- uses the `typing` module, which provides core functionality fundamental to mypy's functioning.\",\n \"the False-y guard in a nicely named fashion so that a curious maintainer\",\n \"and does not have any run-time overhead by design. As it turns out,\",\n \"have any run-time overhead by design. As it turns out, `typing` is not\",\n \"runtime and used in that fashion - it acts as a no-op at\",\n \"typing.cast at runtime, but we don't # want to import typing at runtime.\",\n \"want to import typing at runtime. Here, we inform the type checkers that\",\n \"checkers. if TYPE_CHECKING: # pragma: no cover # not executed at runtime from\",\n \"type analysis tool we use - uses the `typing` module, which provides core\",\n \"be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import\",\n \"it uses separate sources for Python 2/Python 3. Thus, this codebase can not\",\n \"the typing import to be behind a False-y optional to prevent it from\",\n \"at runtime. Here, we inform the type checkers that # we're importing `typing.cast`\",\n \"module provides the False-y guard in a nicely named fashion so that a\",\n \"runtime # but True while type checking. TYPE_CHECKING = False # pragma: no\",\n \"core functionality fundamental to mypy's functioning. Generally, `typing` would be imported at runtime\",\n \"imported at runtime and used in that fashion - it acts as a\",\n \"be present. To work around this, mypy allows the typing import to be\",\n \"2/Python 3. Thus, this codebase can not expect it to be present. To\",\n \"fundamental to mypy's functioning. Generally, `typing` would be imported at runtime and used\",\n \"To work around this, mypy allows the typing import to be behind a\",\n \"if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"]\",\n \"allows the typing import to be behind a False-y optional to prevent it\",\n \"runtime from typing import cast else: # executed at runtime def cast(type_, value):\",\n \"fashion - it acts as a no-op at runtime and does not have\",\n \"calling typing.cast at runtime, but we don't # want to import typing at\",\n \"syntax requires calling typing.cast at runtime, but we don't # want to import\",\n \"inform the type checkers that # we're importing `typing.cast` as `cast` and re-implement\",\n \"is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover # not\",\n \"`mypy` - the static type analysis tool we use - uses the `typing`\",\n \"provides the False-y guard in a nicely named fashion so that a curious\",\n \"follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216\",\n \"TYPE_CHECKING: # pragma: no cover # not executed at runtime from typing import\",\n \"re-implement typing.cast's # runtime behavior in a block that is ignored by type\",\n \"used in that fashion - it acts as a no-op at runtime and\",\n \"acts as a no-op at runtime and does not have any run-time overhead\",\n \"not expect it to be present. To work around this, mypy allows the\",\n \"at runtime, but we don't # want to import typing at runtime. Here,\",\n \"we inform the type checkers that # we're importing `typing.cast` as `cast` and\",\n \"this. In packaging, all static-typing related imports should be guarded as follows: from\",\n \"implementing static typing in packaging. `mypy` - the static type analysis tool we\",\n \"related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING:\",\n \"type-comments can be used to remove the need for the types to be\",\n \"static-typing related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING if\",\n \"executed at runtime from typing import cast else: # executed at runtime def\",\n \"accessible directly during runtime. This module provides the False-y guard in a nicely\",\n \"packaging. `mypy` - the static type analysis tool we use - uses the\",\n \"Generally, `typing` would be imported at runtime and used in that fashion -\",\n \"be behind a False-y optional to prevent it from running at runtime and\",\n \"not executed at runtime from typing import cast else: # executed at runtime\",\n \"https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by the\",\n \"that a curious maintainer can reach here to read this. In packaging, all\",\n \"as a no-op at runtime and does not have any run-time overhead by\",\n \"mypy allows the typing import to be behind a False-y optional to prevent\",\n \"it to be present. To work around this, mypy allows the typing import\",\n \"to read this. In packaging, all static-typing related imports should be guarded as\",\n \"behind a False-y optional to prevent it from running at runtime and type-comments\",\n \"we don't # want to import typing at runtime. Here, we inform the\",\n \"`typing` would be imported at runtime and used in that fashion - it\",\n \"by the typing module is False at runtime # but True while type\",\n \"at runtime and used in that fashion - it acts as a no-op\",\n \"typing import to be behind a False-y optional to prevent it from running\",\n \"- it acts as a no-op at runtime and does not have any\",\n \"and used in that fashion - it acts as a no-op at runtime\",\n \"runtime. Here, we inform the type checkers that # we're importing `typing.cast` as\",\n \"but True while type checking. TYPE_CHECKING = False # pragma: no cover #\",\n \"- the static type analysis tool we use - uses the `typing` module,\",\n \"typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING\",\n \"runtime behavior in a block that is ignored by type checkers. if TYPE_CHECKING:\",\n \"from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\"\",\n \"__all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by the typing module\",\n \"runtime, but we don't # want to import typing at runtime. Here, we\",\n \"# runtime behavior in a block that is ignored by type checkers. if\",\n \"pragma: no cover # not executed at runtime from typing import cast else:\",\n \"mypy's functioning. Generally, `typing` would be imported at runtime and used in that\",\n \"# want to import typing at runtime. Here, we inform the type checkers\",\n \"type checking. TYPE_CHECKING = False # pragma: no cover # typing's cast syntax\",\n \"not vendorable - it uses separate sources for Python 2/Python 3. Thus, this\",\n \"import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ =\",\n \"need for the types to be accessible directly during runtime. This module provides\",\n \"sources for Python 2/Python 3. Thus, this codebase can not expect it to\",\n \"be imported at runtime and used in that fashion - it acts as\",\n \"TYPE_CHECKING constant defined by the typing module is False at runtime # but\",\n \"at runtime from typing import cast else: # executed at runtime def cast(type_,\",\n \"cover # typing's cast syntax requires calling typing.cast at runtime, but we don't\",\n \"packaging, all static-typing related imports should be guarded as follows: from packaging._typing import\",\n \"# not executed at runtime from typing import cast else: # executed at\",\n \"can reach here to read this. In packaging, all static-typing related imports should\",\n \"False-y optional to prevent it from running at runtime and type-comments can be\",\n \"running at runtime and type-comments can be used to remove the need for\",\n \"TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\",\",\n \"TYPE_CHECKING = False # pragma: no cover # typing's cast syntax requires calling\",\n \"present. To work around this, mypy allows the typing import to be behind\",\n \"and type-comments can be used to remove the need for the types to\",\n \"that # we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior\",\n \"block that is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover\",\n \"to be present. To work around this, mypy allows the typing import to\",\n \"# The TYPE_CHECKING constant defined by the typing module is False at runtime\",\n \"we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a\",\n \"True while type checking. TYPE_CHECKING = False # pragma: no cover # typing's\",\n \"static typing in packaging. `mypy` - the static type analysis tool we use\",\n \"as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref:\",\n \"cast syntax requires calling typing.cast at runtime, but we don't # want to\",\n \"typing at runtime. Here, we inform the type checkers that # we're importing\",\n \"the static type analysis tool we use - uses the `typing` module, which\",\n \"to mypy's functioning. Generally, `typing` would be imported at runtime and used in\",\n \"- it uses separate sources for Python 2/Python 3. Thus, this codebase can\",\n \"import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant\",\n \"defined by the typing module is False at runtime # but True while\",\n \"no cover # typing's cast syntax requires calling typing.cast at runtime, but we\",\n \"import typing at runtime. Here, we inform the type checkers that # we're\",\n \"functionality fundamental to mypy's functioning. Generally, `typing` would be imported at runtime and\",\n \"types to be accessible directly during runtime. This module provides the False-y guard\",\n \"runtime and does not have any run-time overhead by design. As it turns\",\n \"from running at runtime and type-comments can be used to remove the need\",\n \"# typing's cast syntax requires calling typing.cast at runtime, but we don't #\",\n \"# but True while type checking. TYPE_CHECKING = False # pragma: no cover\",\n \"don't # want to import typing at runtime. Here, we inform the type\",\n \"the type checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's\",\n \"the `typing` module, which provides core functionality fundamental to mypy's functioning. Generally, `typing`\",\n \"uses separate sources for Python 2/Python 3. Thus, this codebase can not expect\",\n \"This module provides the False-y guard in a nicely named fashion so that\",\n \"while type checking. TYPE_CHECKING = False # pragma: no cover # typing's cast\",\n \"\\\"cast\\\"] # The TYPE_CHECKING constant defined by the typing module is False at\",\n \"it turns out, `typing` is not vendorable - it uses separate sources for\",\n \"guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ...\",\n \"in that fashion - it acts as a no-op at runtime and does\",\n \"# pragma: no cover # typing's cast syntax requires calling typing.cast at runtime,\",\n \"out, `typing` is not vendorable - it uses separate sources for Python 2/Python\",\n \"checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's # runtime\",\n \"As it turns out, `typing` is not vendorable - it uses separate sources\",\n \"optional to prevent it from running at runtime and type-comments can be used\",\n \"tool we use - uses the `typing` module, which provides core functionality fundamental\",\n \"typing.cast's # runtime behavior in a block that is ignored by type checkers.\",\n \"we use - uses the `typing` module, which provides core functionality fundamental to\",\n \"to prevent it from running at runtime and type-comments can be used to\",\n \"read this. In packaging, all static-typing related imports should be guarded as follows:\",\n \"that fashion - it acts as a no-op at runtime and does not\",\n \"by type checkers. if TYPE_CHECKING: # pragma: no cover # not executed at\",\n \"any run-time overhead by design. As it turns out, `typing` is not vendorable\",\n \"at runtime and type-comments can be used to remove the need for the\",\n \"... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined\",\n \"in a block that is ignored by type checkers. if TYPE_CHECKING: # pragma:\",\n \"3. Thus, this codebase can not expect it to be present. To work\",\n \"`cast` and re-implement typing.cast's # runtime behavior in a block that is ignored\",\n \"is False at runtime # but True while type checking. TYPE_CHECKING = False\",\n \"# pragma: no cover # not executed at runtime from typing import cast\",\n \"but we don't # want to import typing at runtime. Here, we inform\",\n \"In packaging, all static-typing related imports should be guarded as follows: from packaging._typing\",\n \"typing import cast else: # executed at runtime def cast(type_, value): # noqa\",\n \"by design. As it turns out, `typing` is not vendorable - it uses\",\n \"= False # pragma: no cover # typing's cast syntax requires calling typing.cast\",\n \"the typing module is False at runtime # but True while type checking.\",\n \"no-op at runtime and does not have any run-time overhead by design. As\",\n \"requires calling typing.cast at runtime, but we don't # want to import typing\",\n \"for the types to be accessible directly during runtime. This module provides the\",\n \"analysis tool we use - uses the `typing` module, which provides core functionality\",\n \"cast else: # executed at runtime def cast(type_, value): # noqa return value\",\n \"is not vendorable - it uses separate sources for Python 2/Python 3. Thus,\",\n \"separate sources for Python 2/Python 3. Thus, this codebase can not expect it\",\n \"and re-implement typing.cast's # runtime behavior in a block that is ignored by\",\n \"from typing import cast else: # executed at runtime def cast(type_, value): #\",\n \"module, which provides core functionality fundamental to mypy's functioning. Generally, `typing` would be\",\n \"uses the `typing` module, which provides core functionality fundamental to mypy's functioning. Generally,\",\n \"named fashion so that a curious maintainer can reach here to read this.\",\n \"here to read this. In packaging, all static-typing related imports should be guarded\",\n \"all static-typing related imports should be guarded as follows: from packaging._typing import TYPE_CHECKING\",\n \"would be imported at runtime and used in that fashion - it acts\",\n \"ignored by type checkers. if TYPE_CHECKING: # pragma: no cover # not executed\",\n \"checking. TYPE_CHECKING = False # pragma: no cover # typing's cast syntax requires\",\n \"fashion so that a curious maintainer can reach here to read this. In\",\n \"cover # not executed at runtime from typing import cast else: # executed\",\n \"run-time overhead by design. As it turns out, `typing` is not vendorable -\",\n \"TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] #\",\n \"static type analysis tool we use - uses the `typing` module, which provides\",\n \"for Python 2/Python 3. Thus, this codebase can not expect it to be\",\n \"typing module is False at runtime # but True while type checking. TYPE_CHECKING\",\n \"module is False at runtime # but True while type checking. TYPE_CHECKING =\",\n \"packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing import ... Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__\",\n \"the need for the types to be accessible directly during runtime. This module\",\n \"it from running at runtime and type-comments can be used to remove the\",\n \"can not expect it to be present. To work around this, mypy allows\",\n \"that is ignored by type checkers. if TYPE_CHECKING: # pragma: no cover #\",\n \"during runtime. This module provides the False-y guard in a nicely named fashion\",\n \"reach here to read this. In packaging, all static-typing related imports should be\",\n \"curious maintainer can reach here to read this. In packaging, all static-typing related\",\n \"to be behind a False-y optional to prevent it from running at runtime\",\n \"a nicely named fashion so that a curious maintainer can reach here to\",\n \"at runtime # but True while type checking. TYPE_CHECKING = False # pragma:\",\n \"the types to be accessible directly during runtime. This module provides the False-y\",\n \"pragma: no cover # typing's cast syntax requires calling typing.cast at runtime, but\",\n \"importing `typing.cast` as `cast` and re-implement typing.cast's # runtime behavior in a block\",\n \"Python 2/Python 3. Thus, this codebase can not expect it to be present.\",\n \"which provides core functionality fundamental to mypy's functioning. Generally, `typing` would be imported\",\n \"overhead by design. As it turns out, `typing` is not vendorable - it\",\n \"this codebase can not expect it to be present. To work around this,\",\n \"should be guarded as follows: from packaging._typing import TYPE_CHECKING if TYPE_CHECKING: from typing\",\n \"import cast else: # executed at runtime def cast(type_, value): # noqa return\",\n \"vendorable - it uses separate sources for Python 2/Python 3. Thus, this codebase\",\n \"a False-y optional to prevent it from running at runtime and type-comments can\",\n \"type checkers that # we're importing `typing.cast` as `cast` and re-implement typing.cast's #\",\n \"prevent it from running at runtime and type-comments can be used to remove\",\n \"type checkers. if TYPE_CHECKING: # pragma: no cover # not executed at runtime\",\n \"be accessible directly during runtime. This module provides the False-y guard in a\",\n \"used to remove the need for the types to be accessible directly during\",\n \"runtime. This module provides the False-y guard in a nicely named fashion so\",\n \"no cover # not executed at runtime from typing import cast else: #\",\n \"runtime and type-comments can be used to remove the need for the types\",\n \"False at runtime # but True while type checking. TYPE_CHECKING = False #\",\n \"design. As it turns out, `typing` is not vendorable - it uses separate\",\n \"False-y guard in a nicely named fashion so that a curious maintainer can\",\n \"`typing` is not vendorable - it uses separate sources for Python 2/Python 3.\",\n \"nicely named fashion so that a curious maintainer can reach here to read\",\n \"Here, we inform the type checkers that # we're importing `typing.cast` as `cast`\",\n \"directly during runtime. This module provides the False-y guard in a nicely named\",\n \"[\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by the typing module is False\",\n \"Ref: https://github.com/python/mypy/issues/3216 \\\"\\\"\\\" __all__ = [\\\"TYPE_CHECKING\\\", \\\"cast\\\"] # The TYPE_CHECKING constant defined by\",\n \"False # pragma: no cover # typing's cast syntax requires calling typing.cast at\",\n \"if TYPE_CHECKING: # pragma: no cover # not executed at runtime from typing\",\n \"can be used to remove the need for the types to be accessible\",\n \"to be accessible directly during runtime. This module provides the False-y guard in\"\n]","isConversational":false}}},{"rowIdx":2575,"cells":{"ngram":{"kind":"list like","value":["// 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam","== 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12.","between read value and gas cmd. # This prevents unexpected pedal range rescaling","elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else:","if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack:","True if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req","cancel the system even if we are using lat only control if (frame","CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name,","CS.out.vEgo < 12. # at low speed we always assume the lead is","at 100Hz seem to allow a higher rate limit, as the rate limit","in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn))","= hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False","causes a bad fault sound so play a good sound instead send_ui =","enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request","MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert =","brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive","import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker","TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams","can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2))","bad fault sound so play a good sound instead send_ui = True if","entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill =","NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm","as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req,","*** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step","opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer =","or CS.out.vEgo < 12. # at low speed we always assume the lead","1 # on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill","% 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if","0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy()","interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN,","0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame","= 1 # on entering standstill, send standstill request if CS.out.standstill and not","from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer","in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: #","self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead,","frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake","for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step ==","send exactly zero if gas cmd is zero. Interceptor will send the max","zero. Interceptor will send the max between read value and gas cmd. #","lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type,","+ PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset =","else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer","import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command,","not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8:","make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from","0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead,","if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static","abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1","in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType","False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and","if we are using lat only control if (frame % 3 == 0","frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl,","CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req =","CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active, CS, frame,","= 0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd,","delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not","very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE =","gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame //","0 else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses","= 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line,","assume the lead is present so ACC can be engaged # Lexus IS","VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or","than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd","> 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 #","something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in","import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self,","message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd,","and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode.","lead is present so ACC can be engaged # Lexus IS uses a","apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer,","a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not","can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at","100Hz but we send asap if: # - there is something to display","MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset","\\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR,","// 2)) # we can spam can to cancel the system even if","present so ACC can be engaged # Lexus IS uses a different cancellation","# RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER,","interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo,","and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus,","the pcm to disengage causes a bad fault sound so play a good","steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams)","to disengage causes a bad fault sound so play a good sound instead","enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): #","speed we always assume the lead is present so ACC can be engaged","pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault sound so","car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame % 2 ==","from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values","{0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this","pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2","steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert)","sending it at 100Hz seem to allow a higher rate limit, as the","fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step, vl) in","0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist","self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault","CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4,","pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else:","whitelist 0x191 in the panda # if frame % 2 == 0: #","is at 100Hz but we send asap if: # - there is something","forcing the pcm to disengage causes a bad fault sound so play a","update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart):","gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,","low speed we always assume the lead is present so ACC can be","pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor","steer_alert) and not self.alert_active) or \\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui","100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled))","== 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) #","# can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame //","42Hz, with counter adding alternatively 1 and 2; # sending it at 100Hz","self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel","limit, as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer,","Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame","# we can spam can to cancel the system even if we are","CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor","CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req","in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX","selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import","TODO: probably can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled.","% 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer,","and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame","elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3,","pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command =","if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd,","different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl:","(actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd","not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True","(not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active","create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command","frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but","msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame %","# forcing the pcm to disengage causes a bad fault sound so play","the system even if we are using lat only control if (frame %","to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired,","create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams","MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset)","at low speed we always assume the lead is present so ACC can","and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero.","+ PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd","+ pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd =","if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line,","[0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0,","# Cut steering while we're in a known fault state (2s) if not","can be engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd","hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if","0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0.,","apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably","the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0,","and not self.alert_active) or \\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui =","False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs","2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the","fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and","create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from","fault state (2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg)","CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer,","self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\"steer {0} {1}","% 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs ***","# TODO: probably can delete this. CS.pcm_acc_status uses a different signal # than","enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0","self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if","100Hz seem to allow a higher rate limit, as the rate limit seems","instead send_ui = True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer,","CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if","ACC can be engaged # Lexus IS uses a different cancellation message if","self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def","STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars:","static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame","= apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering","gas cmd is zero. Interceptor will send the max between read value and","= not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a","# LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda","if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0,","= hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and","control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota","apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command,","self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the","sound instead send_ui = True if (frame % 100 == 0 or send_ui):","pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and","and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered","if: # - there is something to display # - there is something","= False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel =","= opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self,","signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and","they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 #","if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2))","clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command,","limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame","displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui","CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set","[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,","<filename>selfdrive/car/toyota/carcontroller.py from cereal import car from common.numpy_fast import clip, interp from selfdrive.car import","interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE *","= False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack =","apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while","= lead or CS.out.vEgo < 12. # at low speed we always assume","bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas","even if we are using lat only control if (frame % 3 ==","different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill,","#*** control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) #","0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +","VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req = False","seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame %","car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active =","this message at 42Hz, with counter adding alternatively 1 and 2; # sending","opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled,","imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2","0x191 in the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer,","CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn))","create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\","a known fault state (2s) if not enabled or CS.steer_state in [9, 25]","car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from","stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw]","steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: #","new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return new_actuators,","#print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows","MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,","[-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command,","import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer","1 # TODO: probably can delete this. CS.pcm_acc_status uses a different signal #","2)) # we can spam can to cancel the system even if we","a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif","there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert =","(fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif","apply_steer # Cut steering while we're in a known fault state (2s) if","[CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else:","pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low speed we","= 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can","enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # ***","0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS)","0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert,","0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can delete","PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd =","apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut","% 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame //","at 42Hz, with counter adding alternatively 1 and 2; # sending it at","can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to cancel","1 and 2; # sending it at 100Hz seem to allow a higher","create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS,","control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead =","the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame))","or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd:","True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line,","CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor will send","= True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd,","lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl:","CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill","[9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else:","class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False","self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active,","import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\","can trace shows this message at 42Hz, with counter adding alternatively 1 and","CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low","if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly","0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H,","in the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0,","interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep","= clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer","min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with","lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd:","to cancel the system even if we are using lat only control if","frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can","== 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if","CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different","entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not","right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 #","engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint","can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0","left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled:","CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in a","frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame","else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) #","MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque","= 0 else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status","can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step, vl)","0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5,","this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully","and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at","(frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart,","PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint","if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead","# This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas","or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low speed","or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req =","self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill or","(addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0","# - there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw","if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr,","self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack","12. # at low speed we always assume the lead is present so","# Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in","CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel =","100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for","good sound instead send_ui = True if (frame % 100 == 0 or","it at 100Hz seem to allow a higher rate limit, as the rate","CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer,","in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0","asap if: # - there is something to display # - there is","# gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4","0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) #","right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer,","prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd","different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled","uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if","% 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo","= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in","# on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and","import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg","False if ((fcw_alert or steer_alert) and not self.alert_active) or \\ (not (fcw_alert or","if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas","0, 0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and","from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController():","self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill or it's","a bad fault sound so play a good sound instead send_ui = True","((fcw_alert or steer_alert) and not self.alert_active) or \\ (not (fcw_alert or steer_alert) and","a good sound instead send_ui = True if (frame % 100 == 0","{2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at","cmd is zero. Interceptor will send the max between read value and gas","can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel =","hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not","system even if we are using lat only control if (frame % 3","not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill","common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM):","+ PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +","or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req","can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd","pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,","[CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15,","self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer","\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not","state (2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) >","we send asap if: # - there is something to display # -","MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED","if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED","not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill","# send exactly zero if gas cmd is zero. Interceptor will send the","\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert","CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0])","CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with counter adding","[0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4,","offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION],","CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in","PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo,","= pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame %","pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd,","LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda #","+ PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,","actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return","can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in","messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint","// 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in","value and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd,","are using lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl)","can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0,","else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses a","actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if","disengage causes a bad fault sound so play a good sound instead send_ui","from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan","[0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel","CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req = False","= True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to","left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert))","standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and","0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is","[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and","8: # pcm entered standstill or it's disabled self.standstill_req = False self.last_steer =","= car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active","0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False","we always assume the lead is present so ACC can be engaged #","# *** static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS:","% fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus))","a higher rate limit, as the rate limit seems imposed # on consecutive","(frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or","CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: #","common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import","CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer =","consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and","if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send","CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3,","the max between read value and gas cmd. # This prevents unexpected pedal","else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 ==","{1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message","is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert","mesg is at 100Hz but we send asap if: # - there is","< 12. # at low speed we always assume the lead is present","sound so play a good sound instead send_ui = True if (frame %","not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on","CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif","new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas =","and 2; # sending it at 100Hz seem to allow a higher rate","== 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req,","send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR","PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset","self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line,","CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class","in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0])","and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2])","can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're","actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to cancel the","apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses a different","active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas","alternatively 1 and 2; # sending it at 100Hz seem to allow a","and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead,","and whitelist 0x191 in the panda # if frame % 2 == 0:","CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +","rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is","= False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer =","standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill","= new_steer != apply_steer # Cut steering while we're in a known fault","0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer","apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\"steer {0}","in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active)","False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0","self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame","MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in","CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request if CS.out.standstill","[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE","= [] #*** control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim,","// 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we","== 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr,","pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor","CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status !=","msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can","0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame","exactly zero if gas cmd is zero. Interceptor will send the max between","= actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas","Cut steering while we're in a known fault state (2s) if not enabled","= 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer","0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we","- there is something to display # - there is something to stop","# if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame //","max between read value and gas cmd. # This prevents unexpected pedal range","rate limit, as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer,","[] #*** control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor)","sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo,","2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg,","0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. #","create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR,","Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS,","interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]:","cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer,","25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req","CS.out.standstill can_sends = [] #*** control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer,","and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status","Interceptor will send the max between read value and gas cmd. # This","selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command,","VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0","so play a good sound instead send_ui = True if (frame % 100","2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can","self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we send asap","= interceptor_gas_cmd # ui mesg is at 100Hz but we send asap if:","= int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer","False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name)","it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends =","# sending it at 100Hz seem to allow a higher rate limit, as","PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE","self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas","0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED","apply_steer_req, frame // 2)) # we can spam can to cancel the system","= False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control","interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz","PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,","steering while we're in a known fault state (2s) if not enabled or","left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5","VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or \\","[CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel","= False if ((fcw_alert or steer_alert) and not self.alert_active) or \\ (not (fcw_alert","CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and","pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel,","interceptor_gas_cmd # ui mesg is at 100Hz but we send asap if: #","[VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or","apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can delete this.","CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer,","== VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert","and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request if","with counter adding alternatively 1 and 2; # sending it at 100Hz seem","and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req =","\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION,","message at 42Hz, with counter adding alternatively 1 and 2; # sending it","uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer))","steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0","will send the max between read value and gas cmd. # This prevents","CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from","hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and","* CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer","# toyota can trace shows this message at 42Hz, with counter adding alternatively","new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited =","meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering","False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack')","cereal import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command,","seem to allow a higher rate limit, as the rate limit seems imposed","3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo <","send asap if: # - there is something to display # - there","!= 8: # pcm entered standstill or it's disabled self.standstill_req = False self.last_steer","pcm entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill","apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR:","CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req =","toyota can trace shows this message at 42Hz, with counter adding alternatively 1","send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm","max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with counter","and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if","int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer !=","2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel +","on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0","right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS","if gas cmd is zero. Interceptor will send the max between read value","pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX))","2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas","MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE =","selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import","probably can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm","and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer","request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not","= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for","can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle","adding alternatively 1 and 2; # sending it at 100Hz seem to allow","2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we send","0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars,","TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType =","rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if","higher rate limit, as the rate limit seems imposed # on consecutive messages","*** static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if","opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def","bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu","self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in a known","to allow a higher rate limit, as the rate limit seems imposed #","# ui mesg is at 100Hz but we send asap if: # -","PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],","0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer *","= PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd","frame // 2)) # we can spam can to cancel the system even","__init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False","- there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert","range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg","= apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return new_actuators, can_sends","= apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\"steer","dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req","vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel","only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead","be engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd and","and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing","using lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or","MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake","PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert","vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint","frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191","send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100","2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2))","panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame","while we're in a known fault state (2s) if not enabled or CS.steer_state","unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd #","send_ui = True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert,","frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero","fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui =","mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if","elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault sound","interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer =","on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint","can_sends = [] #*** control msgs *** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim,","is something to display # - there is something to stop displaying fcw_alert","= CS.out.standstill can_sends = [] #*** control msgs *** #print(\"steer {0} {1} {2}","can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 ==","# on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 ==","always assume the lead is present so ACC can be engaged # Lexus","fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators","100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO:","!= apply_steer # Cut steering while we're in a known fault state (2s)","clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer =","# than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status:","pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui","play a good sound instead send_ui = True if (frame % 100 ==","counter adding alternatively 1 and 2; # sending it at 100Hz seem to","if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer","and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer","creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0,","0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA","self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're","def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart,","self.gas = 0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators,","= car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame % 2","the lead is present so ACC can be engaged # Lexus IS uses","from cereal import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits,","RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]:","shows this message at 42Hz, with counter adding alternatively 1 and 2; #","in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],","MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo,","at 100Hz but we send asap if: # - there is something to","lead or CS.out.vEgo < 12. # at low speed we always assume the","= False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas =","not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer =","there is something to display # - there is something to stop displaying","= CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active, CS,","confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1","new_steer != apply_steer # Cut steering while we're in a known fault state","has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE","lead = lead or CS.out.vEgo < 12. # at low speed we always","self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes","CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer #","CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type,","{3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz,","if ((fcw_alert or steer_alert) and not self.alert_active) or \\ (not (fcw_alert or steer_alert)","pcm_cancel_cmd = 1 # on entering standstill, send standstill request if CS.out.standstill and","from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP,","standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in","for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4,","right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) #","clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) #","interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\","# at low speed we always assume the lead is present so ACC","disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = []","NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import","and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor will","known fault state (2s) if not enabled or CS.steer_state in [9, 25] or","or steer_alert) and not self.alert_active) or \\ (not (fcw_alert or steer_alert) and self.alert_active):","# steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps,","send the max between read value and gas cmd. # This prevents unexpected","windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command","torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited","left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu:","== 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd","cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel","zero if gas cmd is zero. Interceptor will send the max between read","can spam can to cancel the system even if we are using lat","# - there is something to display # - there is something to","not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad","# offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED +","read value and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer,","= True if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled","CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step,","This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas =","frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs","frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0,","self.alert_active) or \\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active","display # - there is something to stop displaying fcw_alert = hud_alert ==","= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE =","0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0])","self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs ***","self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #***","PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd =","cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and","in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in","or \\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active =","send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or \\ (not","and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very","enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint","STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params","frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) #","# pcm entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer","is zero. Interceptor will send the max between read value and gas cmd.","True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage","we are using lat only control if (frame % 3 == 0 and","= 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4,","[0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED","to display # - there is something to stop displaying fcw_alert = hud_alert","IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]:","but we send asap if: # - there is something to display #","from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\ create_accel_command,","pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False,","lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS =","spam can to cancel the system even if we are using lat only","or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame %","ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame %","import create_steer_command, create_ui_command, \\ create_accel_command, create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR,","False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0","# can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to","trace shows this message at 42Hz, with counter adding alternatively 1 and 2;","CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal","is present so ACC can be engaged # Lexus IS uses a different","2; # sending it at 100Hz seem to allow a higher rate limit,","something to display # - there is something to stop displaying fcw_alert =","so ACC can be engaged # Lexus IS uses a different cancellation message","we can spam can to cancel the system even if we are using","* (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0.","or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends","== 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators =","*** #print(\"steer {0} {1} {2} {3}\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace","not self.alert_active) or \\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True","gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has","can to cancel the system even if we are using lat only control","CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd =","0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and","0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],","pcm to disengage causes a bad fault sound so play a good sound","fault sound so play a good sound instead send_ui = True if (frame","cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2))","= interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE","CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send","0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3,","self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited","CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer /","% 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart,","def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill =","ui mesg is at 100Hz but we send asap if: # - there","(2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100:","create_acc_cancel_command, \\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\ MIN_ACC_SPEED,","allow a higher rate limit, as the rate limit seems imposed # on","we're in a known fault state (2s) if not enabled or CS.steer_state in","if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req =","= 0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited =","pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req,","= clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX)","in a known fault state (2s) if not enabled or CS.steer_state in [9,","= 1 # TODO: probably can delete this. CS.pcm_acc_status uses a different signal"],"string":"[\n \"// 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam\",\n \"== 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12.\",\n \"between read value and gas cmd. # This prevents unexpected pedal range rescaling\",\n \"elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else:\",\n \"if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack:\",\n \"True if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req\",\n \"cancel the system even if we are using lat only control if (frame\",\n \"CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name,\",\n \"CS.out.vEgo < 12. # at low speed we always assume the lead is\",\n \"at 100Hz seem to allow a higher rate limit, as the rate limit\",\n \"in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn))\",\n \"= hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False\",\n \"causes a bad fault sound so play a good sound instead send_ui =\",\n \"enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request\",\n \"MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert =\",\n \"brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive\",\n \"import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker\",\n \"TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams\",\n \"can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2))\",\n \"bad fault sound so play a good sound instead send_ui = True if\",\n \"entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill =\",\n \"NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm\",\n \"as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req,\",\n \"*** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step\",\n \"opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer =\",\n \"or CS.out.vEgo < 12. # at low speed we always assume the lead\",\n \"1 # on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill\",\n \"% 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if\",\n \"0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy()\",\n \"interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN,\",\n \"0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame\",\n \"= 1 # on entering standstill, send standstill request if CS.out.standstill and not\",\n \"from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer\",\n \"in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: #\",\n \"self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead,\",\n \"frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake\",\n \"for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step ==\",\n \"send exactly zero if gas cmd is zero. Interceptor will send the max\",\n \"zero. Interceptor will send the max between read value and gas cmd. #\",\n \"lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type,\",\n \"+ PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset =\",\n \"else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer\",\n \"import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command,\",\n \"not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8:\",\n \"make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from\",\n \"0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead,\",\n \"if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static\",\n \"abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1\",\n \"in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType\",\n \"False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and\",\n \"if we are using lat only control if (frame % 3 == 0\",\n \"frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl,\",\n \"CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req =\",\n \"CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active, CS, frame,\",\n \"= 0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd,\",\n \"delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not\",\n \"very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE =\",\n \"gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame //\",\n \"0 else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses\",\n \"= 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line,\",\n \"assume the lead is present so ACC can be engaged # Lexus IS\",\n \"VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or\",\n \"than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd\",\n \"> 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 #\",\n \"something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in\",\n \"import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self,\",\n \"message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd,\",\n \"and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode.\",\n \"lead is present so ACC can be engaged # Lexus IS uses a\",\n \"apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer,\",\n \"a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not\",\n \"can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at\",\n \"100Hz but we send asap if: # - there is something to display\",\n \"MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset\",\n \"\\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR,\",\n \"// 2)) # we can spam can to cancel the system even if\",\n \"present so ACC can be engaged # Lexus IS uses a different cancellation\",\n \"# RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER,\",\n \"interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo,\",\n \"and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus,\",\n \"the pcm to disengage causes a bad fault sound so play a good\",\n \"steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams)\",\n \"to disengage causes a bad fault sound so play a good sound instead\",\n \"enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): #\",\n \"speed we always assume the lead is present so ACC can be engaged\",\n \"pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault sound so\",\n \"car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame % 2 ==\",\n \"from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values\",\n \"{0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this\",\n \"pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2\",\n \"steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert)\",\n \"sending it at 100Hz seem to allow a higher rate limit, as the\",\n \"fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step, vl) in\",\n \"0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist\",\n \"self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault\",\n \"CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4,\",\n \"pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else:\",\n \"whitelist 0x191 in the panda # if frame % 2 == 0: #\",\n \"is at 100Hz but we send asap if: # - there is something\",\n \"forcing the pcm to disengage causes a bad fault sound so play a\",\n \"update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart):\",\n \"gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,\",\n \"low speed we always assume the lead is present so ACC can be\",\n \"pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor\",\n \"steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui\",\n \"100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled))\",\n \"== 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) #\",\n \"# can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame //\",\n \"42Hz, with counter adding alternatively 1 and 2; # sending it at 100Hz\",\n \"self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel\",\n \"limit, as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer,\",\n \"Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame\",\n \"# we can spam can to cancel the system even if we are\",\n \"CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor\",\n \"CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req\",\n \"in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX\",\n \"selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import\",\n \"TODO: probably can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled.\",\n \"% 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer,\",\n \"and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame\",\n \"elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3,\",\n \"pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command =\",\n \"if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd,\",\n \"different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl:\",\n \"(actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd\",\n \"not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True\",\n \"(not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active\",\n \"create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command\",\n \"frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but\",\n \"msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame %\",\n \"# forcing the pcm to disengage causes a bad fault sound so play\",\n \"the system even if we are using lat only control if (frame %\",\n \"to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired,\",\n \"create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams\",\n \"MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset)\",\n \"at low speed we always assume the lead is present so ACC can\",\n \"and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero.\",\n \"+ PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd\",\n \"+ pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd =\",\n \"if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line,\",\n \"[0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0,\",\n \"# Cut steering while we're in a known fault state (2s) if not\",\n \"can be engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd\",\n \"hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if\",\n \"0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0.,\",\n \"apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably\",\n \"the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0,\",\n \"and not self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui =\",\n \"False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs\",\n \"2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the\",\n \"fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and\",\n \"create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from\",\n \"fault state (2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg)\",\n \"CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer,\",\n \"self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\\\"steer {0} {1}\",\n \"% 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs ***\",\n \"# TODO: probably can delete this. CS.pcm_acc_status uses a different signal # than\",\n \"enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0\",\n \"self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if\",\n \"100Hz seem to allow a higher rate limit, as the rate limit seems\",\n \"instead send_ui = True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer,\",\n \"CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if\",\n \"ACC can be engaged # Lexus IS uses a different cancellation message if\",\n \"self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def\",\n \"STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars:\",\n \"static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame\",\n \"= apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering\",\n \"gas cmd is zero. Interceptor will send the max between read value and\",\n \"= not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a\",\n \"# LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda\",\n \"if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0,\",\n \"= hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and\",\n \"control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota\",\n \"apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command,\",\n \"self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the\",\n \"sound instead send_ui = True if (frame % 100 == 0 or send_ui):\",\n \"pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and\",\n \"and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered\",\n \"if: # - there is something to display # - there is something\",\n \"= False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel =\",\n \"= opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self,\",\n \"signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and\",\n \"they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 #\",\n \"if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2))\",\n \"clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command,\",\n \"limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame\",\n \"displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui\",\n \"CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set\",\n \"[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,\",\n \"<filename>selfdrive/car/toyota/carcontroller.py from cereal import car from common.numpy_fast import clip, interp from selfdrive.car import\",\n \"interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE *\",\n \"= False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack =\",\n \"apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while\",\n \"= lead or CS.out.vEgo < 12. # at low speed we always assume\",\n \"bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas\",\n \"even if we are using lat only control if (frame % 3 ==\",\n \"different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill,\",\n \"#*** control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) #\",\n \"0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +\",\n \"VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req = False\",\n \"seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame %\",\n \"car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active =\",\n \"this message at 42Hz, with counter adding alternatively 1 and 2; # sending\",\n \"opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled,\",\n \"imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2\",\n \"0x191 in the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer,\",\n \"CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn))\",\n \"create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\\",\n \"a known fault state (2s) if not enabled or CS.steer_state in [9, 25]\",\n \"car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from\",\n \"stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw]\",\n \"steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: #\",\n \"new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return new_actuators,\",\n \"#print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows\",\n \"MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,\",\n \"[-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command,\",\n \"import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer\",\n \"1 # TODO: probably can delete this. CS.pcm_acc_status uses a different signal #\",\n \"2)) # we can spam can to cancel the system even if we\",\n \"a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif\",\n \"there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert =\",\n \"(fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif\",\n \"apply_steer # Cut steering while we're in a known fault state (2s) if\",\n \"[CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else:\",\n \"pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low speed we\",\n \"= 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can\",\n \"enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # ***\",\n \"0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS)\",\n \"0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert,\",\n \"0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can delete\",\n \"PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd =\",\n \"apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut\",\n \"% 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame //\",\n \"at 42Hz, with counter adding alternatively 1 and 2; # sending it at\",\n \"can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to cancel\",\n \"1 and 2; # sending it at 100Hz seem to allow a higher\",\n \"create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS,\",\n \"control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead =\",\n \"the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame))\",\n \"or steer_alert) and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd:\",\n \"True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line,\",\n \"CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor will send\",\n \"= True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd,\",\n \"lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl:\",\n \"CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill\",\n \"[9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else:\",\n \"class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False\",\n \"self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active,\",\n \"import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\\",\n \"can trace shows this message at 42Hz, with counter adding alternatively 1 and\",\n \"CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low\",\n \"if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly\",\n \"0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H,\",\n \"in the panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0,\",\n \"interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep\",\n \"= clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer\",\n \"min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with\",\n \"lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd:\",\n \"to cancel the system even if we are using lat only control if\",\n \"frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can\",\n \"== 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if\",\n \"CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different\",\n \"entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not\",\n \"right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 #\",\n \"engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint\",\n \"can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0\",\n \"left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled:\",\n \"CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in a\",\n \"frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame\",\n \"else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) #\",\n \"MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque\",\n \"= 0 else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status\",\n \"can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step, vl)\",\n \"0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5,\",\n \"this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully\",\n \"and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at\",\n \"(frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart,\",\n \"PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint\",\n \"if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead\",\n \"# This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas\",\n \"or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. # at low speed\",\n \"or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req =\",\n \"self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill or\",\n \"(addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0\",\n \"# - there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw\",\n \"if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr,\",\n \"self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack\",\n \"12. # at low speed we always assume the lead is present so\",\n \"# Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in\",\n \"CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel =\",\n \"100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for\",\n \"good sound instead send_ui = True if (frame % 100 == 0 or\",\n \"it at 100Hz seem to allow a higher rate limit, as the rate\",\n \"CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer,\",\n \"in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0\",\n \"asap if: # - there is something to display # - there is\",\n \"# gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4\",\n \"0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) #\",\n \"right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer,\",\n \"prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd\",\n \"different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled\",\n \"uses a different signal # than CS.cruiseState.enabled. confirm they're not meaningfully different if\",\n \"% 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo\",\n \"= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in\",\n \"# on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and\",\n \"import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg\",\n \"False if ((fcw_alert or steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert or\",\n \"if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas\",\n \"0, 0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and\",\n \"from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController():\",\n \"self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill or it's\",\n \"a bad fault sound so play a good sound instead send_ui = True\",\n \"((fcw_alert or steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and\",\n \"a good sound instead send_ui = True if (frame % 100 == 0\",\n \"{2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at\",\n \"cmd is zero. Interceptor will send the max between read value and gas\",\n \"can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel =\",\n \"hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not\",\n \"system even if we are using lat only control if (frame % 3\",\n \"not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill\",\n \"common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM):\",\n \"+ PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +\",\n \"or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req\",\n \"can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd\",\n \"pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,\",\n \"[CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15,\",\n \"self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer\",\n \"\\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active = not\",\n \"state (2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) >\",\n \"we send asap if: # - there is something to display # -\",\n \"MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED\",\n \"if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED\",\n \"not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status != 8: # pcm entered standstill\",\n \"# send exactly zero if gas cmd is zero. Interceptor will send the\",\n \"\\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert\",\n \"CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0])\",\n \"CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with counter adding\",\n \"[0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4,\",\n \"offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION],\",\n \"CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in\",\n \"PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo,\",\n \"= pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame %\",\n \"pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd,\",\n \"LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda #\",\n \"+ PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0,\",\n \"actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return\",\n \"can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in\",\n \"messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint\",\n \"// 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in\",\n \"value and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd,\",\n \"are using lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl)\",\n \"can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0,\",\n \"else: apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses a\",\n \"actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if\",\n \"disengage causes a bad fault sound so play a good sound instead send_ui\",\n \"from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan\",\n \"[0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel\",\n \"CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req = False\",\n \"= True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to\",\n \"left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert))\",\n \"standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and\",\n \"0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is\",\n \"[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and\",\n \"8: # pcm entered standstill or it's disabled self.standstill_req = False self.last_steer =\",\n \"= car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active\",\n \"0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False\",\n \"we always assume the lead is present so ACC can be engaged #\",\n \"# *** static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS:\",\n \"% fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus))\",\n \"a higher rate limit, as the rate limit seems imposed # on consecutive\",\n \"(frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or\",\n \"CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: #\",\n \"common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import\",\n \"CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer =\",\n \"consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and\",\n \"if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send\",\n \"CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3,\",\n \"the max between read value and gas cmd. # This prevents unexpected pedal\",\n \"else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 ==\",\n \"{1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message\",\n \"is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert\",\n \"mesg is at 100Hz but we send asap if: # - there is\",\n \"< 12. # at low speed we always assume the lead is present\",\n \"sound so play a good sound instead send_ui = True if (frame %\",\n \"not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on\",\n \"CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif\",\n \"new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas =\",\n \"and 2; # sending it at 100Hz seem to allow a higher rate\",\n \"== 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req,\",\n \"send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR\",\n \"PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset\",\n \"self.accel = 0 def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line,\",\n \"CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class\",\n \"in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0])\",\n \"and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2])\",\n \"can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm they're\",\n \"actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to cancel the\",\n \"apply_steer_req = 1 # TODO: probably can delete this. CS.pcm_acc_status uses a different\",\n \"active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas\",\n \"alternatively 1 and 2; # sending it at 100Hz seem to allow a\",\n \"and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead,\",\n \"and whitelist 0x191 in the panda # if frame % 2 == 0:\",\n \"CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED +\",\n \"rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is\",\n \"= False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer =\",\n \"standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill\",\n \"= new_steer != apply_steer # Cut steering while we're in a known fault\",\n \"0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer\",\n \"apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\\\"steer {0}\",\n \"in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active)\",\n \"False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0\",\n \"self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame\",\n \"MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in\",\n \"CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request if CS.out.standstill\",\n \"[0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE\",\n \"= [] #*** control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim,\",\n \"// 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we\",\n \"== 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr,\",\n \"pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor\",\n \"CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status !=\",\n \"msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can\",\n \"0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame\",\n \"exactly zero if gas cmd is zero. Interceptor will send the max between\",\n \"= actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas\",\n \"Cut steering while we're in a known fault state (2s) if not enabled\",\n \"= 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer\",\n \"0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we\",\n \"- there is something to display # - there is something to stop\",\n \"# if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame //\",\n \"max between read value and gas cmd. # This prevents unexpected pedal range\",\n \"rate limit, as the rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer,\",\n \"[] #*** control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor)\",\n \"sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo,\",\n \"2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg,\",\n \"0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12. #\",\n \"create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR,\",\n \"Lexus IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS,\",\n \"interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]:\",\n \"cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer,\",\n \"25] or abs(CS.out.steeringRateDeg) > 100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req\",\n \"CS.out.standstill can_sends = [] #*** control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer,\",\n \"and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req = True if CS.pcm_acc_status\",\n \"Interceptor will send the max between read value and gas cmd. # This\",\n \"selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command,\",\n \"VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP, VM): self.last_steer = 0\",\n \"so play a good sound instead send_ui = True if (frame % 100\",\n \"2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can\",\n \"self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we send asap\",\n \"= interceptor_gas_cmd # ui mesg is at 100Hz but we send asap if:\",\n \"= int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer\",\n \"False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name)\",\n \"it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends =\",\n \"# sending it at 100Hz seem to allow a higher rate limit, as\",\n \"PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE\",\n \"self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas\",\n \"0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED\",\n \"apply_steer_req, frame // 2)) # we can spam can to cancel the system\",\n \"= False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control\",\n \"interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz\",\n \"PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED,\",\n \"steering while we're in a known fault state (2s) if not enabled or\",\n \"left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5\",\n \"VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or \\\\\",\n \"[CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel\",\n \"= False if ((fcw_alert or steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert\",\n \"CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and\",\n \"pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel,\",\n \"interceptor_gas_cmd # ui mesg is at 100Hz but we send asap if: #\",\n \"[VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or\",\n \"apply_steer_req = 0 else: apply_steer_req = 1 # TODO: probably can delete this.\",\n \"CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer,\",\n \"== VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui = False if ((fcw_alert\",\n \"and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering standstill, send standstill request if\",\n \"with counter adding alternatively 1 and 2; # sending it at 100Hz seem\",\n \"and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not self.standstill_hack: self.standstill_req =\",\n \"\\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION,\",\n \"message at 42Hz, with counter adding alternatively 1 and 2; # sending it\",\n \"uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer))\",\n \"steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0\",\n \"will send the max between read value and gas cmd. # This prevents\",\n \"CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from\",\n \"hud_alert, left_line, right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and\",\n \"* CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer\",\n \"# toyota can trace shows this message at 42Hz, with counter adding alternatively\",\n \"new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited =\",\n \"meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1 # on entering\",\n \"False self.last_standstill = False self.standstill_req = False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack')\",\n \"cereal import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command,\",\n \"seem to allow a higher rate limit, as the rate limit seems imposed\",\n \"3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo <\",\n \"send asap if: # - there is something to display # - there\",\n \"!= 8: # pcm entered standstill or it's disabled self.standstill_req = False self.last_steer\",\n \"pcm entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill\",\n \"apply_steer, apply_steer_req, frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR:\",\n \"CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req =\",\n \"toyota can trace shows this message at 42Hz, with counter adding alternatively 1\",\n \"send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm\",\n \"max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz, with counter\",\n \"and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if\",\n \"int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer !=\",\n \"2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel +\",\n \"on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 == 0\",\n \"right_line, lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS\",\n \"if gas cmd is zero. Interceptor will send the max between read value\",\n \"pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX))\",\n \"2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas\",\n \"MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE =\",\n \"selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import\",\n \"probably can delete this. CS.pcm_acc_status uses a different signal # than CS.cruiseState.enabled. confirm\",\n \"and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer\",\n \"request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in NO_STOP_TIMER_CAR and not\",\n \"= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for\",\n \"can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle\",\n \"adding alternatively 1 and 2; # sending it at 100Hz seem to allow\",\n \"2)) self.gas = interceptor_gas_cmd # ui mesg is at 100Hz but we send\",\n \"0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars,\",\n \"TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA mode. Set ret.steerControlType =\",\n \"rate limit seems imposed # on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if\",\n \"higher rate limit, as the rate limit seems imposed # on consecutive messages\",\n \"*** static msgs *** for (addr, cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if\",\n \"opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def\",\n \"bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu\",\n \"self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're in a known\",\n \"to allow a higher rate limit, as the rate limit seems imposed #\",\n \"# ui mesg is at 100Hz but we send asap if: # -\",\n \"PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],\",\n \"0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer *\",\n \"= PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd\",\n \"frame // 2)) # we can spam can to cancel the system even\",\n \"__init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False\",\n \"- there is something to stop displaying fcw_alert = hud_alert == VisualAlert.fcw steer_alert\",\n \"range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui mesg\",\n \"= apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel new_actuators.gas = self.gas return new_actuators, can_sends\",\n \"= apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs *** #print(\\\"steer\",\n \"dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req\",\n \"vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel = self.accel\",\n \"only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead\",\n \"be engaged # Lexus IS uses a different cancellation message if pcm_cancel_cmd and\",\n \"and self.alert_active): send_ui = True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing\",\n \"using lat only control if (frame % 3 == 0 and CS.CP.openpilotLongitudinalControl) or\",\n \"MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0]) # offset for creep and windbrake\",\n \"PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert\",\n \"vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint\",\n \"frame // 2)) # LTA mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191\",\n \"send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100\",\n \"2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2))\",\n \"panda # if frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame\",\n \"while we're in a known fault state (2s) if not enabled or CS.steer_state\",\n \"unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd #\",\n \"send_ui = True if (frame % 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert,\",\n \"frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero\",\n \"fcw_alert = hud_alert == VisualAlert.fcw steer_alert = hud_alert in [VisualAlert.steerRequired, VisualAlert.ldw] send_ui =\",\n \"mode. Set ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if\",\n \"elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad fault sound\",\n \"interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer =\",\n \"on entering standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint\",\n \"can_sends = [] #*** control msgs *** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim,\",\n \"is something to display # - there is something to stop displaying fcw_alert\",\n \"= CS.out.standstill can_sends = [] #*** control msgs *** #print(\\\"steer {0} {1} {2}\",\n \"can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 ==\",\n \"# on consecutive messages can_sends.append(create_steer_command(self.packer, apply_steer, apply_steer_req, frame)) if frame % 2 ==\",\n \"always assume the lead is present so ACC can be engaged # Lexus\",\n \"fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators\",\n \"100: apply_steer = 0 apply_steer_req = 0 else: apply_steer_req = 1 # TODO:\",\n \"!= apply_steer # Cut steering while we're in a known fault state (2s)\",\n \"clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) # steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer =\",\n \"# than CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status:\",\n \"pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas = interceptor_gas_cmd # ui\",\n \"play a good sound instead send_ui = True if (frame % 100 ==\",\n \"counter adding alternatively 1 and 2; # sending it at 100Hz seem to\",\n \"if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer\",\n \"and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer\",\n \"creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0,\",\n \"0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0, 0, frame // 2)) # LTA\",\n \"self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer # Cut steering while we're\",\n \"def update(self, enabled, active, CS, frame, actuators, pcm_cancel_cmd, hud_alert, left_line, right_line, lead, left_lane_depart,\",\n \"self.gas = 0 self.accel = 0 def update(self, enabled, active, CS, frame, actuators,\",\n \"= car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame % 2\",\n \"the lead is present so ACC can be engaged # Lexus IS uses\",\n \"from cereal import car from common.numpy_fast import clip, interp from selfdrive.car import apply_toyota_steer_torque_limits,\",\n \"RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]:\",\n \"shows this message at 42Hz, with counter adding alternatively 1 and 2; #\",\n \"in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],\",\n \"MIN_ACC_SPEED + PEDAL_TRANSITION], [0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo,\",\n \"at 100Hz but we send asap if: # - there is something to\",\n \"lead or CS.out.vEgo < 12. # at low speed we always assume the\",\n \"= False self.steer_rate_limited = False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas =\",\n \"not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100: apply_steer =\",\n \"there is something to display # - there is something to stop displaying\",\n \"= CANPacker(dbc_name) self.gas = 0 self.accel = 0 def update(self, enabled, active, CS,\",\n \"confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd = 1\",\n \"new_steer != apply_steer # Cut steering while we're in a known fault state\",\n \"has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4, CAR.RAV4H, CAR.HIGHLANDER, CAR.HIGHLANDERH]: PEDAL_SCALE\",\n \"lead = lead or CS.out.vEgo < 12. # at low speed we always\",\n \"self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes\",\n \"CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited = new_steer != apply_steer #\",\n \"CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type,\",\n \"{3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace shows this message at 42Hz,\",\n \"if ((fcw_alert or steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert or steer_alert)\",\n \"pcm_cancel_cmd = 1 # on entering standstill, send standstill request if CS.out.standstill and\",\n \"from common.op_params import opParams VisualAlert = car.CarControl.HUDControl.VisualAlert class CarController(): def __init__(self, dbc_name, CP,\",\n \"standstill, send standstill request if CS.out.standstill and not self.last_standstill and CS.CP.carFingerprint not in\",\n \"for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4,\",\n \"right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) #\",\n \"clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX) #\",\n \"interp from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\\",\n \"# at low speed we always assume the lead is present so ACC\",\n \"disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = []\",\n \"NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params import\",\n \"and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd is zero. Interceptor will\",\n \"known fault state (2s) if not enabled or CS.steer_state in [9, 25] or\",\n \"or steer_alert) and not self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and self.alert_active):\",\n \"# steer torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps,\",\n \"send the max between read value and gas cmd. # This prevents unexpected\",\n \"windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command\",\n \"torque new_steer = int(round(actuators.steer * CarControllerParams.STEER_MAX)) apply_steer = apply_toyota_steer_torque_limits(new_steer, self.last_steer, CS.out.steeringTorqueEps, CarControllerParams) self.steer_rate_limited\",\n \"left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame % 100 == 0 and CS.CP.enableDsu:\",\n \"== 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send exactly zero if gas cmd\",\n \"cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer / CarControllerParams.STEER_MAX new_actuators.accel\",\n \"zero if gas cmd is zero. Interceptor will send the max between read\",\n \"can spam can to cancel the system even if we are using lat\",\n \"# - there is something to display # - there is something to\",\n \"not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage causes a bad\",\n \"# offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED +\",\n \"read value and gas cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer,\",\n \"= True if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled\",\n \"CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs *** for (addr, cars, bus, fr_step,\",\n \"This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2)) self.gas =\",\n \"frame % 100 == 0 and CS.CP.enableDsu: can_sends.append(create_fcw_command(self.packer, fcw_alert)) # *** static msgs\",\n \"frame)) if frame % 2 == 0 and CS.CP.carFingerprint in TSS2_CAR: can_sends.append(create_lta_steer_command(self.packer, 0,\",\n \"self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active\",\n \"display # - there is something to stop displaying fcw_alert = hud_alert ==\",\n \"= interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.3, 0.4, 0.0]) else: PEDAL_SCALE =\",\n \"0.0]) else: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION], [0.4, 0.5, 0.0])\",\n \"self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #*** control msgs ***\",\n \"self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends = [] #***\",\n \"PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE * (actuators.accel + pedal_offset) interceptor_gas_cmd =\",\n \"cars, bus, fr_step, vl) in STATIC_DSU_MSGS: if frame % fr_step == 0 and\",\n \"in STATIC_DSU_MSGS: if frame % fr_step == 0 and CS.CP.enableDsu and CS.CP.carFingerprint in\",\n \"or \\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True self.alert_active =\",\n \"send_ui = False if ((fcw_alert or steer_alert) and not self.alert_active) or \\\\ (not\",\n \"and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very\",\n \"enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint\",\n \"STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED, PEDAL_TRANSITION, CarControllerParams from opendbc.can.packer import CANPacker from common.op_params\",\n \"frame % 2 == 0: # can_sends.append(create_steer_command(self.packer, 0, 0, frame // 2)) #\",\n \"# pcm entered standstill or it's disabled self.standstill_req = False self.last_steer = apply_steer\",\n \"is zero. Interceptor will send the max between read value and gas cmd.\",\n \"True self.alert_active = not self.alert_active elif pcm_cancel_cmd: # forcing the pcm to disengage\",\n \"we are using lat only control if (frame % 3 == 0 and\",\n \"= 0.5 # RAV4 has very sensitive gas pedal if CS.CP.carFingerprint in [CAR.RAV4,\",\n \"[0.15, 0.3, 0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED\",\n \"to display # - there is something to stop displaying fcw_alert = hud_alert\",\n \"IS uses a different cancellation message if pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]:\",\n \"but we send asap if: # - there is something to display #\",\n \"from selfdrive.car import apply_toyota_steer_torque_limits, create_gas_interceptor_command, make_can_msg from selfdrive.car.toyota.toyotacan import create_steer_command, create_ui_command, \\\\ create_accel_command,\",\n \"pcm_cancel_cmd, self.standstill_req, lead, CS.acc_type, CS.distance_btn)) self.accel = pcm_accel_cmd else: can_sends.append(create_accel_command(self.packer, 0, pcm_cancel_cmd, False,\",\n \"lead, left_lane_depart, right_lane_depart): # gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS =\",\n \"spam can to cancel the system even if we are using lat only\",\n \"or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart, enabled)) if frame %\",\n \"ret.steerControlType = car.CarParams.SteerControlType.angle and whitelist 0x191 in the panda # if frame %\",\n \"import create_steer_command, create_ui_command, \\\\ create_accel_command, create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR,\",\n \"False self.standstill_hack = opParams().get('standstill_hack') self.packer = CANPacker(dbc_name) self.gas = 0 self.accel = 0\",\n \"# can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam can to\",\n \"trace shows this message at 42Hz, with counter adding alternatively 1 and 2;\",\n \"CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has very sensitive gas pedal\",\n \"is present so ACC can be engaged # Lexus IS uses a different\",\n \"2; # sending it at 100Hz seem to allow a higher rate limit,\",\n \"something to display # - there is something to stop displaying fcw_alert =\",\n \"so ACC can be engaged # Lexus IS uses a different cancellation message\",\n \"we can spam can to cancel the system even if we are using\",\n \"* (actuators.accel + pedal_offset) interceptor_gas_cmd = clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0.\",\n \"or it's disabled self.standstill_req = False self.last_steer = apply_steer self.last_standstill = CS.out.standstill can_sends\",\n \"== 0 and CS.CP.enableDsu and CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators =\",\n \"*** #print(\\\"steer {0} {1} {2} {3}\\\".format(apply_steer, min_lim, max_lim, CS.steer_torque_motor) # toyota can trace\",\n \"not self.alert_active) or \\\\ (not (fcw_alert or steer_alert) and self.alert_active): send_ui = True\",\n \"gas and brake if CS.CP.enableGasInterceptor and enabled: MAX_INTERCEPTOR_GAS = 0.5 # RAV4 has\",\n \"can to cancel the system even if we are using lat only control\",\n \"CS.cruiseState.enabled. confirm they're not meaningfully different if not enabled and CS.pcm_acc_status: pcm_cancel_cmd =\",\n \"0, pcm_cancel_cmd, False, lead, CS.acc_type, CS.distance_btn)) if frame % 2 == 0 and\",\n \"0.0]) elif CS.CP.carFingerprint in [CAR.COROLLA]: PEDAL_SCALE = interp(CS.out.vEgo, [0.0, MIN_ACC_SPEED, MIN_ACC_SPEED + PEDAL_TRANSITION],\",\n \"pcm to disengage causes a bad fault sound so play a good sound\",\n \"fault sound so play a good sound instead send_ui = True if (frame\",\n \"cmd. # This prevents unexpected pedal range rescaling can_sends.append(create_gas_interceptor_command(self.packer, interceptor_gas_cmd, frame // 2))\",\n \"= interp(CS.out.vEgo, [0.0, 2.3, MIN_ACC_SPEED + PEDAL_TRANSITION], [-.4, 0.0, 0.2]) pedal_command = PEDAL_SCALE\",\n \"CS.distance_btn)) if frame % 2 == 0 and CS.CP.enableGasInterceptor and CS.CP.openpilotLongitudinalControl: # send\",\n \"0.5, 0.0]) # offset for creep and windbrake pedal_offset = interp(CS.out.vEgo, [0.0, 2.3,\",\n \"self.last_steer = 0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited\",\n \"CS.CP.carFingerprint in cars: can_sends.append(make_can_msg(addr, vl, bus)) new_actuators = actuators.copy() new_actuators.steer = apply_steer /\",\n \"% 100 == 0 or send_ui): can_sends.append(create_ui_command(self.packer, steer_alert, pcm_cancel_cmd, left_line, right_line, left_lane_depart, right_lane_depart,\",\n \"def __init__(self, dbc_name, CP, VM): self.last_steer = 0 self.alert_active = False self.last_standstill =\",\n \"ui mesg is at 100Hz but we send asap if: # - there\",\n \"(2s) if not enabled or CS.steer_state in [9, 25] or abs(CS.out.steeringRateDeg) > 100:\",\n \"create_acc_cancel_command, \\\\ create_fcw_command, create_lta_steer_command from selfdrive.car.toyota.values import CAR, STATIC_DSU_MSGS, NO_STOP_TIMER_CAR, TSS2_CAR, \\\\ MIN_ACC_SPEED,\",\n \"allow a higher rate limit, as the rate limit seems imposed # on\",\n \"we're in a known fault state (2s) if not enabled or CS.steer_state in\",\n \"if CS.pcm_acc_status != 8: # pcm entered standstill or it's disabled self.standstill_req =\",\n \"= 0 self.alert_active = False self.last_standstill = False self.standstill_req = False self.steer_rate_limited =\",\n \"pcm_cancel_cmd and CS.CP.carFingerprint in [CAR.LEXUS_IS, CAR.LEXUS_RC]: can_sends.append(create_acc_cancel_command(self.packer)) elif CS.CP.openpilotLongitudinalControl: can_sends.append(create_accel_command(self.packer, pcm_accel_cmd, pcm_cancel_cmd, self.standstill_req,\",\n \"= clip(pedal_command, 0., MAX_INTERCEPTOR_GAS) else: interceptor_gas_cmd = 0. pcm_accel_cmd = clip(actuators.accel, CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX)\",\n \"in a known fault state (2s) if not enabled or CS.steer_state in [9,\",\n \"= 1 # TODO: probably can delete this. CS.pcm_acc_status uses a different signal\"\n]","isConversational":false}}},{"rowIdx":2576,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":2577,"cells":{"ngram":{"kind":"list like","value":["code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'),","'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction',","import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class","SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction =","Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL')","= StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15',","('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"),","= SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25',","'15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\",","StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'),","choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30',","'25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime',","QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10',","\"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate QR","choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image =","'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')])","(\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\",","<gh_stars>1-10 from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators","StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()])","'5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color =","from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content","code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'),","wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content =","choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error","class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'),","SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")])","'10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white',","code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")])","(\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL') generate_code","Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image","= SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\",","(\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"),","('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"),","FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm):","= SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction","\"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL') generate_code =","\"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\",","('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"),","SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size =","code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"),","(\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate","('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"),","(\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate QR Code')","flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired","from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import","DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5',","from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size',","('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color","wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15',","'20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow',","('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"),","('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'),","\"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\",","\"Error Correction\"), (\"H\", \"H\"), (\"L\", \"L\"), (\"M\", \"M\"), (\"Q\", \"Q\")]) code_image = StringField('Image","import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'),","\"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error Correction\"), (\"H\", \"H\"), (\"L\",","SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size","SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'),","'30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\",","validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20',","import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content',","('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour',","\"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction', choices=[(\"H\", \"Error"],"string":"[\n \"code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'),\",\n \"'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction',\",\n \"import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class\",\n \"SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction =\",\n \"Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL')\",\n \"= StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15',\",\n \"('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"),\",\n \"= SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25',\",\n \"'15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\",\",\n \"StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'),\",\n \"choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30',\",\n \"'25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime',\",\n \"QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10',\",\n \"\\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate QR\",\n \"choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image =\",\n \"'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')])\",\n \"(\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\",\",\n \"<gh_stars>1-10 from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators\",\n \"StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()])\",\n \"'5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color =\",\n \"from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content\",\n \"code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'),\",\n \"wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content =\",\n \"choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error\",\n \"class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'),\",\n \"SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")])\",\n \"'10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white',\",\n \"code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")])\",\n \"(\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL') generate_code\",\n \"Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image\",\n \"= SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\",\",\n \"(\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"),\",\n \"('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"),\",\n \"FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm):\",\n \"= SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction\",\n \"\\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL') generate_code =\",\n \"\\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\",\",\n \"('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"),\",\n \"SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size =\",\n \"code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"),\",\n \"(\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate\",\n \"('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"),\",\n \"(\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image URL') generate_code = SubmitField('Generate QR Code')\",\n \"flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import DataRequired\",\n \"from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, SelectField from wtforms.validators import\",\n \"DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5',\",\n \"from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size',\",\n \"('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color\",\n \"wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15',\",\n \"'20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow',\",\n \"('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"),\",\n \"('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'),\",\n \"\\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\",\",\n \"\\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\", \\\"L\\\"), (\\\"M\\\", \\\"M\\\"), (\\\"Q\\\", \\\"Q\\\")]) code_image = StringField('Image\",\n \"import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'),\",\n \"\\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error Correction\\\"), (\\\"H\\\", \\\"H\\\"), (\\\"L\\\",\",\n \"SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content', validators=[DataRequired()]) code_size\",\n \"SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'),\",\n \"'30')]) code_color = SelectField('Colour', choices=[('white', 'Colour'), (\\\"white\\\", \\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\",\",\n \"validators=[DataRequired()]) code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20',\",\n \"import StringField, SubmitField, SelectField from wtforms.validators import DataRequired class QRGenerator(FlaskForm): code_content = StringField('Content',\",\n \"('10', '10'), ('15', '15'), ('20', '20'), ('25', '25'), ('30', '30')]) code_color = SelectField('Colour',\",\n \"\\\"White\\\"), ('yellow', \\\"Yellow\\\"), ('lime', \\\"Green\\\"), (\\\"#ffa500\\\", \\\"Orange\\\")]) code_correction = SelectField('Error Correction', choices=[(\\\"H\\\", \\\"Error\"\n]","isConversational":false}}},{"rowIdx":2578,"cells":{"ngram":{"kind":"list like","value":["a stock price, and return the most recent (last) volatility estimate. Parameters ----------","pandas.Series A series of adjusted closing prices for a stock. l : float","the exponential moving average model. Making this value smaller will cause the model","exponential moving average model of the volatility of a stock price, and return","recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ == '__main__': test_run()","prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent","A series of adjusted closing prices for a stock. l : float The","value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from a",": float The 'lambda' parameter of the exponential moving average model. Making this","'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if","series. \"\"\" # TODO: Implement the exponential moving average volatility model and return","return the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A","value smaller will cause the model to weight older terms less relative to","series of adjusted closing prices for a stock. l : float The 'lambda'","average model of the volatility of a stock price, and return the most","price, and return the most recent (last) volatility estimate. Parameters ---------- prices :","TODO: Implement the exponential moving average volatility model and return the last value.","import numpy as np def estimate_volatility(prices, l): \"\"\"Create an exponential moving average model","print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ == '__main__':","smaller will cause the model to weight older terms less relative to more","the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series","this value smaller will cause the model to weight older terms less relative","exponential moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def","run get_most_volatile() with stock prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'],","of the exponential moving average model. Making this value smaller will cause the","'lambda' parameter of the exponential moving average model. Making this value smaller will","prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7)))","last element of your exponential moving averge volatility model series. \"\"\" # TODO:","terms less relative to more recent terms. Returns ------- last_vol : float The","weight older terms less relative to more recent terms. Returns ------- last_vol :","volatility model series. \"\"\" # TODO: Implement the exponential moving average volatility model","Returns ------- last_vol : float The last element of your exponential moving averge","volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run","estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing prices for","moving average model of the volatility of a stock price, and return the","of your exponential moving averge volatility model series. \"\"\" # TODO: Implement the","with stock prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True)","more recent terms. Returns ------- last_vol : float The last element of your","less relative to more recent terms. Returns ------- last_vol : float The last","get_most_volatile() with stock prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date',","volatility of a stock price, and return the most recent (last) volatility estimate.","average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test","l : float The 'lambda' parameter of the exponential moving average model. Making","and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with","float The 'lambda' parameter of the exponential moving average model. Making this value","\"\"\" # TODO: Implement the exponential moving average volatility model and return the","return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock","parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7))","exponential moving average model. Making this value smaller will cause the model to","average model. Making this value smaller will cause the model to weight older","Parameters ---------- prices : pandas.Series A series of adjusted closing prices for a","relative to more recent terms. Returns ------- last_vol : float The last element","as pd import numpy as np def estimate_volatility(prices, l): \"\"\"Create an exponential moving","recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted","pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices,","squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ ==","pandas as pd import numpy as np def estimate_volatility(prices, l): \"\"\"Create an exponential","parameter of the exponential moving average model. Making this value smaller will cause","model of the volatility of a stock price, and return the most recent","as np def estimate_volatility(prices, l): \"\"\"Create an exponential moving average model of the","model. Making this value smaller will cause the model to weight older terms","of a stock price, and return the most recent (last) volatility estimate. Parameters","from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility","of the volatility of a stock price, and return the most recent (last)","l): \"\"\"Create an exponential moving average model of the volatility of a stock","will cause the model to weight older terms less relative to more recent","and return the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series","element of your exponential moving averge volatility model series. \"\"\" # TODO: Implement","moving averge volatility model series. \"\"\" # TODO: Implement the exponential moving average","a stock. l : float The 'lambda' parameter of the exponential moving average","last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from","stock. l : float The 'lambda' parameter of the exponential moving average model.","estimate_volatility(prices, l): \"\"\"Create an exponential moving average model of the volatility of a","model to weight older terms less relative to more recent terms. Returns -------","return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\"","np def estimate_volatility(prices, l): \"\"\"Create an exponential moving average model of the volatility","model series. \"\"\" # TODO: Implement the exponential moving average volatility model and","to weight older terms less relative to more recent terms. Returns ------- last_vol",": pandas.Series A series of adjusted closing prices for a stock. l :","averge volatility model series. \"\"\" # TODO: Implement the exponential moving average volatility","your exponential moving averge volatility model series. \"\"\" # TODO: Implement the exponential","---------- prices : pandas.Series A series of adjusted closing prices for a stock.","(last) volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing","moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'):","\"\"\"Create an exponential moving average model of the volatility of a stock price,","\"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[","prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\" prices","The 'lambda' parameter of the exponential moving average model. Making this value smaller","exponential moving averge volatility model series. \"\"\" # TODO: Implement the exponential moving","Implement the exponential moving average volatility model and return the last value. return","moving average model. Making this value smaller will cause the model to weight","to more recent terms. Returns ------- last_vol : float The last element of","older terms less relative to more recent terms. Returns ------- last_vol : float","the model to weight older terms less relative to more recent terms. Returns","# TODO: Implement the exponential moving average volatility model and return the last","the volatility of a stock price, and return the most recent (last) volatility","prices : pandas.Series A series of adjusted closing prices for a stock. l","prices for a stock. l : float The 'lambda' parameter of the exponential","an exponential moving average model of the volatility of a stock price, and","most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series of","pd import numpy as np def estimate_volatility(prices, l): \"\"\"Create an exponential moving average","of adjusted closing prices for a stock. l : float The 'lambda' parameter","index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__","the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices","numpy as np def estimate_volatility(prices, l): \"\"\"Create an exponential moving average model of","import pandas as pd import numpy as np def estimate_volatility(prices, l): \"\"\"Create an","last_vol : float The last element of your exponential moving averge volatility model","cause the model to weight older terms less relative to more recent terms.","Making this value smaller will cause the model to weight older terms less",": float The last element of your exponential moving averge volatility model series.","adjusted closing prices for a stock. l : float The 'lambda' parameter of","recent terms. Returns ------- last_vol : float The last element of your exponential","------- last_vol : float The last element of your exponential moving averge volatility","float The last element of your exponential moving averge volatility model series. \"\"\"","def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\" prices =","volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing prices","stock prices from a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most","a file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate:","model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \"\"\"Test run get_most_volatile()","stock price, and return the most recent (last) volatility estimate. Parameters ---------- prices","the exponential moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1]","for a stock. l : float The 'lambda' parameter of the exponential moving","closing prices for a stock. l : float The 'lambda' parameter of the","= pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7))) #","def estimate_volatility(prices, l): \"\"\"Create an exponential moving average model of the volatility of","file.\"\"\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices,","terms. Returns ------- last_vol : float The last element of your exponential moving","test_run(filename='data.csv'): \"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\" prices = pd.read_csv(filename,","The last element of your exponential moving averge volatility model series. \"\"\" #"],"string":"[\n \"a stock price, and return the most recent (last) volatility estimate. Parameters ----------\",\n \"pandas.Series A series of adjusted closing prices for a stock. l : float\",\n \"the exponential moving average model. Making this value smaller will cause the model\",\n \"exponential moving average model of the volatility of a stock price, and return\",\n \"recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ == '__main__': test_run()\",\n \"prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent\",\n \"A series of adjusted closing prices for a stock. l : float The\",\n \"value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from a\",\n \": float The 'lambda' parameter of the exponential moving average model. Making this\",\n \"'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if\",\n \"series. \\\"\\\"\\\" # TODO: Implement the exponential moving average volatility model and return\",\n \"return the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A\",\n \"value smaller will cause the model to weight older terms less relative to\",\n \"series of adjusted closing prices for a stock. l : float The 'lambda'\",\n \"average model of the volatility of a stock price, and return the most\",\n \"price, and return the most recent (last) volatility estimate. Parameters ---------- prices :\",\n \"TODO: Implement the exponential moving average volatility model and return the last value.\",\n \"import numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model\",\n \"print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ == '__main__':\",\n \"smaller will cause the model to weight older terms less relative to more\",\n \"the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series\",\n \"this value smaller will cause the model to weight older terms less relative\",\n \"exponential moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def\",\n \"run get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'],\",\n \"of the exponential moving average model. Making this value smaller will cause the\",\n \"'lambda' parameter of the exponential moving average model. Making this value smaller will\",\n \"prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7)))\",\n \"last element of your exponential moving averge volatility model series. \\\"\\\"\\\" # TODO:\",\n \"terms less relative to more recent terms. Returns ------- last_vol : float The\",\n \"weight older terms less relative to more recent terms. Returns ------- last_vol :\",\n \"volatility model series. \\\"\\\"\\\" # TODO: Implement the exponential moving average volatility model\",\n \"Returns ------- last_vol : float The last element of your exponential moving averge\",\n \"volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run\",\n \"estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing prices for\",\n \"moving average model of the volatility of a stock price, and return the\",\n \"of your exponential moving averge volatility model series. \\\"\\\"\\\" # TODO: Implement the\",\n \"with stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True)\",\n \"more recent terms. Returns ------- last_vol : float The last element of your\",\n \"less relative to more recent terms. Returns ------- last_vol : float The last\",\n \"get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date',\",\n \"volatility of a stock price, and return the most recent (last) volatility estimate.\",\n \"average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test\",\n \"l : float The 'lambda' parameter of the exponential moving average model. Making\",\n \"and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with\",\n \"float The 'lambda' parameter of the exponential moving average model. Making this value\",\n \"\\\"\\\"\\\" # TODO: Implement the exponential moving average volatility model and return the\",\n \"return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock\",\n \"parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7))\",\n \"exponential moving average model. Making this value smaller will cause the model to\",\n \"average model. Making this value smaller will cause the model to weight older\",\n \"Parameters ---------- prices : pandas.Series A series of adjusted closing prices for a\",\n \"relative to more recent terms. Returns ------- last_vol : float The last element\",\n \"as pd import numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving\",\n \"recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted\",\n \"pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices,\",\n \"squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__ ==\",\n \"pandas as pd import numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential\",\n \"parameter of the exponential moving average model. Making this value smaller will cause\",\n \"model of the volatility of a stock price, and return the most recent\",\n \"as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model of the\",\n \"model. Making this value smaller will cause the model to weight older terms\",\n \"of a stock price, and return the most recent (last) volatility estimate. Parameters\",\n \"from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility\",\n \"of the volatility of a stock price, and return the most recent (last)\",\n \"l): \\\"\\\"\\\"Create an exponential moving average model of the volatility of a stock\",\n \"will cause the model to weight older terms less relative to more recent\",\n \"and return the most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series\",\n \"element of your exponential moving averge volatility model series. \\\"\\\"\\\" # TODO: Implement\",\n \"moving averge volatility model series. \\\"\\\"\\\" # TODO: Implement the exponential moving average\",\n \"a stock. l : float The 'lambda' parameter of the exponential moving average\",\n \"last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from\",\n \"stock. l : float The 'lambda' parameter of the exponential moving average model.\",\n \"estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model of the volatility of a\",\n \"model to weight older terms less relative to more recent terms. Returns -------\",\n \"return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from a file.\\\"\\\"\\\"\",\n \"np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model of the volatility\",\n \"model series. \\\"\\\"\\\" # TODO: Implement the exponential moving average volatility model and\",\n \"to weight older terms less relative to more recent terms. Returns ------- last_vol\",\n \": pandas.Series A series of adjusted closing prices for a stock. l :\",\n \"averge volatility model series. \\\"\\\"\\\" # TODO: Implement the exponential moving average volatility\",\n \"your exponential moving averge volatility model series. \\\"\\\"\\\" # TODO: Implement the exponential\",\n \"---------- prices : pandas.Series A series of adjusted closing prices for a stock.\",\n \"(last) volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing\",\n \"moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'):\",\n \"\\\"\\\"\\\"Create an exponential moving average model of the volatility of a stock price,\",\n \"\\\"\\\"\\\"Test run get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[\",\n \"prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices\",\n \"The 'lambda' parameter of the exponential moving average model. Making this value smaller\",\n \"exponential moving averge volatility model series. \\\"\\\"\\\" # TODO: Implement the exponential moving\",\n \"Implement the exponential moving average volatility model and return the last value. return\",\n \"moving average model. Making this value smaller will cause the model to weight\",\n \"to more recent terms. Returns ------- last_vol : float The last element of\",\n \"older terms less relative to more recent terms. Returns ------- last_vol : float\",\n \"the model to weight older terms less relative to more recent terms. Returns\",\n \"# TODO: Implement the exponential moving average volatility model and return the last\",\n \"the volatility of a stock price, and return the most recent (last) volatility\",\n \"prices : pandas.Series A series of adjusted closing prices for a stock. l\",\n \"prices for a stock. l : float The 'lambda' parameter of the exponential\",\n \"an exponential moving average model of the volatility of a stock price, and\",\n \"most recent (last) volatility estimate. Parameters ---------- prices : pandas.Series A series of\",\n \"pd import numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average\",\n \"of adjusted closing prices for a stock. l : float The 'lambda' parameter\",\n \"index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) # print(estimate_volatility(prices, 0.7)) if __name__\",\n \"the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices\",\n \"numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model of\",\n \"import pandas as pd import numpy as np def estimate_volatility(prices, l): \\\"\\\"\\\"Create an\",\n \"last_vol : float The last element of your exponential moving averge volatility model\",\n \"cause the model to weight older terms less relative to more recent terms.\",\n \"Making this value smaller will cause the model to weight older terms less\",\n \": float The last element of your exponential moving averge volatility model series.\",\n \"adjusted closing prices for a stock. l : float The 'lambda' parameter of\",\n \"recent terms. Returns ------- last_vol : float The last element of your exponential\",\n \"------- last_vol : float The last element of your exponential moving averge volatility\",\n \"float The last element of your exponential moving averge volatility model series. \\\"\\\"\\\"\",\n \"def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices =\",\n \"volatility estimate. Parameters ---------- prices : pandas.Series A series of adjusted closing prices\",\n \"stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most\",\n \"a file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate:\",\n \"model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1] def test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile()\",\n \"stock price, and return the most recent (last) volatility estimate. Parameters ---------- prices\",\n \"the exponential moving average volatility model and return the last value. return prices.ewm(alpha=(1-l)).mean()[-1]\",\n \"for a stock. l : float The 'lambda' parameter of the exponential moving\",\n \"closing prices for a stock. l : float The 'lambda' parameter of the\",\n \"= pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices, 0.7))) #\",\n \"def estimate_volatility(prices, l): \\\"\\\"\\\"Create an exponential moving average model of the volatility of\",\n \"file.\\\"\\\"\\\" prices = pd.read_csv(filename, parse_dates=[ 'date'], index_col='date', squeeze=True) print(\\\"Most recent volatility estimate: {:.6f}\\\".format(estimate_volatility(prices,\",\n \"terms. Returns ------- last_vol : float The last element of your exponential moving\",\n \"test_run(filename='data.csv'): \\\"\\\"\\\"Test run get_most_volatile() with stock prices from a file.\\\"\\\"\\\" prices = pd.read_csv(filename,\",\n \"The last element of your exponential moving averge volatility model series. \\\"\\\"\\\" #\"\n]","isConversational":false}}},{"rowIdx":2579,"cells":{"ngram":{"kind":"list like","value":["q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())]","'C') & 1 prev = s[i] for l, r in lr: yield res[r]","= [None] * (n + 1); res[0] = 0 prev = '$' for","lr: yield res[r] - res[l] if __name__ == '__main__': ans = main() print(*ans,","sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] *","lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n","s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main():","+= (prev == 'A' and s[i] == 'C') & 1 prev = s[i]","in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i]","0 prev = '$' for i in range(1, n+1): res[i] = res[i-1] res[i]","prev = s[i] for l, r in lr: yield res[r] - res[l] if","* (n + 1); res[0] = 0 prev = '$' for i in","def main(): res = [None] * (n + 1); res[0] = 0 prev","= s[i] for l, r in lr: yield res[r] - res[l] if __name__","for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A'","1 prev = s[i] for l, r in lr: yield res[r] - res[l]","= '$' for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev","res[0] = 0 prev = '$' for i in range(1, n+1): res[i] =","'A' and s[i] == 'C') & 1 prev = s[i] for l, r","(n + 1); res[0] = 0 prev = '$' for i in range(1,","res[i] = res[i-1] res[i] += (prev == 'A' and s[i] == 'C') &","= map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] *","'$' for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev ==","l, r in lr: yield res[r] - res[l] if __name__ == '__main__': ans","map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2)","* 2) def main(): res = [None] * (n + 1); res[0] =","== 'C') & 1 prev = s[i] for l, r in lr: yield","+ sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None]","= res[i-1] res[i] += (prev == 'A' and s[i] == 'C') & 1","sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def","+ 1); res[0] = 0 prev = '$' for i in range(1, n+1):","= zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n +","i in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and","and s[i] == 'C') & 1 prev = s[i] for l, r in","res[i-1] res[i] += (prev == 'A' and s[i] == 'C') & 1 prev","prev = '$' for i in range(1, n+1): res[i] = res[i-1] res[i] +=","yield res[r] - res[l] if __name__ == '__main__': ans = main() print(*ans, sep='\\n')","== 'A' and s[i] == 'C') & 1 prev = s[i] for l,","main(): res = [None] * (n + 1); res[0] = 0 prev =","= '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res","r in lr: yield res[r] - res[l] if __name__ == '__main__': ans =","n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int,","res = [None] * (n + 1); res[0] = 0 prev = '$'","range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i] ==","(prev == 'A' and s[i] == 'C') & 1 prev = s[i] for","s[i] == 'C') & 1 prev = s[i] for l, r in lr:","for l, r in lr: yield res[r] - res[l] if __name__ == '__main__':","import sys n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr","zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n + 1);","s[i] for l, r in lr: yield res[r] - res[l] if __name__ ==","in lr: yield res[r] - res[l] if __name__ == '__main__': ans = main()","sys n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr =","'$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res =","sys.stdin.read().split())] * 2) def main(): res = [None] * (n + 1); res[0]","& 1 prev = s[i] for l, r in lr: yield res[r] -","= 0 prev = '$' for i in range(1, n+1): res[i] = res[i-1]","1); res[0] = 0 prev = '$' for i in range(1, n+1): res[i]","res[i] += (prev == 'A' and s[i] == 'C') & 1 prev =","2) def main(): res = [None] * (n + 1); res[0] = 0","[None] * (n + 1); res[0] = 0 prev = '$' for i","n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i] == 'C')"],"string":"[\n \"q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())]\",\n \"'C') & 1 prev = s[i] for l, r in lr: yield res[r]\",\n \"= [None] * (n + 1); res[0] = 0 prev = '$' for\",\n \"lr: yield res[r] - res[l] if __name__ == '__main__': ans = main() print(*ans,\",\n \"sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] *\",\n \"lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n\",\n \"s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main():\",\n \"+= (prev == 'A' and s[i] == 'C') & 1 prev = s[i]\",\n \"in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i]\",\n \"0 prev = '$' for i in range(1, n+1): res[i] = res[i-1] res[i]\",\n \"prev = s[i] for l, r in lr: yield res[r] - res[l] if\",\n \"* (n + 1); res[0] = 0 prev = '$' for i in\",\n \"def main(): res = [None] * (n + 1); res[0] = 0 prev\",\n \"= s[i] for l, r in lr: yield res[r] - res[l] if __name__\",\n \"for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A'\",\n \"1 prev = s[i] for l, r in lr: yield res[r] - res[l]\",\n \"= '$' for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev\",\n \"res[0] = 0 prev = '$' for i in range(1, n+1): res[i] =\",\n \"'A' and s[i] == 'C') & 1 prev = s[i] for l, r\",\n \"(n + 1); res[0] = 0 prev = '$' for i in range(1,\",\n \"res[i] = res[i-1] res[i] += (prev == 'A' and s[i] == 'C') &\",\n \"= map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] *\",\n \"'$' for i in range(1, n+1): res[i] = res[i-1] res[i] += (prev ==\",\n \"l, r in lr: yield res[r] - res[l] if __name__ == '__main__': ans\",\n \"map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2)\",\n \"* 2) def main(): res = [None] * (n + 1); res[0] =\",\n \"== 'C') & 1 prev = s[i] for l, r in lr: yield\",\n \"+ sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None]\",\n \"= res[i-1] res[i] += (prev == 'A' and s[i] == 'C') & 1\",\n \"sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def\",\n \"+ 1); res[0] = 0 prev = '$' for i in range(1, n+1):\",\n \"= zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n +\",\n \"i in range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and\",\n \"and s[i] == 'C') & 1 prev = s[i] for l, r in\",\n \"res[i-1] res[i] += (prev == 'A' and s[i] == 'C') & 1 prev\",\n \"prev = '$' for i in range(1, n+1): res[i] = res[i-1] res[i] +=\",\n \"yield res[r] - res[l] if __name__ == '__main__': ans = main() print(*ans, sep='\\\\n')\",\n \"== 'A' and s[i] == 'C') & 1 prev = s[i] for l,\",\n \"main(): res = [None] * (n + 1); res[0] = 0 prev =\",\n \"= '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res\",\n \"r in lr: yield res[r] - res[l] if __name__ == '__main__': ans =\",\n \"n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int,\",\n \"res = [None] * (n + 1); res[0] = 0 prev = '$'\",\n \"range(1, n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i] ==\",\n \"(prev == 'A' and s[i] == 'C') & 1 prev = s[i] for\",\n \"s[i] == 'C') & 1 prev = s[i] for l, r in lr:\",\n \"for l, r in lr: yield res[r] - res[l] if __name__ == '__main__':\",\n \"import sys n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr\",\n \"zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res = [None] * (n + 1);\",\n \"s[i] for l, r in lr: yield res[r] - res[l] if __name__ ==\",\n \"in lr: yield res[r] - res[l] if __name__ == '__main__': ans = main()\",\n \"sys n, q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr =\",\n \"'$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())] * 2) def main(): res =\",\n \"sys.stdin.read().split())] * 2) def main(): res = [None] * (n + 1); res[0]\",\n \"& 1 prev = s[i] for l, r in lr: yield res[r] -\",\n \"= 0 prev = '$' for i in range(1, n+1): res[i] = res[i-1]\",\n \"1); res[0] = 0 prev = '$' for i in range(1, n+1): res[i]\",\n \"res[i] += (prev == 'A' and s[i] == 'C') & 1 prev =\",\n \"2) def main(): res = [None] * (n + 1); res[0] = 0\",\n \"[None] * (n + 1); res[0] = 0 prev = '$' for i\",\n \"n+1): res[i] = res[i-1] res[i] += (prev == 'A' and s[i] == 'C')\"\n]","isConversational":false}}},{"rowIdx":2580,"cells":{"ngram":{"kind":"list like","value":["= torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg =","src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)","output = self.fc_out(trg) #output = [batch size, trg len, output dim] return output","embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim,","len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos","len] #src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len","for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch","device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len]","= [batch size, trg len, embed dim] for layer in self.layers: trg =","self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size,","= nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask,","= torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) *","embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) /","len, embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask)","dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position =","embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:,","forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src =","size, trg len] #enc_src = [batch size, src len, embed dim] #trg_mask =","super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim))","#trg = [batch size, trg len] #enc_src = [batch size, src len, embed","#trg = [batch size, trg len, embed dim] for layer in self.layers: trg","nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self,","pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg =","as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim,","len, embed dim] output = self.fc_out(trg) #output = [batch size, trg len, output","position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))","= nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers =","size, trg len, embed dim] for layer in self.layers: trg = layer(trg, enc_src,","nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask):","self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer in self.layers:","embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out =","+ self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer in","= [batch size, src len, embed dim] #trg_mask = [batch size, 1, trg","get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term =","[batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos","enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output =","trg len, trg len] #src_mask = [batch size, 1, 1, src len] batch_size","max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding","trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size,","dim] output = self.fc_out(trg) #output = [batch size, trg len, output dim] return","return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0,","* (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] =","size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc =","1, trg len, trg len] #src_mask = [batch size, 1, 1, src len]","= nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)])","import torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def","trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg","embed dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask =","[batch size, trg len, embed dim] output = self.fc_out(trg) #output = [batch size,","0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return pos_enc","nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim,","dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask = [batch","dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length,","range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device =","self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg","trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch","[batch size, 1, trg len, trg len] #src_mask = [batch size, 1, 1,","trg len] #src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0]","batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos =","trg len, embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask,","= trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch","= [batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim):","= nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim)","self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src,","max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0,","layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size,","import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim,","len, trg len] #src_mask = [batch size, 1, 1, src len] batch_size =","#src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len =","torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) *","embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _","src len, embed dim] #trg_mask = [batch size, 1, trg len, trg len]","output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position","enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch size,","= [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg","expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding =","= device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg","for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale =","def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term","self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim]","torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self,","embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg","= torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position *","len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg","num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim)","nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for","[batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc","nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim,","trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output = self.fc_out(trg)","DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads,","trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg","pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return","trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len,","size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos =","torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2]","(-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position","from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers,","size, src len, embed dim] #trg_mask = [batch size, 1, trg len, trg","self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device","[batch size, trg len, embed dim] for layer in self.layers: trg = layer(trg,","#output = [batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len,","size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch","DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout,","layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output","self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len,","embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads,","torch import torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module):","[batch size, src len, embed dim] #trg_mask = [batch size, 1, trg len,","torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term)","import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device,","embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term)","self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in","trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size,","= [batch size, trg len] #enc_src = [batch size, src len, embed dim]","expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout)","[batch size, trg len] #enc_src = [batch size, src len, embed dim] #trg_mask","= torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg)","in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg","__init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding","src_mask) #trg = [batch size, trg len, embed dim] output = self.fc_out(trg) #output","#pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))","max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] =","self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers","trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len,","len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim)","[batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg =","= self.fc_out(trg) #output = [batch size, trg len, output dim] return output def","/ embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position *","embed dim] output = self.fc_out(trg) #output = [batch size, trg len, output dim]","nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out","device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim)","= [batch size, trg len, embed dim] output = self.fc_out(trg) #output = [batch","= 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding =","1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0,","= nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def","* self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for","_ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device)","= self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed","#trg = [batch size, trg len, embed dim] output = self.fc_out(trg) #output =","= [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1]","len, embed dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask","pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2)","1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size,","src_mask): #trg = [batch size, trg len] #enc_src = [batch size, src len,","= layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim]","dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale","dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg =","def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__()","len] #enc_src = [batch size, src len, embed dim] #trg_mask = [batch size,","#enc_src = [batch size, src len, embed dim] #trg_mask = [batch size, 1,","self.fc_out(trg) #output = [batch size, trg len, output dim] return output def get_positional_encoding(self,","self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim,","output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1)","= trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len]","torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0)","#self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim,","size, trg len, embed dim] output = self.fc_out(trg) #output = [batch size, trg","= torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:,","nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout","import torch import torch.nn as nn from DecoderLayer import DecoderLayer import math class","#trg_mask = [batch size, 1, trg len, trg len] #src_mask = [batch size,","<filename>Decoder.py import torch import torch.nn as nn from DecoderLayer import DecoderLayer import math","= [batch size, 1, trg len, trg len] #src_mask = [batch size, 1,","size, 1, trg len, trg len] #src_mask = [batch size, 1, 1, src","1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) +","in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device","math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length","torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch","class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length =","embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim,","num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout =","div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position","trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed","trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch size, src","trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale)","trg len] #enc_src = [batch size, src len, embed dim] #trg_mask = [batch","num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding","= nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout)","output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg,","Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30):","trg len, embed dim] output = self.fc_out(trg) #output = [batch size, trg len,","2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2]","def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src","output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding =","self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer","30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length,"],"string":"[\n \"= torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg =\",\n \"src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\",\n \"output = self.fc_out(trg) #output = [batch size, trg len, output dim] return output\",\n \"embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim,\",\n \"len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos\",\n \"len] #src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len\",\n \"for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch\",\n \"device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len]\",\n \"= [batch size, trg len, embed dim] for layer in self.layers: trg =\",\n \"self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size,\",\n \"= nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask,\",\n \"= torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) *\",\n \"embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) /\",\n \"len, embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask)\",\n \"dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position =\",\n \"embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:,\",\n \"forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src =\",\n \"size, trg len] #enc_src = [batch size, src len, embed dim] #trg_mask =\",\n \"super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim))\",\n \"#trg = [batch size, trg len] #enc_src = [batch size, src len, embed\",\n \"#trg = [batch size, trg len, embed dim] for layer in self.layers: trg\",\n \"nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self,\",\n \"pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg =\",\n \"as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim,\",\n \"len, embed dim] output = self.fc_out(trg) #output = [batch size, trg len, output\",\n \"position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))\",\n \"= nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers =\",\n \"size, trg len, embed dim] for layer in self.layers: trg = layer(trg, enc_src,\",\n \"nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask):\",\n \"self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer in self.layers:\",\n \"embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out =\",\n \"+ self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer in\",\n \"= [batch size, src len, embed dim] #trg_mask = [batch size, 1, trg\",\n \"get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term =\",\n \"[batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos\",\n \"enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output =\",\n \"trg len, trg len] #src_mask = [batch size, 1, 1, src len] batch_size\",\n \"max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding\",\n \"trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size,\",\n \"dim] output = self.fc_out(trg) #output = [batch size, trg len, output dim] return\",\n \"return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0,\",\n \"* (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] =\",\n \"size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc =\",\n \"1, trg len, trg len] #src_mask = [batch size, 1, 1, src len]\",\n \"= nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)])\",\n \"import torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def\",\n \"trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg\",\n \"embed dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask =\",\n \"[batch size, trg len, embed dim] output = self.fc_out(trg) #output = [batch size,\",\n \"0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return pos_enc\",\n \"nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim,\",\n \"dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask = [batch\",\n \"dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length,\",\n \"range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device =\",\n \"self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg\",\n \"trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch\",\n \"[batch size, 1, trg len, trg len] #src_mask = [batch size, 1, 1,\",\n \"trg len] #src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0]\",\n \"batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos =\",\n \"trg len, embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask,\",\n \"= trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch\",\n \"= [batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim):\",\n \"= nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim)\",\n \"self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src,\",\n \"max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0,\",\n \"layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size,\",\n \"import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim,\",\n \"len, trg len] #src_mask = [batch size, 1, 1, src len] batch_size =\",\n \"#src_mask = [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len =\",\n \"torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) *\",\n \"embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _\",\n \"src len, embed dim] #trg_mask = [batch size, 1, trg len, trg len]\",\n \"output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position\",\n \"enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch size,\",\n \"= [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg\",\n \"expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding =\",\n \"= device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg\",\n \"for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale =\",\n \"def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term\",\n \"self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim]\",\n \"torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self,\",\n \"embed dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg\",\n \"= torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position *\",\n \"len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg\",\n \"num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim)\",\n \"nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for\",\n \"[batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc\",\n \"nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim,\",\n \"trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output = self.fc_out(trg)\",\n \"DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads,\",\n \"trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg\",\n \"pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term) return\",\n \"trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len,\",\n \"size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos =\",\n \"torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2]\",\n \"(-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position\",\n \"from DecoderLayer import DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers,\",\n \"size, src len, embed dim] #trg_mask = [batch size, 1, trg len, trg\",\n \"self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device\",\n \"[batch size, trg len, embed dim] for layer in self.layers: trg = layer(trg,\",\n \"#output = [batch size, trg len, output dim] return output def get_positional_encoding(self, max_seq_len,\",\n \"size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch\",\n \"DecoderLayer import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout,\",\n \"layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim] output\",\n \"self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len,\",\n \"embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads,\",\n \"torch import torch.nn as nn from DecoderLayer import DecoderLayer import math class Decoder(nn.Module):\",\n \"[batch size, src len, embed dim] #trg_mask = [batch size, 1, trg len,\",\n \"torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term)\",\n \"import math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device,\",\n \"embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position * div_term)\",\n \"self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in\",\n \"trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size,\",\n \"= [batch size, trg len] #enc_src = [batch size, src len, embed dim]\",\n \"expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout)\",\n \"[batch size, trg len] #enc_src = [batch size, src len, embed dim] #trg_mask\",\n \"= torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg)\",\n \"in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg\",\n \"__init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding\",\n \"src_mask) #trg = [batch size, trg len, embed dim] output = self.fc_out(trg) #output\",\n \"#pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))\",\n \"max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] =\",\n \"self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers\",\n \"trg len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len,\",\n \"len, output dim] return output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim)\",\n \"[batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg =\",\n \"= self.fc_out(trg) #output = [batch size, trg len, output dim] return output def\",\n \"/ embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2] = torch.cos(position *\",\n \"embed dim] output = self.fc_out(trg) #output = [batch size, trg len, output dim]\",\n \"nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out\",\n \"device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim)\",\n \"= [batch size, trg len, embed dim] output = self.fc_out(trg) #output = [batch\",\n \"= 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding =\",\n \"1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0,\",\n \"= nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def\",\n \"* self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for\",\n \"_ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device)\",\n \"= self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed\",\n \"#trg = [batch size, trg len, embed dim] output = self.fc_out(trg) #output =\",\n \"= [batch size, 1, 1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1]\",\n \"len, embed dim] #trg_mask = [batch size, 1, trg len, trg len] #src_mask\",\n \"pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2)\",\n \"1, src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size,\",\n \"src_mask): #trg = [batch size, trg len] #enc_src = [batch size, src len,\",\n \"= layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed dim]\",\n \"dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale\",\n \"dim] for layer in self.layers: trg = layer(trg, enc_src, trg_mask, src_mask) #trg =\",\n \"def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__()\",\n \"len] #enc_src = [batch size, src len, embed dim] #trg_mask = [batch size,\",\n \"#enc_src = [batch size, src len, embed dim] #trg_mask = [batch size, 1,\",\n \"self.fc_out(trg) #output = [batch size, trg len, output dim] return output def get_positional_encoding(self,\",\n \"self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim,\",\n \"output def get_positional_encoding(self, max_seq_len, embed_dim): pos_enc = torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1)\",\n \"= trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len]\",\n \"torch.zeros(max_seq_len, embed_dim) position = torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0)\",\n \"#self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim,\",\n \"size, trg len, embed dim] output = self.fc_out(trg) #output = [batch size, trg\",\n \"= torch.arange(0, max_seq_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:,\",\n \"nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout\",\n \"import torch import torch.nn as nn from DecoderLayer import DecoderLayer import math class\",\n \"#trg_mask = [batch size, 1, trg len, trg len] #src_mask = [batch size,\",\n \"<filename>Decoder.py import torch import torch.nn as nn from DecoderLayer import DecoderLayer import math\",\n \"= [batch size, 1, trg len, trg len] #src_mask = [batch size, 1,\",\n \"size, 1, trg len, trg len] #src_mask = [batch size, 1, 1, src\",\n \"1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale) +\",\n \"in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device\",\n \"math class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length\",\n \"torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch\",\n \"class Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length =\",\n \"embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim,\",\n \"num_heads, expand_dim, dropout) for _ in range(num_layers)]) self.fc_out = nn.Linear(embed_dim, output_dim) self.dropout =\",\n \"div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position\",\n \"trg = layer(trg, enc_src, trg_mask, src_mask) #trg = [batch size, trg len, embed\",\n \"trg_mask, src_mask): #trg = [batch size, trg len] #enc_src = [batch size, src\",\n \"trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device) #pos = [batch size, trg len] trg = self.dropout((self.tok_embedding(trg) * self.scale)\",\n \"trg len] #enc_src = [batch size, src len, embed dim] #trg_mask = [batch\",\n \"num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding\",\n \"= nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim)) self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout)\",\n \"output_dim) self.dropout = nn.Dropout(dropout) self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg,\",\n \"Decoder(nn.Module): def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30):\",\n \"trg len, embed dim] output = self.fc_out(trg) #output = [batch size, trg len,\",\n \"2) * (-math.log(10000.0) / embed_dim)) pos_enc[:, 0::2] = torch.sin(position * div_term) pos_enc[:, 1::2]\",\n \"def forward(self, trg, enc_src, trg_mask, src_mask): #trg = [batch size, trg len] #enc_src\",\n \"output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30): super().__init__() self.tok_embedding =\",\n \"self.scale) + self.pos_embedding(pos)) #trg = [batch size, trg len, embed dim] for layer\",\n \"30): super().__init__() self.tok_embedding = nn.Embedding(output_dim, embed_dim) #self.pos_embedding = nn.Embedding(max_length, embed_dim) self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length,\"\n]","isConversational":false}}},{"rowIdx":2581,"cells":{"ngram":{"kind":"list like","value":["from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\"","return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None:","False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return","Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up","documentation on setting up the nxos proxy minion look in the documentation for","on setting up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`.","the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions",":mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log","import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__)","For documentation on setting up the nxos proxy minion look in the documentation","salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def","__virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False,","\"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return","try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None):","return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if","{} if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else: data =","if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else: data = salt.utils.nxos.version_info()",".. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion look","import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__","as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy","Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos","= \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err","salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\"nxos.initialized\"]() is False: return {}","__virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\"nxos.initialized\"]()","salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if","= logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except","proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos","minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import","salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"]","proxy is None: return {} if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\":","def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__","proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else: data = salt.utils.nxos.version_info() return","NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try:","except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy():","return {} if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else: data","is None: return {} if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()}","if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\"nxos.initialized\"]() is False: return","for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError","def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\"nxos.initialized\"]() is","<gh_stars>1000+ \"\"\" Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on","up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import","2016.11.0 For documentation on setting up the nxos proxy minion look in the","NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy","= [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err:","NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if","err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {}","log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info()","\"\"\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log =","err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is","minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion","logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__","if proxy is None: return {} if proxy[\"nxos.initialized\"]() is False: return {} return","versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion look in","in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform from","None: return {} if proxy[\"nxos.initialized\"]() is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else:","documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import","is False: return {} return {\"nxos\": proxy[\"nxos.grains\"]()} else: data = salt.utils.nxos.version_info() return salt.utils.nxos.system_info(data)","setting up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\"","logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError","[\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return","look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import salt.utils.nxos import salt.utils.platform","import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ =","for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the","import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__():","__proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as","__virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def","the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging","salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ =","system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\"nxos.initialized\"]() is False:","\"\"\" Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting","nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \"\"\" import logging import"],"string":"[\n \"from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\"\",\n \"return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None:\",\n \"False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return\",\n \"Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up\",\n \"documentation on setting up the nxos proxy minion look in the documentation for\",\n \"on setting up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`.\",\n \"the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions\",\n \":mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log\",\n \"import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__)\",\n \"For documentation on setting up the nxos proxy minion look in the documentation\",\n \"salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def\",\n \"__virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False,\",\n \"\\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return\",\n \"try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None):\",\n \"return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if\",\n \"{} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else: data =\",\n \"if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else: data = salt.utils.nxos.version_info()\",\n \".. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion look\",\n \"import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__\",\n \"as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy\",\n \"Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos\",\n \"= \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err\",\n \"salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {}\",\n \"__virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]()\",\n \"salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if\",\n \"= logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except\",\n \"proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos\",\n \"minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import\",\n \"salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"]\",\n \"proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\":\",\n \"def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__\",\n \"proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else: data = salt.utils.nxos.version_info() return\",\n \"NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try:\",\n \"except NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy():\",\n \"return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else: data\",\n \"is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()}\",\n \"if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return\",\n \"for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError\",\n \"def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is\",\n \"<gh_stars>1000+ \\\"\\\"\\\" Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on\",\n \"up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import\",\n \"2016.11.0 For documentation on setting up the nxos proxy minion look in the\",\n \"NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy\",\n \"= [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err:\",\n \"NxosClientError as err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if\",\n \"err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {}\",\n \"log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info()\",\n \"\\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log =\",\n \"err: return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is\",\n \"minions .. versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion\",\n \"logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__\",\n \"if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return\",\n \"versionadded:: 2016.11.0 For documentation on setting up the nxos proxy minion look in\",\n \"in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from\",\n \"None: return {} if proxy[\\\"nxos.initialized\\\"]() is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else:\",\n \"documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform from salt.exceptions import\",\n \"is False: return {} return {\\\"nxos\\\": proxy[\\\"nxos.grains\\\"]()} else: data = salt.utils.nxos.version_info() return salt.utils.nxos.system_info(data)\",\n \"setting up the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\"\",\n \"logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError\",\n \"[\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return\",\n \"look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import salt.utils.nxos import salt.utils.platform\",\n \"import salt.utils.nxos import salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ =\",\n \"for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting up the\",\n \"import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__():\",\n \"__proxyenabled__ = [\\\"nxos\\\"] __virtualname__ = \\\"nxos\\\" def __virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as\",\n \"__virtual__(): try: salt.utils.nxos.version_info() except NxosClientError as err: return False, err return __virtualname__ def\",\n \"the nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging\",\n \"salt.utils.platform from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\\\"nxos\\\"] __virtualname__ =\",\n \"system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None: return {} if proxy[\\\"nxos.initialized\\\"]() is False:\",\n \"\\\"\\\"\\\" Grains for Cisco NX-OS minions .. versionadded:: 2016.11.0 For documentation on setting\",\n \"nxos proxy minion look in the documentation for :mod:`salt.proxy.nxos<salt.proxy.nxos>`. \\\"\\\"\\\" import logging import\"\n]","isConversational":false}}},{"rowIdx":2582,"cells":{"ngram":{"kind":"list like","value":["self.account = account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field","{}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver,","unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type,","None, last_fw_ver: str = None, first_online: str = None, use_nat: bool = False,","str = None, first_online: str = None, use_nat: bool = False, operation_system: str","None, device_type: int = None, limit: int = 50, provider: int = None,","= None, device_type: int = None, provider: int = None): self.unique_id = unique_id","dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"],","int = None, quick_search: str = \"\", remote_custom_field: str = None, sort: str","TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"],","None, sort: str = \"\", start: int = 0, unique_id: str = \"\")","= ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online =","unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment:","self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider,","int = None, ipaddr: str = None, mac: str = None, remote_custom_field: str",") return device @classmethod def get_list(cls, account: int = None, device_type: int =","\"/devices/\" def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str","get_list(cls, account: int = None, device_type: int = None, limit: int = 50,","= device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device =","self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online","self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment","last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def","from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str,","{}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat,","None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr = ipaddr","= operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def","import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\"","str, account: int, device_id: int = None, ipaddr: str = None, mac: str","self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac","self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr","= \"/devices/\" def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr:","mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] )","object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"],","TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str, account: int, device_id:","self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"],","device_type: int = None, limit: int = 50, provider: int = None, quick_search:","account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field","last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type: {},","self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat","False, operation_system: str = None, udpxy_addr: str = None, device_type: int = None,","int = None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr","self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod","int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return","= last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system =","remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field:","remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat: {},","account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system,","return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {},","\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type:","use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider","__str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\","from typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url","int = 0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start,","{}, comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system:","device @classmethod def get_list(cls, account: int = None, device_type: int = None, limit:","0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account,","unique_id: str, account: int, device_id: int = None, ipaddr: str = None, mac:","self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider, self.account","account: int, device_id: int = None, ipaddr: str = None, mac: str =","import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str, account: int,","str = None, comment: str = None, last_online: str = None, last_fw_ver: str","self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict)","def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"],","remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return","None, mac: str = None, remote_custom_field: str = None, comment: str = None,","= unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac =","quick_search: str = \"\", remote_custom_field: str = None, sort: str = \"\", start:","use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format(","unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"],","account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{},","\"\", remote_custom_field: str = None, sort: str = \"\", start: int = 0,","int = None, provider: int = None): self.unique_id = unique_id self.account = account","int = 50, provider: int = None, quick_search: str = \"\", remote_custom_field: str","provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online,","device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"],","= None, udpxy_addr: str = None, device_type: int = None, provider: int =","= None, limit: int = 50, provider: int = None, quick_search: str =","= \"\", remote_custom_field: str = None, sort: str = \"\", start: int =","None, ipaddr: str = None, mac: str = None, remote_custom_field: str = None,","str = None, udpxy_addr: str = None, device_type: int = None, provider: int","= remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online =","str = None, sort: str = \"\", start: int = 0, unique_id: str","mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online: {},","device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online,","= None, sort: str = \"\", start: int = 0, unique_id: str =","-> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"],","None, last_online: str = None, last_fw_ver: str = None, first_online: str = None,","operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account: int","remote_custom_field: str = None, comment: str = None, last_online: str = None, last_fw_ver:","first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider: {},","{}, \\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {},","\"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field,","last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr","provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{},","operation_system: str = None, udpxy_addr: str = None, device_type: int = None, provider:","provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account: int = None, device_type:","comment: str = None, last_online: str = None, last_fw_ver: str = None, first_online:","self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device","account: int = None, device_type: int = None, limit: int = 50, provider:","{}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider: {}, account:","str = None, mac: str = None, remote_custom_field: str = None, comment: str","= None, remote_custom_field: str = None, comment: str = None, last_online: str =","typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url =","= mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver =","str = None, device_type: int = None, provider: int = None): self.unique_id =","_path_url = \"/devices/\" def __init__(self, unique_id: str, account: int, device_id: int = None,","def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str =","account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account: int = None, device_type: int","str = \"\", remote_custom_field: str = None, sort: str = \"\", start: int","= None, comment: str = None, last_online: str = None, last_fw_ver: str =","class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str, account: int, device_id: int","= device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment =","self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online","def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {},","comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {},","self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider, self.account )","None, comment: str = None, last_online: str = None, last_fw_ver: str = None,","str = None, last_fw_ver: str = None, first_online: str = None, use_nat: bool","{}, last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\","= last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr =","\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id,","{}, \\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac,","device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice(","return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver:","sort: str = \"\", start: int = 0, unique_id: str = \"\") ->","None, quick_search: str = \"\", remote_custom_field: str = None, sort: str = \"\",","last_fw_ver: str = None, first_online: str = None, use_nat: bool = False, operation_system:","= TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"],","-> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort,","{}, operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id,","ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online","tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str, account:","unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat:","= \"\", start: int = 0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]:","_dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"],","int = None, device_type: int = None, limit: int = 50, provider: int","mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver","sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {},","str = None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str","self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system","Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self,","ipaddr: str = None, mac: str = None, remote_custom_field: str = None, comment:","first_online: str = None, use_nat: bool = False, operation_system: str = None, udpxy_addr:","= None, last_fw_ver: str = None, first_online: str = None, use_nat: bool =","= None, mac: str = None, remote_custom_field: str = None, comment: str =","\"\", start: int = 0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices","{}, first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider:","limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return","str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider,","device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{},","ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver: {}, first_online:","= udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) ->","device_id: int = None, ipaddr: str = None, mac: str = None, remote_custom_field:","return device @classmethod def get_list(cls, account: int = None, device_type: int = None,","ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"]","= super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def","Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id:","last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system","def get_list(cls, account: int = None, device_type: int = None, limit: int =","<gh_stars>0 from typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel):","\"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\ last_fw_ver: {},","None, provider: int = None): self.unique_id = unique_id self.account = account self.id =","device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment","use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account:","remote_custom_field: str = None, sort: str = \"\", start: int = 0, unique_id:","provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"],","start: int = 0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices =","devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online:","= use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider =","device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account: int = None,","__init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str = None,","device_type: int = None, provider: int = None): self.unique_id = unique_id self.account =","= account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field =","devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices","= None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr =","{}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr,","limit: int = 50, provider: int = None, quick_search: str = \"\", remote_custom_field:","quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \"\"\"id:{}, ipaddr:{}, mac:{}, unique_id:{},","udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field,","None, device_type: int = None, provider: int = None): self.unique_id = unique_id self.account","udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls, account: int =","self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type","= 0, unique_id: str = \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit,","= None, ipaddr: str = None, mac: str = None, remote_custom_field: str =","self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type,","comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat","= None, quick_search: str = \"\", remote_custom_field: str = None, sort: str =","{}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment,","= None, device_type: int = None, limit: int = 50, provider: int =","50, provider: int = None, quick_search: str = \"\", remote_custom_field: str = None,","first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type","operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict:","udpxy_addr: str = None, device_type: int = None, provider: int = None): self.unique_id","= None, provider: int = None): self.unique_id = unique_id self.account = account self.id","@classmethod def get_list(cls, account: int = None, device_type: int = None, limit: int","operation_system: {}, \\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr,","= 50, provider: int = None, quick_search: str = \"\", remote_custom_field: str =","mac: str = None, remote_custom_field: str = None, comment: str = None, last_online:","unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac = mac","= None, first_online: str = None, use_nat: bool = False, operation_system: str =","use_nat: bool = False, operation_system: str = None, udpxy_addr: str = None, device_type:","provider: int = None): self.unique_id = unique_id self.account = account self.id = device_id","self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver","List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def","= False, operation_system: str = None, udpxy_addr: str = None, device_type: int =","= None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str =","None, udpxy_addr: str = None, device_type: int = None, provider: int = None):","str = \"\", start: int = 0, unique_id: str = \"\") -> Optional[Tuple[List[object],","last_online: {}, \\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\ udpxy_addr:","None, first_online: str = None, use_nat: bool = False, operation_system: str = None,","= \"\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search,","= first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type =","Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id)","str = None, remote_custom_field: str = None, comment: str = None, last_online: str","int = None, limit: int = 50, provider: int = None, quick_search: str","str = None, last_online: str = None, last_fw_ver: str = None, first_online: str","self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider","last_online: str = None, last_fw_ver: str = None, first_online: str = None, use_nat:","= comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat =","last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod","None, limit: int = 50, provider: int = None, quick_search: str = \"\",","= provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"],","device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"], remote_custom_field=device_dict[\"remote_custom_field\"], comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"],","int, device_id: int = None, ipaddr: str = None, mac: str = None,","None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str = None,","remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online","super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self):","TmsDevice(TmsExtendedModel): _path_url = \"/devices/\" def __init__(self, unique_id: str, account: int, device_id: int =","udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object:","first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device @classmethod def get_list(cls,","comment=device_dict[\"comment\"], last_online=device_dict[\"last_online\"], last_fw_ver=device_dict[\"last_fw_ver\"], first_online=device_dict[\"first_online\"], use_nat=device_dict[\"use_nat\"], operation_system=device_dict[\"operation_system\"], udpxy_addr=device_dict[\"udpxy_addr\"], device_type=device_dict[\"device_type\"], provider=device_dict[\"provider\"], account=device_dict[\"account\"] ) return device","bool = False, operation_system: str = None, udpxy_addr: str = None, device_type: int","provider: int = None, quick_search: str = \"\", remote_custom_field: str = None, sort:","None, remote_custom_field: str = None, comment: str = None, last_online: str = None,","= None, last_online: str = None, last_fw_ver: str = None, first_online: str =","@staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\"unique_id\"], device_id=device_dict[\"id\"], ipaddr=device_dict[\"ipaddr\"], mac=device_dict[\"mac\"],"],"string":"[\n \"self.account = account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field\",\n \"{}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver,\",\n \"unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type,\",\n \"None, last_fw_ver: str = None, first_online: str = None, use_nat: bool = False,\",\n \"str = None, first_online: str = None, use_nat: bool = False, operation_system: str\",\n \"None, device_type: int = None, limit: int = 50, provider: int = None,\",\n \"= None, device_type: int = None, provider: int = None): self.unique_id = unique_id\",\n \"dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"],\",\n \"int = None, quick_search: str = \\\"\\\", remote_custom_field: str = None, sort: str\",\n \"TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"],\",\n \"None, sort: str = \\\"\\\", start: int = 0, unique_id: str = \\\"\\\")\",\n \"= ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online =\",\n \"unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment:\",\n \"self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider,\",\n \"int = None, ipaddr: str = None, mac: str = None, remote_custom_field: str\",\n \") return device @classmethod def get_list(cls, account: int = None, device_type: int =\",\n \"\\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str\",\n \"get_list(cls, account: int = None, device_type: int = None, limit: int = 50,\",\n \"= device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device =\",\n \"self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online\",\n \"self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment\",\n \"last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def\",\n \"from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str,\",\n \"{}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat,\",\n \"None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr = ipaddr\",\n \"= operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def\",\n \"import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\"\",\n \"str, account: int, device_id: int = None, ipaddr: str = None, mac: str\",\n \"self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac\",\n \"self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr\",\n \"= \\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr:\",\n \"mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] )\",\n \"object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"],\",\n \"TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id:\",\n \"self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"],\",\n \"device_type: int = None, limit: int = 50, provider: int = None, quick_search:\",\n \"account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field\",\n \"last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {},\",\n \"self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat\",\n \"False, operation_system: str = None, udpxy_addr: str = None, device_type: int = None,\",\n \"int = None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr\",\n \"self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod\",\n \"int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return\",\n \"= last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system =\",\n \"remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field:\",\n \"remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {},\",\n \"account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system,\",\n \"return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {},\",\n \"\\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type:\",\n \"use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider\",\n \"__str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\\",\n \"from typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url\",\n \"int = 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start,\",\n \"{}, comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system:\",\n \"device @classmethod def get_list(cls, account: int = None, device_type: int = None, limit:\",\n \"0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account,\",\n \"unique_id: str, account: int, device_id: int = None, ipaddr: str = None, mac:\",\n \"self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider, self.account\",\n \"account: int, device_id: int = None, ipaddr: str = None, mac: str =\",\n \"import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account: int,\",\n \"str = None, comment: str = None, last_online: str = None, last_fw_ver: str\",\n \"self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict)\",\n \"def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"],\",\n \"remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return\",\n \"None, mac: str = None, remote_custom_field: str = None, comment: str = None,\",\n \"= unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac =\",\n \"quick_search: str = \\\"\\\", remote_custom_field: str = None, sort: str = \\\"\\\", start:\",\n \"use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format(\",\n \"unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"],\",\n \"account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{},\",\n \"\\\"\\\", remote_custom_field: str = None, sort: str = \\\"\\\", start: int = 0,\",\n \"int = None, provider: int = None): self.unique_id = unique_id self.account = account\",\n \"int = 50, provider: int = None, quick_search: str = \\\"\\\", remote_custom_field: str\",\n \"provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online,\",\n \"device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"],\",\n \"= None, udpxy_addr: str = None, device_type: int = None, provider: int =\",\n \"= None, limit: int = 50, provider: int = None, quick_search: str =\",\n \"= \\\"\\\", remote_custom_field: str = None, sort: str = \\\"\\\", start: int =\",\n \"None, ipaddr: str = None, mac: str = None, remote_custom_field: str = None,\",\n \"str = None, udpxy_addr: str = None, device_type: int = None, provider: int\",\n \"= remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online =\",\n \"str = None, sort: str = \\\"\\\", start: int = 0, unique_id: str\",\n \"mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {},\",\n \"device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online,\",\n \"= None, sort: str = \\\"\\\", start: int = 0, unique_id: str =\",\n \"-> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"],\",\n \"None, last_online: str = None, last_fw_ver: str = None, first_online: str = None,\",\n \"operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account: int\",\n \"remote_custom_field: str = None, comment: str = None, last_online: str = None, last_fw_ver:\",\n \"first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider: {},\",\n \"{}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {},\",\n \"\\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field,\",\n \"last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr\",\n \"provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{},\",\n \"operation_system: str = None, udpxy_addr: str = None, device_type: int = None, provider:\",\n \"provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account: int = None, device_type:\",\n \"comment: str = None, last_online: str = None, last_fw_ver: str = None, first_online:\",\n \"self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device\",\n \"account: int = None, device_type: int = None, limit: int = 50, provider:\",\n \"{}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider: {}, account:\",\n \"str = None, mac: str = None, remote_custom_field: str = None, comment: str\",\n \"= None, remote_custom_field: str = None, comment: str = None, last_online: str =\",\n \"typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url =\",\n \"= mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver =\",\n \"str = None, device_type: int = None, provider: int = None): self.unique_id =\",\n \"_path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id: int = None,\",\n \"def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str =\",\n \"account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account: int = None, device_type: int\",\n \"str = \\\"\\\", remote_custom_field: str = None, sort: str = \\\"\\\", start: int\",\n \"= None, comment: str = None, last_online: str = None, last_fw_ver: str =\",\n \"class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id: int\",\n \"= device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment =\",\n \"self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online\",\n \"def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {},\",\n \"comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {},\",\n \"self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type, self.provider, self.account )\",\n \"None, comment: str = None, last_online: str = None, last_fw_ver: str = None,\",\n \"str = None, last_fw_ver: str = None, first_online: str = None, use_nat: bool\",\n \"{}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\\\\",\n \"= last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr =\",\n \"\\\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id,\",\n \"{}, \\\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac,\",\n \"device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice(\",\n \"return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver:\",\n \"sort: str = \\\"\\\", start: int = 0, unique_id: str = \\\"\\\") ->\",\n \"None, quick_search: str = \\\"\\\", remote_custom_field: str = None, sort: str = \\\"\\\",\",\n \"last_fw_ver: str = None, first_online: str = None, use_nat: bool = False, operation_system:\",\n \"= TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"],\",\n \"-> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort,\",\n \"{}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id,\",\n \"ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online\",\n \"tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account:\",\n \"unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat:\",\n \"= \\\"\\\", start: int = 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]:\",\n \"_dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"],\",\n \"int = None, device_type: int = None, limit: int = 50, provider: int\",\n \"mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver\",\n \"sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {},\",\n \"str = None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str\",\n \"self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system\",\n \"Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self,\",\n \"ipaddr: str = None, mac: str = None, remote_custom_field: str = None, comment:\",\n \"first_online: str = None, use_nat: bool = False, operation_system: str = None, udpxy_addr:\",\n \"= None, last_fw_ver: str = None, first_online: str = None, use_nat: bool =\",\n \"= None, mac: str = None, remote_custom_field: str = None, comment: str =\",\n \"\\\"\\\", start: int = 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices\",\n \"{}, first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider:\",\n \"limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return\",\n \"str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider,\",\n \"device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{},\",\n \"ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver: {}, first_online:\",\n \"= udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) ->\",\n \"device_id: int = None, ipaddr: str = None, mac: str = None, remote_custom_field:\",\n \"return device @classmethod def get_list(cls, account: int = None, device_type: int = None,\",\n \"ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"]\",\n \"= super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def\",\n \"Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id:\",\n \"last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system\",\n \"def get_list(cls, account: int = None, device_type: int = None, limit: int =\",\n \"<gh_stars>0 from typing import List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel):\",\n \"\\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \\\\ last_fw_ver: {},\",\n \"None, provider: int = None): self.unique_id = unique_id self.account = account self.id =\",\n \"device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment\",\n \"use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account:\",\n \"remote_custom_field: str = None, sort: str = \\\"\\\", start: int = 0, unique_id:\",\n \"provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"],\",\n \"start: int = 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices =\",\n \"devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online:\",\n \"= use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider =\",\n \"device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account: int = None,\",\n \"__init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str = None,\",\n \"device_type: int = None, provider: int = None): self.unique_id = unique_id self.account =\",\n \"= account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field =\",\n \"devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices\",\n \"= None): self.unique_id = unique_id self.account = account self.id = device_id self.ipaddr =\",\n \"{}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr,\",\n \"limit: int = 50, provider: int = None, quick_search: str = \\\"\\\", remote_custom_field:\",\n \"quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self): return \\\"\\\"\\\"id:{}, ipaddr:{}, mac:{}, unique_id:{},\",\n \"udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field,\",\n \"None, device_type: int = None, provider: int = None): self.unique_id = unique_id self.account\",\n \"udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls, account: int =\",\n \"self.first_online = first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type\",\n \"= 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit,\",\n \"= None, ipaddr: str = None, mac: str = None, remote_custom_field: str =\",\n \"self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver, self.first_online, self.use_nat, self.operation_system, self.udpxy_addr, self.device_type,\",\n \"comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat = use_nat\",\n \"= None, quick_search: str = \\\"\\\", remote_custom_field: str = None, sort: str =\",\n \"{}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment,\",\n \"= None, device_type: int = None, limit: int = 50, provider: int =\",\n \"50, provider: int = None, quick_search: str = \\\"\\\", remote_custom_field: str = None,\",\n \"first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type\",\n \"operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict:\",\n \"udpxy_addr: str = None, device_type: int = None, provider: int = None): self.unique_id\",\n \"= None, provider: int = None): self.unique_id = unique_id self.account = account self.id\",\n \"@classmethod def get_list(cls, account: int = None, device_type: int = None, limit: int\",\n \"operation_system: {}, \\\\ udpxy_addr: {}, device_type: {}, provider: {}, account: {}\\\"\\\"\\\".format( self.id, self.ipaddr,\",\n \"= 50, provider: int = None, quick_search: str = \\\"\\\", remote_custom_field: str =\",\n \"mac: str = None, remote_custom_field: str = None, comment: str = None, last_online:\",\n \"unique_id self.account = account self.id = device_id self.ipaddr = ipaddr self.mac = mac\",\n \"= None, first_online: str = None, use_nat: bool = False, operation_system: str =\",\n \"use_nat: bool = False, operation_system: str = None, udpxy_addr: str = None, device_type:\",\n \"provider: int = None): self.unique_id = unique_id self.account = account self.id = device_id\",\n \"self.mac = mac self.remote_custom_field = remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver\",\n \"List, Optional, Tuple from tmsproviderapisdk.tms_extended_model import TmsExtendedModel class TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def\",\n \"= False, operation_system: str = None, udpxy_addr: str = None, device_type: int =\",\n \"= None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str =\",\n \"None, udpxy_addr: str = None, device_type: int = None, provider: int = None):\",\n \"str = \\\"\\\", start: int = 0, unique_id: str = \\\"\\\") -> Optional[Tuple[List[object],\",\n \"last_online: {}, \\\\ last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \\\\ udpxy_addr:\",\n \"None, first_online: str = None, use_nat: bool = False, operation_system: str = None,\",\n \"= \\\"\\\") -> Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search,\",\n \"= first_online self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type =\",\n \"Optional[Tuple[List[object], int]]: devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id)\",\n \"str = None, remote_custom_field: str = None, comment: str = None, last_online: str\",\n \"int = None, limit: int = 50, provider: int = None, quick_search: str\",\n \"str = None, last_online: str = None, last_fw_ver: str = None, first_online: str\",\n \"self.use_nat = use_nat self.operation_system = operation_system self.udpxy_addr = udpxy_addr self.device_type = device_type self.provider\",\n \"last_online: str = None, last_fw_ver: str = None, first_online: str = None, use_nat:\",\n \"= comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online self.use_nat =\",\n \"last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod\",\n \"None, limit: int = 50, provider: int = None, quick_search: str = \\\"\\\",\",\n \"= provider @staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"],\",\n \"device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"], remote_custom_field=device_dict[\\\"remote_custom_field\\\"], comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"],\",\n \"int, device_id: int = None, ipaddr: str = None, mac: str = None,\",\n \"None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str = None,\",\n \"remote_custom_field self.comment = comment self.last_online = last_online self.last_fw_ver = last_fw_ver self.first_online = first_online\",\n \"super().get_list(start=start, limit=limit, account=account, device_type=device_type, provider=provider, quick_search=quick_search, remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id) return devices def __str__(self):\",\n \"TmsDevice(TmsExtendedModel): _path_url = \\\"/devices/\\\" def __init__(self, unique_id: str, account: int, device_id: int =\",\n \"udpxy_addr self.device_type = device_type self.provider = provider @staticmethod def _dict_to_object(device_dict: dict) -> object:\",\n \"first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device @classmethod def get_list(cls,\",\n \"comment=device_dict[\\\"comment\\\"], last_online=device_dict[\\\"last_online\\\"], last_fw_ver=device_dict[\\\"last_fw_ver\\\"], first_online=device_dict[\\\"first_online\\\"], use_nat=device_dict[\\\"use_nat\\\"], operation_system=device_dict[\\\"operation_system\\\"], udpxy_addr=device_dict[\\\"udpxy_addr\\\"], device_type=device_dict[\\\"device_type\\\"], provider=device_dict[\\\"provider\\\"], account=device_dict[\\\"account\\\"] ) return device\",\n \"bool = False, operation_system: str = None, udpxy_addr: str = None, device_type: int\",\n \"provider: int = None, quick_search: str = \\\"\\\", remote_custom_field: str = None, sort:\",\n \"None, remote_custom_field: str = None, comment: str = None, last_online: str = None,\",\n \"= None, last_online: str = None, last_fw_ver: str = None, first_online: str =\",\n \"@staticmethod def _dict_to_object(device_dict: dict) -> object: device = TmsDevice( unique_id=device_dict[\\\"unique_id\\\"], device_id=device_dict[\\\"id\\\"], ipaddr=device_dict[\\\"ipaddr\\\"], mac=device_dict[\\\"mac\\\"],\"\n]","isConversational":false}}},{"rowIdx":2583,"cells":{"ngram":{"kind":"list like","value":["def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad","Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D","dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0)","make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L,","__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class","assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field,","Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx, walls): assert","self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L,","** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def","elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented in","if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field,","field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx)","dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a,","== 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif","# Note, inheritance order matters to get walled grad & laplacian call #","__init__(self, L, dim): self.L = L self.dim = dim @property def L_half(self): return","self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self,","a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls)","dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3:","self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L,","from __future__ import print_function, division import numpy as np from ciabatta.meta import make_repr_str","_laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1:","field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension') return grad","A_i(self): return self.M ** self.dim @property def dA(self): return self.dx ** self.dim def","if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad,","int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property def A_i(self):","class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim,","('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self,","self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def","self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r,","dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx)","Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx,","self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get walled grad","grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx >","def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx,","walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2","('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim","python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D,","_div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim ==","return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim)","walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else:","__repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def","Grad_i not implemented in this dimension\") return grad_i def _walled_laplace(field, dx, walls): assert","2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i,","self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx)","def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def","class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L","(field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2:","walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim = dim","self.M = int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property","a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls,","def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0","div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0","grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx)","grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls)","== 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim ==","* (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r):","* self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D","grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,),","self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant')","field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad not implemented","def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace","L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L /","0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim","elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in","dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim","= L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] *","dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented","field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds,","make_repr_str(self, fs) # Note, inheritance order matters to get walled grad & laplacian","__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)]","walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L,","= np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim","= np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2:","self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) #","* a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r),","this dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert","i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),","self.walls = walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def","f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif","> self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion","self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)]","L, dx): assert r.ndim == 2 M = int(round(L / dx)) dx =","[('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim,","& laplacian call # (see diamond problem on wikipedia and how python handles","== 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds,","def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls","field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad,","def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim ==","return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx >","= D self.dt = dt if self.D > self.dx ** 2 / (2.0","r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx)","elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in","3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension')","return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx","import numpy as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics,","dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make","dx, walls) else: raise Exception(\"Walled Grad_i not implemented in this dimension\") return grad_i","else: raise Exception(\"Grad_i not implemented in this dimension\") return grad_i def _grad(field, dx):","dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented","self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L',","raise Exception('Density calc not implemented in this dimension') return f / dx **","where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self,","1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3:","f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented","walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim","density(r, L, dx): assert r.ndim == 2 M = int(round(L / dx)) dx","L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,),","dim @property def L_half(self): return self.L / 2.0 @property def A(self): return self.L","self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a,","dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1:","Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L","dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def","dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self,","self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return","field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in","f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise","0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif","a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued","self.M ** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r):","in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape","def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx,","field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension') return div","else: raise Exception('Grad not implemented in this dimension') return grad def _div(field, dx):","walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3:","elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad not","self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L,","self.L / self.M @property def A_i(self): return self.M ** self.dim @property def dA(self):","('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs)","walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field,","return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),","dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f)","return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M =","a_0=a_0) self.D = D self.dt = dt if self.D > self.dx ** 2","_grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L',","fields of all kinds. \"\"\" from __future__ import print_function, division import numpy as","== 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx,","implemented in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape ==","== 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension')","3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad not implemented in this","return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs =","[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs)","def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs =","walls): assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if","in this dimension') return grad def _div(field, dx): assert dx > 0.0 div","L, dim): self.L = L self.dim = dim @property def L_half(self): return self.L","/ (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a","grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif","assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim","= np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim","np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3:","self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L),","= L self.dim = dim @property def L_half(self): return self.L / 2.0 @property","> 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls)","__future__ import print_function, division import numpy as np from ciabatta.meta import make_repr_str from","self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),","iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L',","__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D),","class Space(object): def __init__(self, L, dim): self.L = L self.dim = dim @property","assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i","walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension') return","return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs =","dx(self): return self.L / self.M @property def A_i(self): return self.M ** self.dim @property","elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad,","inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls)","laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not","self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)]","dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field)","numpy as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics","= [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def","field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx)","Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where","lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1:","dimension\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx","('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0):","Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L /","dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian","walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif","dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim ==","** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if","assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field,","= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)]","def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return","fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field):","class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self,","field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension') return f","*= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a,","self.dt = dt if self.D > self.dx ** 2 / (2.0 * self.dim","in this dimension\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape ==","if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif","return f / dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0","self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim',","field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx)","def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert","== 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension')","dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2:","a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L),","a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls),","inheritance order matters to get walled grad & laplacian call # (see diamond","grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else:","dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field,","dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented","[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar):","self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls',","walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls',","def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0',","L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self):","fs) # Note, inheritance order matters to get walled grad & laplacian call","= np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim ==","raise Exception(\"Walled grad not implemented in this dimension\") return grad def _walled_grad_i(field, inds,","make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M","self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls),","field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx)","to get walled grad & laplacian call # (see diamond problem on wikipedia","= np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif","elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field,","r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs","walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not implemented in this","*args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self,","return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',","== 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim ==","grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3:","it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0):","field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim ==","= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class","elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in","field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim","not implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert dx","== 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else:","walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif","dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return","2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self):","elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field,","div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not","Exception('Grad not implemented in this dimension') return grad def _div(field, dx): assert dx","get walled grad & laplacian call # (see diamond problem on wikipedia and","= np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls)","np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim ==","= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt),","3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise","_walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad =","walls) else: raise Exception(\"Walled grad not implemented in this dimension\") return grad def","in this dimension\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape","self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx,","constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs","== 2 M = int(round(L / dx)) dx = L / M inds","int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r, L, dx)","(2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a +=","L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def","lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)]","walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx,","== 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx,","A class hierarchy relating to fields of all kinds. \"\"\" from __future__ import","return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return","and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx,","D self.dt = dt if self.D > self.dx ** 2 / (2.0 *","dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a","3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension') return","walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not","inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim","== 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension')","if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field,","not implemented in this dimension\") return grad def _walled_grad_i(field, inds, dx, walls): assert","fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim,","grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self):","3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return","walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field","dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div,","== walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if","division import numpy as np from ciabatta.meta import make_repr_str from fealty import lattice,","('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to","2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise","('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r,","call # (see diamond problem on wikipedia and how python handles it) class","field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad","== walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim ==","dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a,","this dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert","elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i","A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs","grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx,","_laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0',","fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt',","return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',","** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return","dimension') return grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1],","def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D',","def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim',","walls) else: raise Exception(\"Walled Grad_i not implemented in this dimension\") return grad_i def","> 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif","handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt,","return self.L / 2.0 @property def A(self): return self.L ** self.dim def iterate(self,","2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass","[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0',","implemented in this dimension\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape ==","> 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx)","fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs)","walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad","== 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not implemented","dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2:","field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this","def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L,","self.D > self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable","def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx))","assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace,","dx) else: raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field,","@property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def","dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i =","field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)","np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif","_grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a,","self.M @property def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx","grad & laplacian call # (see diamond problem on wikipedia and how python","grad, dx) else: raise Exception('Grad not implemented in this dimension') return grad def","field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad,","self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters","f / dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace","pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class","WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0)","implemented in this dimension\") return grad_i def _grad(field, dx): assert dx > 0.0","field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds,","dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field,","D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt =","self.L / 2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args,","__init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls =","_walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),","walls): assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape +","1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls)","not implemented in this dimension') return f / dx ** r.shape[1] def _laplace(field,","dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2:","_walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert","a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0","field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace,","return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L,","div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim","1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx)","dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise","grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif","return make_repr_str(self, fs) # Note, inheritance order matters to get walled grad &","dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return","dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field,","raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds, dx):","grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim ==","implemented in this dimension') return f / dx ** r.shape[1] def _laplace(field, dx):","print_function, division import numpy as np from ciabatta.meta import make_repr_str from fealty import","order matters to get walled grad & laplacian call # (see diamond problem","> 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field,","** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L),","def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L),","3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not implemented in","laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',","= walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self):","r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def","dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L',","field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls)","== inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i,","diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self):","== 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i,","dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim","elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field,","return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim ==","fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0',","self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return","fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return","in this dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0","dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field,","0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype)","Exception(\"Walled Grad_i not implemented in this dimension\") return grad_i def _walled_laplace(field, dx, walls):","raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt","return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self,","[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return","= np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim ==","== 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds,","if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field,","grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls)","this dimension\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert","self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r,","> 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape,","self.L = L self.dim = dim @property def L_half(self): return self.L / 2.0","wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim,","this dimension\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape","2 M = int(round(L / dx)) dx = L / M inds =","walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim ==","Exception(\"Walled grad not implemented in this dimension\") return grad def _walled_grad_i(field, inds, dx,","__init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D","of all kinds. \"\"\" from __future__ import print_function, division import numpy as np","('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note,","/ dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace =","self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def","= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self,","def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)","('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order","dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field)","self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return","inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not implemented in this dimension\")","L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,),","laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim ==","self.dim = dim @property def L_half(self): return self.L / 2.0 @property def A(self):","field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx)","self.D = D self.dt = dt if self.D > self.dx ** 2 /","dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field,","2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls)","dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs","M = int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r,","dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',","dx)) dx = L / M inds = lattice.r_to_i(r, L, dx) f =","field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not","assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim ==","np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self,","Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx,","= dim @property def L_half(self): return self.L / 2.0 @property def A(self): return","np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field,","Exception('Density calc not implemented in this dimension') return f / dx ** r.shape[1]","dx) else: raise Exception('Grad not implemented in this dimension') return grad def _div(field,","__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self,","if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace,","grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self):","dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim ==","dx, walls) else: raise Exception(\"Walled grad not implemented in this dimension\") return grad","inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim ==","def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim","_walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace =","assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,),","import print_function, division import numpy as np from ciabatta.meta import make_repr_str from fealty","relating to fields of all kinds. \"\"\" from __future__ import print_function, division import","dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a *=","dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D > self.dx","assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace,","Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def","_walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs","else: raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds,","fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self,","== 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif","WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim',","dx): assert r.ndim == 2 M = int(round(L / dx)) dx = L","field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace","__init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property","dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim","return self.M ** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self,","class hierarchy relating to fields of all kinds. \"\"\" from __future__ import print_function,","lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs","self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L,","kinds. \"\"\" from __future__ import print_function, division import numpy as np from ciabatta.meta","r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def","2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim","/ 2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs):","laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim","field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian","else: raise Exception(\"Walled grad not implemented in this dimension\") return grad def _walled_grad_i(field,","class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx,","def __init__(self, L, dim): self.L = L self.dim = dim @property def L_half(self):","this dimension\") return grad_i def _grad(field, dx): assert dx > 0.0 grad =","* self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian()","self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim',","self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L',","field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim ==","calc not implemented in this dimension') return f / dx ** r.shape[1] def","if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2:","field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx)","self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx):","* (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2:","dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled","self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt,","> 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field,","np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object):","laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise","* self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D',","r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def","i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim',","== 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx,","grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not","fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L =","grad_i, dx, walls) else: raise Exception(\"Walled Grad_i not implemented in this dimension\") return","fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M =","grad, dx, walls) else: raise Exception(\"Walled grad not implemented in this dimension\") return","[('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self,","iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return","dx) else: raise Exception(\"Grad_i not implemented in this dimension\") return grad_i def _grad(field,","assert r.ndim == 2 M = int(round(L / dx)) dx = L /","field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif","implemented in this dimension') return grad def _div(field, dx): assert dx > 0.0","def dx(self): return self.L / self.M @property def A_i(self): return self.M ** self.dim","laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension') return laplace","Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim,","dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field,","obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r):","field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented in this dimension\") return grad_i","inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim","= int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property def","grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not","problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self,","2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1:","L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls","f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this","return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self,","else: raise Exception(\"Walled Grad_i not implemented in this dimension\") return grad_i def _walled_laplace(field,","dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented","dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self):","dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx,","not implemented in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape","('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs)","import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L,","inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim","dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert dx >","diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def","_grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if","L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed","density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i):","div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4:","2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls)","\"\"\" A class hierarchy relating to fields of all kinds. \"\"\" from __future__","0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif","# Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a,","all kinds. \"\"\" from __future__ import print_function, division import numpy as np from","dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim ==","1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i,","(M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds,","dim): self.L = L self.dim = dim @property def L_half(self): return self.L /","zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def","self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() *","walled grad & laplacian call # (see diamond problem on wikipedia and how","return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self):","self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self,","Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert","return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx >","# (see diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar,","self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx,","raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx, walls):","a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt if","fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L,","def L_half(self): return self.L / 2.0 @property def A(self): return self.L ** self.dim","not implemented in this dimension') return grad def _div(field, dx): assert dx >","raise Exception(\"Walled Grad_i not implemented in this dimension\") return grad_i def _walled_laplace(field, dx,","make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M = int(round(L","return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def","dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape","dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt","assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if","dimension\") return grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape","2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc","dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L / self.M","self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs =","L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt,","('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim,","Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0)","self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L',","walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3:","0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad,","('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim,","grad not implemented in this dimension\") return grad def _walled_grad_i(field, inds, dx, walls):","dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float)","def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx)","M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if","+ (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim ==","_grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim","(see diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing):","class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a","== 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad not implemented in","L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds,","field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise","make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim):","= int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r, L,","__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt),","/ dx)) dx = L / M inds = lattice.r_to_i(r, L, dx) f","dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt","Space(object): def __init__(self, L, dim): self.L = L self.dim = dim @property def","('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls,","Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def","f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f)","self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i,","self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L),","= np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def","('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim,","**kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs)","* self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',","def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),","this dimension') return grad def _div(field, dx): assert dx > 0.0 div =","walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1]","dt if self.D > self.dx ** 2 / (2.0 * self.dim * self.dt):","== walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1:","0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad,","4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension') return","field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace,","grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls)","from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def","def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)]","@property def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L,","self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self,","self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def","f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim","inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim","field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds,","return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self,","L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),","('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get walled","Note, inheritance order matters to get walled grad & laplacian call # (see","hierarchy relating to fields of all kinds. \"\"\" from __future__ import print_function, division","('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D,","import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L","== 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx)","field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence","assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2 assert","laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim","elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented","self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim),","2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise","1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim","grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise","def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim","def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L,","L_half(self): return self.L / 2.0 @property def A(self): return self.L ** self.dim def","implemented in this dimension\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape","grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0","return grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape +","Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a =","dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),","np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r),","/ M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int)","== 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented in this dimension\")","== inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i,","return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0):","dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1]","== 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else:","L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D =","def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space):","dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D","raise Exception('Grad not implemented in this dimension') return grad def _div(field, dx): assert","else: raise Exception('Density calc not implemented in this dimension') return f / dx","field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim =","field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i,","+= self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim',","dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self):","self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx)","2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else:","walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\"Walled grad not implemented in this dimension\")","this dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert dx","laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),","self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim ==","def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def","in this dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert","dx, a_0=a_0) self.D = D self.dt = dt if self.D > self.dx **","('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self,","field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this","field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not","from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L","if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div,","else: raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx,","grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim","(field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim ==","dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) *","WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L,","elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in","laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2","('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2","== 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension')","(self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return","self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D *","= [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L,","== 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif","np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim ==","self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M","self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class","not implemented in this dimension\") return grad_i def _grad(field, dx): assert dx >","@property def dx(self): return self.L / self.M @property def A_i(self): return self.M **","dx)) @property def dx(self): return self.L / self.M @property def A_i(self): return self.M","== 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density","assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field,","dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx","L self.dim = dim @property def L_half(self): return self.L / 2.0 @property def","self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D),","a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx)","inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx,","dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls #","D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs =","def density(r, L, dx): assert r.ndim == 2 M = int(round(L / dx))","def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self):","div, dx) else: raise Exception('Divergence not implemented in this dimension') return div def","self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs =","/ dx)) @property def dx(self): return self.L / self.M @property def A_i(self): return","raise Exception(\"Grad_i not implemented in this dimension\") return grad_i def _grad(field, dx): assert","fs) def density(r, L, dx): assert r.ndim == 2 M = int(round(L /","dx = L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1]","def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx ** self.dim","('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return","laplacian call # (see diamond problem on wikipedia and how python handles it)","def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs)","make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self,","f) else: raise Exception('Density calc not implemented in this dimension') return f /","dimension\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert","walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx,","('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self,","dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f)","elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace,","np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim","not implemented in this dimension\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape","lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim","== 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this","r.ndim == 2 M = int(round(L / dx)) dx = L / M","laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace def","self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance","def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0)","self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self,","self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r):","('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar):","inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i","3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension') return","grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if","self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L),","= np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif","self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx,","return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self):","dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx,","__init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim *","grad_i, dx) else: raise Exception(\"Grad_i not implemented in this dimension\") return grad_i def","\"\"\" from __future__ import print_function, division import numpy as np from ciabatta.meta import","self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert","r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim","field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented in this","field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim ==","implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert dx >","dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim ==","3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\"Grad_i not implemented in this dimension\") return","field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds,","np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2:","1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls)","return grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype)","elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div,","= dt if self.D > self.dx ** 2 / (2.0 * self.dim *","laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3:","inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls)","ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self,","== 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx,","L, dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D >","__init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D,","@property def L_half(self): return self.L / 2.0 @property def A(self): return self.L **","matters to get walled grad & laplacian call # (see diamond problem on","elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else:","Exception(\"Grad_i not implemented in this dimension\") return grad_i def _grad(field, dx): assert dx","how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls,","field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field,","make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim,","dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field,","def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt',","as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class","field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this","density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx)","in this dimension\") return grad_i def _grad(field, dx): assert dx > 0.0 grad","walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else:","field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim ==","('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx):","L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def","@property def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx **","walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return","to fields of all kinds. \"\"\" from __future__ import print_function, division import numpy","+ (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim","** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r): return","elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3:","1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim","/ self.M @property def A_i(self): return self.M ** self.dim @property def dA(self): return","inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx)","np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim","self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0):","= lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim ==","return self.L / self.M @property def A_i(self): return self.M ** self.dim @property def","field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field,","== 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else:","on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L,","self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L,","self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return","walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\"Walled","def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx,","if self.D > self.dx ** 2 / (2.0 * self.dim * self.dt): raise","self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get","fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx)","D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim,","_walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self):"],"string":"[\n \"def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad\",\n \"Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D\",\n \"dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0)\",\n \"make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L,\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class\",\n \"assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field,\",\n \"Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx, walls): assert\",\n \"self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L,\",\n \"** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def\",\n \"elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in\",\n \"if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field,\",\n \"field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx)\",\n \"dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a,\",\n \"== 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif\",\n \"# Note, inheritance order matters to get walled grad & laplacian call #\",\n \"__init__(self, L, dim): self.L = L self.dim = dim @property def L_half(self): return\",\n \"self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self,\",\n \"a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls)\",\n \"dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3:\",\n \"self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L,\",\n \"from __future__ import print_function, division import numpy as np from ciabatta.meta import make_repr_str\",\n \"_laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1:\",\n \"field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension') return grad\",\n \"A_i(self): return self.M ** self.dim @property def dA(self): return self.dx ** self.dim def\",\n \"if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad,\",\n \"int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property def A_i(self):\",\n \"class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim,\",\n \"('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self,\",\n \"self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def\",\n \"self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r,\",\n \"dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx)\",\n \"Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx,\",\n \"self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get walled grad\",\n \"grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx >\",\n \"def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx,\",\n \"walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2\",\n \"('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim\",\n \"python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D,\",\n \"_div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim ==\",\n \"return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim)\",\n \"walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else:\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def\",\n \"Grad_i not implemented in this dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert\",\n \"2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i,\",\n \"self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx)\",\n \"def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def\",\n \"class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L\",\n \"(field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2:\",\n \"walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim = dim\",\n \"self.M = int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property\",\n \"a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls,\",\n \"def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0\",\n \"div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0\",\n \"grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx)\",\n \"grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls)\",\n \"== 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim ==\",\n \"* (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r):\",\n \"* self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D\",\n \"grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,),\",\n \"self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant')\",\n \"field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad not implemented\",\n \"def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace\",\n \"L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L /\",\n \"0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim\",\n \"elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in\",\n \"dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim\",\n \"= L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] *\",\n \"dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented\",\n \"field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds,\",\n \"make_repr_str(self, fs) # Note, inheritance order matters to get walled grad & laplacian\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)]\",\n \"walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L,\",\n \"= np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim\",\n \"= np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2:\",\n \"self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) #\",\n \"* a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r),\",\n \"this dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert\",\n \"i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),\",\n \"self.walls = walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def\",\n \"f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif\",\n \"> self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion\",\n \"self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)]\",\n \"L, dx): assert r.ndim == 2 M = int(round(L / dx)) dx =\",\n \"[('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim,\",\n \"& laplacian call # (see diamond problem on wikipedia and how python handles\",\n \"== 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds,\",\n \"def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls\",\n \"field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad,\",\n \"def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim ==\",\n \"return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx >\",\n \"= D self.dt = dt if self.D > self.dx ** 2 / (2.0\",\n \"r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx)\",\n \"elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in\",\n \"3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension')\",\n \"return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx\",\n \"import numpy as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics,\",\n \"dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make\",\n \"dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return grad_i\",\n \"else: raise Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i def _grad(field, dx):\",\n \"dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented\",\n \"self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L',\",\n \"raise Exception('Density calc not implemented in this dimension') return f / dx **\",\n \"where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self,\",\n \"1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3:\",\n \"f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented\",\n \"walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim\",\n \"density(r, L, dx): assert r.ndim == 2 M = int(round(L / dx)) dx\",\n \"L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,),\",\n \"dim @property def L_half(self): return self.L / 2.0 @property def A(self): return self.L\",\n \"self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a,\",\n \"dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1:\",\n \"Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L\",\n \"dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def\",\n \"dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self,\",\n \"self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return\",\n \"field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in\",\n \"f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise\",\n \"0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif\",\n \"a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued\",\n \"self.M ** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r):\",\n \"in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape\",\n \"def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx,\",\n \"field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension') return div\",\n \"else: raise Exception('Grad not implemented in this dimension') return grad def _div(field, dx):\",\n \"walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3:\",\n \"elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad not\",\n \"self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L,\",\n \"self.L / self.M @property def A_i(self): return self.M ** self.dim @property def dA(self):\",\n \"('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs)\",\n \"walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field,\",\n \"return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),\",\n \"dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f)\",\n \"return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M =\",\n \"a_0=a_0) self.D = D self.dt = dt if self.D > self.dx ** 2\",\n \"_grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L',\",\n \"fields of all kinds. \\\"\\\"\\\" from __future__ import print_function, division import numpy as\",\n \"== 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx,\",\n \"implemented in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape ==\",\n \"== 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension')\",\n \"3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad not implemented in this\",\n \"return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs =\",\n \"[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs)\",\n \"def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs =\",\n \"walls): assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if\",\n \"in this dimension') return grad def _div(field, dx): assert dx > 0.0 div\",\n \"L, dim): self.L = L self.dim = dim @property def L_half(self): return self.L\",\n \"/ (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a\",\n \"grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif\",\n \"assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim\",\n \"= np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim\",\n \"np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3:\",\n \"self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L),\",\n \"= L self.dim = dim @property def L_half(self): return self.L / 2.0 @property\",\n \"> 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls)\",\n \"__future__ import print_function, division import numpy as np from ciabatta.meta import make_repr_str from\",\n \"self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),\",\n \"iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L',\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D),\",\n \"class Space(object): def __init__(self, L, dim): self.L = L self.dim = dim @property\",\n \"assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i\",\n \"walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension') return\",\n \"return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs =\",\n \"dx(self): return self.L / self.M @property def A_i(self): return self.M ** self.dim @property\",\n \"elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad,\",\n \"inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls)\",\n \"laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not\",\n \"self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)]\",\n \"dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field)\",\n \"numpy as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics\",\n \"= [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def\",\n \"field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx)\",\n \"Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where\",\n \"lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1:\",\n \"dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx\",\n \"('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0):\",\n \"Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L /\",\n \"dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian\",\n \"walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif\",\n \"dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim ==\",\n \"** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if\",\n \"assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field,\",\n \"= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)]\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return\",\n \"fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field):\",\n \"class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self,\",\n \"field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension') return f\",\n \"*= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a,\",\n \"self.dt = dt if self.D > self.dx ** 2 / (2.0 * self.dim\",\n \"in this dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape ==\",\n \"if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif\",\n \"return f / dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0\",\n \"self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx)\",\n \"def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert\",\n \"== 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension')\",\n \"dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2:\",\n \"a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L),\",\n \"a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls),\",\n \"inheritance order matters to get walled grad & laplacian call # (see diamond\",\n \"grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else:\",\n \"dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field,\",\n \"dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented\",\n \"[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar):\",\n \"self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls',\",\n \"walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls',\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0',\",\n \"L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self):\",\n \"fs) # Note, inheritance order matters to get walled grad & laplacian call\",\n \"= np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim ==\",\n \"raise Exception(\\\"Walled grad not implemented in this dimension\\\") return grad def _walled_grad_i(field, inds,\",\n \"make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M\",\n \"self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls),\",\n \"field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx)\",\n \"to get walled grad & laplacian call # (see diamond problem on wikipedia\",\n \"= np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif\",\n \"elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field,\",\n \"r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs\",\n \"walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented in this\",\n \"*args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self,\",\n \"return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',\",\n \"== 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim ==\",\n \"grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3:\",\n \"it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0):\",\n \"field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim ==\",\n \"= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class\",\n \"elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in\",\n \"field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim\",\n \"not implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert dx\",\n \"== 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else:\",\n \"walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif\",\n \"dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return\",\n \"2 / (2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self):\",\n \"elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field,\",\n \"div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not\",\n \"Exception('Grad not implemented in this dimension') return grad def _div(field, dx): assert dx\",\n \"get walled grad & laplacian call # (see diamond problem on wikipedia and\",\n \"= np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls)\",\n \"np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim ==\",\n \"= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt),\",\n \"3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise\",\n \"_walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad =\",\n \"walls) else: raise Exception(\\\"Walled grad not implemented in this dimension\\\") return grad def\",\n \"in this dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape\",\n \"self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx,\",\n \"constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs\",\n \"== 2 M = int(round(L / dx)) dx = L / M inds\",\n \"int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r, L, dx)\",\n \"(2.0 * self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a +=\",\n \"L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def\",\n \"lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)]\",\n \"walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx,\",\n \"== 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx,\",\n \"A class hierarchy relating to fields of all kinds. \\\"\\\"\\\" from __future__ import\",\n \"return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return\",\n \"and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx,\",\n \"D self.dt = dt if self.D > self.dx ** 2 / (2.0 *\",\n \"dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a\",\n \"3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension') return\",\n \"walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not\",\n \"inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim\",\n \"== 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this dimension')\",\n \"if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field,\",\n \"not implemented in this dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls): assert\",\n \"fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim,\",\n \"grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self):\",\n \"3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return\",\n \"walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls # Make field\",\n \"dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div,\",\n \"== walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if\",\n \"division import numpy as np from ciabatta.meta import make_repr_str from fealty import lattice,\",\n \"('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to\",\n \"2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise\",\n \"('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r,\",\n \"call # (see diamond problem on wikipedia and how python handles it) class\",\n \"field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad\",\n \"== walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim ==\",\n \"dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a,\",\n \"this dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert\",\n \"elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i\",\n \"A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs\",\n \"grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx,\",\n \"_laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0',\",\n \"fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt',\",\n \"return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',\",\n \"** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return\",\n \"dimension') return grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1],\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D',\",\n \"def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"walls) else: raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return grad_i def\",\n \"> 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif\",\n \"handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt,\",\n \"return self.L / 2.0 @property def A(self): return self.L ** self.dim def iterate(self,\",\n \"2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass\",\n \"[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0',\",\n \"implemented in this dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape ==\",\n \"> 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx)\",\n \"fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs)\",\n \"walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad\",\n \"== 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented\",\n \"dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2:\",\n \"field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this\",\n \"def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L,\",\n \"self.D > self.dx ** 2 / (2.0 * self.dim * self.dt): raise Exception('Unstable\",\n \"def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx))\",\n \"assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace,\",\n \"dx) else: raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field,\",\n \"@property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def\",\n \"dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i =\",\n \"field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)\",\n \"np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif\",\n \"_grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a,\",\n \"self.M @property def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx\",\n \"grad & laplacian call # (see diamond problem on wikipedia and how python\",\n \"grad, dx) else: raise Exception('Grad not implemented in this dimension') return grad def\",\n \"field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad,\",\n \"self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters\",\n \"f / dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace\",\n \"pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class\",\n \"WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0)\",\n \"implemented in this dimension\\\") return grad_i def _grad(field, dx): assert dx > 0.0\",\n \"field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds,\",\n \"dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field,\",\n \"D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt =\",\n \"self.L / 2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args,\",\n \"__init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls =\",\n \"_walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),\",\n \"walls): assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape +\",\n \"1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls)\",\n \"not implemented in this dimension') return f / dx ** r.shape[1] def _laplace(field,\",\n \"dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim == 2:\",\n \"_walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert\",\n \"a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0\",\n \"field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace,\",\n \"return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L,\",\n \"div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim\",\n \"1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx)\",\n \"dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise\",\n \"grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif\",\n \"return make_repr_str(self, fs) # Note, inheritance order matters to get walled grad &\",\n \"dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self): return\",\n \"dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field,\",\n \"raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds, dx):\",\n \"grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim ==\",\n \"implemented in this dimension') return f / dx ** r.shape[1] def _laplace(field, dx):\",\n \"print_function, division import numpy as np from ciabatta.meta import make_repr_str from fealty import\",\n \"order matters to get walled grad & laplacian call # (see diamond problem\",\n \"> 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field,\",\n \"** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L),\",\n \"def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L),\",\n \"3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented in\",\n \"laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',\",\n \"= walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self):\",\n \"r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def\",\n \"dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L',\",\n \"field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls)\",\n \"== inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i,\",\n \"diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def __repr__(self):\",\n \"== 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i,\",\n \"dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim\",\n \"elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field,\",\n \"return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim ==\",\n \"fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0',\",\n \"self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return\",\n \"fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return\",\n \"in this dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0\",\n \"dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field,\",\n \"0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype)\",\n \"Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return grad_i def _walled_laplace(field, dx, walls):\",\n \"raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt\",\n \"return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self,\",\n \"[('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return\",\n \"= np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim ==\",\n \"== 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds,\",\n \"if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field,\",\n \"grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls)\",\n \"this dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert\",\n \"self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r,\",\n \"> 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape,\",\n \"self.L = L self.dim = dim @property def L_half(self): return self.L / 2.0\",\n \"wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim,\",\n \"this dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape\",\n \"2 M = int(round(L / dx)) dx = L / M inds =\",\n \"walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim ==\",\n \"Exception(\\\"Walled grad not implemented in this dimension\\\") return grad def _walled_grad_i(field, inds, dx,\",\n \"__init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D\",\n \"of all kinds. \\\"\\\"\\\" from __future__ import print_function, division import numpy as np\",\n \"('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note,\",\n \"/ dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace =\",\n \"self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def\",\n \"= [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self,\",\n \"def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)\",\n \"('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order\",\n \"dx ** r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field)\",\n \"self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return\",\n \"inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\")\",\n \"L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,),\",\n \"laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim ==\",\n \"self.dim = dim @property def L_half(self): return self.L / 2.0 @property def A(self):\",\n \"field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx)\",\n \"self.D = D self.dt = dt if self.D > self.dx ** 2 /\",\n \"dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field,\",\n \"2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls)\",\n \"dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs\",\n \"M = int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r,\",\n \"dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',\",\n \"dx)) dx = L / M inds = lattice.r_to_i(r, L, dx) f =\",\n \"field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not\",\n \"assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim ==\",\n \"np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self,\",\n \"Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx,\",\n \"= dim @property def L_half(self): return self.L / 2.0 @property def A(self): return\",\n \"np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field,\",\n \"Exception('Density calc not implemented in this dimension') return f / dx ** r.shape[1]\",\n \"dx) else: raise Exception('Grad not implemented in this dimension') return grad def _div(field,\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self,\",\n \"if field.ndim == 1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace,\",\n \"grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self):\",\n \"dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim ==\",\n \"dx, walls) else: raise Exception(\\\"Walled grad not implemented in this dimension\\\") return grad\",\n \"inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim ==\",\n \"def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim\",\n \"_walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 laplace =\",\n \"assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,),\",\n \"import print_function, division import numpy as np from ciabatta.meta import make_repr_str from fealty\",\n \"relating to fields of all kinds. \\\"\\\"\\\" from __future__ import print_function, division import\",\n \"dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed self.a *=\",\n \"dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D > self.dx\",\n \"assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: field_numerics.laplace_1d(field, laplace,\",\n \"Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def\",\n \"_walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs\",\n \"else: raise Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds,\",\n \"fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self,\",\n \"== 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif\",\n \"WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"dx): assert r.ndim == 2 M = int(round(L / dx)) dx = L\",\n \"field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace\",\n \"__init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property\",\n \"dimension') return laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim\",\n \"return self.M ** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self,\",\n \"class hierarchy relating to fields of all kinds. \\\"\\\"\\\" from __future__ import print_function,\",\n \"lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs\",\n \"self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L,\",\n \"kinds. \\\"\\\"\\\" from __future__ import print_function, division import numpy as np from ciabatta.meta\",\n \"r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def\",\n \"2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim\",\n \"/ 2.0 @property def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs):\",\n \"laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim\",\n \"field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian\",\n \"else: raise Exception(\\\"Walled grad not implemented in this dimension\\\") return grad def _walled_grad_i(field,\",\n \"class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx,\",\n \"def __init__(self, L, dim): self.L = L self.dim = dim @property def L_half(self):\",\n \"this dimension\\\") return grad_i def _grad(field, dx): assert dx > 0.0 grad =\",\n \"* self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian()\",\n \"self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L',\",\n \"field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim ==\",\n \"calc not implemented in this dimension') return f / dx ** r.shape[1] def\",\n \"if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2:\",\n \"field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx)\",\n \"self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L, dim, dx):\",\n \"* (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2:\",\n \"dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled\",\n \"self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt,\",\n \"> 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field,\",\n \"np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object):\",\n \"laplace, dx, walls) elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise\",\n \"* self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D',\",\n \"r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def\",\n \"i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"== 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx,\",\n \"grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not\",\n \"fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L =\",\n \"grad_i, dx, walls) else: raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return\",\n \"fs) class Field(Space): def __init__(self, L, dim, dx): Space.__init__(self, L, dim) self.M =\",\n \"grad, dx, walls) else: raise Exception(\\\"Walled grad not implemented in this dimension\\\") return\",\n \"[('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self,\",\n \"iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return\",\n \"dx) else: raise Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i def _grad(field,\",\n \"assert r.ndim == 2 M = int(round(L / dx)) dx = L /\",\n \"field_numerics.grad_i_1d(field, inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif\",\n \"implemented in this dimension') return grad def _div(field, dx): assert dx > 0.0\",\n \"def dx(self): return self.L / self.M @property def A_i(self): return self.M ** self.dim\",\n \"laplace, dx, walls) else: raise Exception('Laplacian not implemented in this dimension') return laplace\",\n \"Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim,\",\n \"dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field,\",\n \"obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r):\",\n \"field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i\",\n \"inds, dx, walls): assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim\",\n \"= int(round(self.L / dx)) @property def dx(self): return self.L / self.M @property def\",\n \"grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not\",\n \"problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self,\",\n \"2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1:\",\n \"L, dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls\",\n \"f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not implemented in this\",\n \"return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self,\",\n \"else: raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return grad_i def _walled_laplace(field,\",\n \"dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented\",\n \"dim, dx): Space.__init__(self, L, dim) self.M = int(round(self.L / dx)) @property def dx(self):\",\n \"dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx,\",\n \"not implemented in this dimension') return div def _walled_grad(field, dx, walls): assert field.shape\",\n \"('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs)\",\n \"import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L,\",\n \"inds, grad_i, dx) elif field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim\",\n \"dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert dx >\",\n \"diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def\",\n \"_grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if\",\n \"L, dim, dx, a_0=a_0) self.walls = walls # Make field zero-valued where obstructed\",\n \"density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i):\",\n \"div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4:\",\n \"2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls)\",\n \"\\\"\\\"\\\" A class hierarchy relating to fields of all kinds. \\\"\\\"\\\" from __future__\",\n \"0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif\",\n \"# Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a,\",\n \"all kinds. \\\"\\\"\\\" from __future__ import print_function, division import numpy as np from\",\n \"dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim ==\",\n \"1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i,\",\n \"(M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds,\",\n \"dim): self.L = L self.dim = dim @property def L_half(self): return self.L /\",\n \"zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def\",\n \"self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() *\",\n \"walled grad & laplacian call # (see diamond problem on wikipedia and how\",\n \"return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self):\",\n \"self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self,\",\n \"Exception('Laplacian not implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert\",\n \"return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx >\",\n \"# (see diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar,\",\n \"self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx,\",\n \"raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx, walls):\",\n \"a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt if\",\n \"fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L,\",\n \"def L_half(self): return self.L / 2.0 @property def A(self): return self.L ** self.dim\",\n \"not implemented in this dimension') return grad def _div(field, dx): assert dx >\",\n \"raise Exception(\\\"Walled Grad_i not implemented in this dimension\\\") return grad_i def _walled_laplace(field, dx,\",\n \"make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M = int(round(L\",\n \"return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def\",\n \"dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad = np.empty(field.shape\",\n \"dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt\",\n \"assert inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if\",\n \"dimension\\\") return grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape\",\n \"2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc\",\n \"dim) self.M = int(round(self.L / dx)) @property def dx(self): return self.L / self.M\",\n \"self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs =\",\n \"L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt,\",\n \"('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim,\",\n \"Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0)\",\n \"self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs = [('L',\",\n \"walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3:\",\n \"0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad,\",\n \"('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim,\",\n \"grad not implemented in this dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls):\",\n \"dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float)\",\n \"def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx)\",\n \"M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if\",\n \"+ (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim ==\",\n \"_grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim\",\n \"(see diamond problem on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing):\",\n \"class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a\",\n \"== 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad not implemented in\",\n \"L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds,\",\n \"field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise\",\n \"make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim):\",\n \"= int(round(L / dx)) dx = L / M inds = lattice.r_to_i(r, L,\",\n \"__repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt),\",\n \"/ dx)) dx = L / M inds = lattice.r_to_i(r, L, dx) f\",\n \"dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt\",\n \"Space(object): def __init__(self, L, dim): self.L = L self.dim = dim @property def\",\n \"('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls,\",\n \"Exception('Unstable diffusion constant') def iterate(self): self.a += self.D * self.laplacian() * self.dt def\",\n \"f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f)\",\n \"self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i,\",\n \"self.a += self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L),\",\n \"= np.ones(self.dim * (self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def\",\n \"('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim,\",\n \"**kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs)\",\n \"* self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx',\",\n \"def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),\",\n \"this dimension') return grad def _div(field, dx): assert dx > 0.0 div =\",\n \"walls.shape assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1]\",\n \"dt if self.D > self.dx ** 2 / (2.0 * self.dim * self.dt):\",\n \"== walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1:\",\n \"0.0 grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad,\",\n \"4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence not implemented in this dimension') return\",\n \"field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace,\",\n \"grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls)\",\n \"from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def\",\n \"def iterate(self, *args, **kwargs): pass def __repr__(self): fs = [('L', self.L), ('dim', self.dim)]\",\n \"@property def dA(self): return self.dx ** self.dim def density_field(self, r): return density(r, self.L,\",\n \"self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self,\",\n \"self.L), ('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def\",\n \"f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim\",\n \"inds.ndim == 2 assert field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim\",\n \"field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds,\",\n \"return self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self,\",\n \"L, dim, dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),\",\n \"('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get walled\",\n \"Note, inheritance order matters to get walled grad & laplacian call # (see\",\n \"hierarchy relating to fields of all kinds. \\\"\\\"\\\" from __future__ import print_function, division\",\n \"('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D,\",\n \"import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L\",\n \"== 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx)\",\n \"field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else: raise Exception('Divergence\",\n \"assert field.shape == walls.shape assert dx > 0.0 assert inds.ndim == 2 assert\",\n \"laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls) elif field.ndim\",\n \"elif field.ndim == 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented\",\n \"self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim),\",\n \"2: field_numerics.grad_2d(field, grad, dx) elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise\",\n \"1: field_numerics.laplace_1d(field, laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim\",\n \"grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise\",\n \"def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim\",\n \"def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L,\",\n \"L_half(self): return self.L / 2.0 @property def A(self): return self.L ** self.dim def\",\n \"implemented in this dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape\",\n \"grad_i def _walled_laplace(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0\",\n \"return grad_i def _grad(field, dx): assert dx > 0.0 grad = np.empty(field.shape +\",\n \"Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a =\",\n \"dx, walls, a_0=a_0) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),\",\n \"np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r),\",\n \"/ M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int)\",\n \"== 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in this dimension\\\")\",\n \"== inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i,\",\n \"return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0):\",\n \"dx): assert dx > 0.0 assert inds.ndim == 2 assert field.ndim == inds.shape[1]\",\n \"== 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div, dx) else:\",\n \"L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D =\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space):\",\n \"dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D\",\n \"raise Exception('Grad not implemented in this dimension') return grad def _div(field, dx): assert\",\n \"else: raise Exception('Density calc not implemented in this dimension') return f / dx\",\n \"field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim =\",\n \"field.ndim == 2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i,\",\n \"+= self.D * self.laplacian() * self.dt def __repr__(self): fs = [('L', self.L), ('dim',\",\n \"dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self):\",\n \"self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx)\",\n \"2: field_numerics.grad_i_2d(field, inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else:\",\n \"walled_field_numerics.grad_3d(field, grad, dx, walls) else: raise Exception(\\\"Walled grad not implemented in this dimension\\\")\",\n \"this dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert dx\",\n \"laplacian(self): return _walled_laplace(self.a, self.dx, self.walls) def __repr__(self): fs = [('L', self.L), ('dim', self.dim),\",\n \"self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim ==\",\n \"def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx) def\",\n \"in this dimension') return f / dx ** r.shape[1] def _laplace(field, dx): assert\",\n \"dx, a_0=a_0) self.D = D self.dt = dt if self.D > self.dx **\",\n \"('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self,\",\n \"field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this\",\n \"field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density calc not\",\n \"from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L\",\n \"if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field, div,\",\n \"else: raise Exception('Divergence not implemented in this dimension') return div def _walled_grad(field, dx,\",\n \"grad, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim\",\n \"(field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim ==\",\n \"dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim * (self.M,), dtype=np.float) *\",\n \"WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L,\",\n \"elif field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in\",\n \"laplace def _grad_i(field, inds, dx): assert dx > 0.0 assert inds.ndim == 2\",\n \"('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2\",\n \"== 3: field_numerics.laplace_3d(field, laplace, dx) else: raise Exception('Laplacian not implemented in this dimension')\",\n \"(self.M,), dtype=np.float) * a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return\",\n \"self.dim * self.dt): raise Exception('Unstable diffusion constant') def iterate(self): self.a += self.D *\",\n \"= [('L', self.L), ('dim', self.dim)] return make_repr_str(self, fs) class Field(Space): def __init__(self, L,\",\n \"== 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif\",\n \"np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim ==\",\n \"self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert r.ndim == 2 M\",\n \"self.dim), ('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class\",\n \"not implemented in this dimension\\\") return grad_i def _grad(field, dx): assert dx >\",\n \"@property def dx(self): return self.L / self.M @property def A_i(self): return self.M **\",\n \"dx)) @property def dx(self): return self.L / self.M @property def A_i(self): return self.M\",\n \"== 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else: raise Exception('Density\",\n \"assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field,\",\n \"dimension') return div def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx\",\n \"L self.dim = dim @property def L_half(self): return self.L / 2.0 @property def\",\n \"self.dt def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D),\",\n \"a_0 def grad(self): return _grad(self.a, self.dx) def grad_i(self, r): return _grad_i(self.a, self.r_to_i(r), self.dx)\",\n \"inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx,\",\n \"dim, dx, walls, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0) self.walls = walls #\",\n \"D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def __repr__(self): fs =\",\n \"def density(r, L, dx): assert r.ndim == 2 M = int(round(L / dx))\",\n \"def A(self): return self.L ** self.dim def iterate(self, *args, **kwargs): pass def __repr__(self):\",\n \"div, dx) else: raise Exception('Divergence not implemented in this dimension') return div def\",\n \"self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self): fs =\",\n \"/ dx)) @property def dx(self): return self.L / self.M @property def A_i(self): return\",\n \"raise Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i def _grad(field, dx): assert\",\n \"fs) def density(r, L, dx): assert r.ndim == 2 M = int(round(L /\",\n \"dx = L / M inds = lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1]\",\n \"def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx ** self.dim\",\n \"('dim', self.dim), ('dx', self.dx), ('walls', self.walls), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return\",\n \"laplacian call # (see diamond problem on wikipedia and how python handles it)\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs)\",\n \"make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self,\",\n \"f) else: raise Exception('Density calc not implemented in this dimension') return f /\",\n \"dimension\\\") return grad def _walled_grad_i(field, inds, dx, walls): assert field.shape == walls.shape assert\",\n \"walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx,\",\n \"('dim', self.dim), ('dx', self.dx), ('a_0', self.a_0)] return make_repr_str(self, fs) class Diffusing(Scalar): def __init__(self,\",\n \"dtype=np.int) if f.ndim == 1: field_numerics.density_1d(inds, f) elif f.ndim == 2: field_numerics.density_2d(inds, f)\",\n \"elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace,\",\n \"np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim\",\n \"not implemented in this dimension\\\") return grad_i def _walled_laplace(field, dx, walls): assert field.shape\",\n \"lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self, L, dim): self.L = L self.dim\",\n \"== 3: walled_field_numerics.laplace_3d(field, laplace, dx, walls) else: raise Exception('Laplacian not implemented in this\",\n \"r.ndim == 2 M = int(round(L / dx)) dx = L / M\",\n \"laplace, dx) else: raise Exception('Laplacian not implemented in this dimension') return laplace def\",\n \"self.dim), ('dx', self.dx), ('walls', self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance\",\n \"def __init__(self, L, dim, dx, D, dt, a_0=0.0): Scalar.__init__(self, L, dim, dx, a_0=a_0)\",\n \"self.dx) def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self,\",\n \"self.dx ** self.dim def density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r):\",\n \"('dx', self.dx), ('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar):\",\n \"inds, grad_i, dx) elif field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i\",\n \"3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this dimension') return\",\n \"grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype) if\",\n \"self.r_to_i(r), self.dx) def laplacian(self): return _laplace(self.a, self.dx) def __repr__(self): fs = [('L', self.L),\",\n \"= np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif\",\n \"self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx,\",\n \"return lattice.r_to_i(r, self.L, self.dx) def i_to_r(self, i): return lattice.i_to_r(i, self.L, self.dx) def __repr__(self):\",\n \"dx > 0.0 laplace = np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx,\",\n \"__init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx) self.a = np.ones(self.dim *\",\n \"grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i def\",\n \"\\\"\\\"\\\" from __future__ import print_function, division import numpy as np from ciabatta.meta import\",\n \"self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx): assert\",\n \"r.shape[1] def _laplace(field, dx): assert dx > 0.0 laplace = np.empty_like(field) if field.ndim\",\n \"field.ndim == 3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in this\",\n \"field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim ==\",\n \"implemented in this dimension') return laplace def _grad_i(field, inds, dx): assert dx >\",\n \"dx, walls) elif field.ndim == 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim ==\",\n \"3: field_numerics.grad_i_3d(field, grad_i, dx) else: raise Exception(\\\"Grad_i not implemented in this dimension\\\") return\",\n \"field.ndim == inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds,\",\n \"np.empty_like(field) if field.ndim == 1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2:\",\n \"1: walled_field_numerics.laplace_1d(field, laplace, dx, walls) elif field.ndim == 2: walled_field_numerics.laplace_2d(field, laplace, dx, walls)\",\n \"return grad def _div(field, dx): assert dx > 0.0 div = np.empty(field.shape[:-1], dtype=field.dtype)\",\n \"elif field.ndim == 3: field_numerics.div_2d(field, div, dx) elif field.ndim == 4: field_numerics.div_3d(field, div,\",\n \"= dt if self.D > self.dx ** 2 / (2.0 * self.dim *\",\n \"laplace, dx) elif field.ndim == 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3:\",\n \"inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls)\",\n \"ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class Space(object): def __init__(self,\",\n \"== 2: walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx,\",\n \"L, dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D >\",\n \"__init__(self, L, dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D,\",\n \"@property def L_half(self): return self.L / 2.0 @property def A(self): return self.L **\",\n \"matters to get walled grad & laplacian call # (see diamond problem on\",\n \"elif f.ndim == 2: field_numerics.density_2d(inds, f) elif f.ndim == 3: field_numerics.density_3d(inds, f) else:\",\n \"Exception(\\\"Grad_i not implemented in this dimension\\\") return grad_i def _grad(field, dx): assert dx\",\n \"how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L, dim, dx, walls,\",\n \"field.ndim == 1: walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls) elif field.ndim == 2: walled_field_numerics.grad_i_2d(field,\",\n \"make_repr_str(self, fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim,\",\n \"dtype=field.dtype) if field.ndim == 2: field_numerics.div_1d(field, div, dx) elif field.ndim == 3: field_numerics.div_2d(field,\",\n \"def __repr__(self): fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx), ('D', self.D), ('dt',\",\n \"as np from ciabatta.meta import make_repr_str from fealty import lattice, field_numerics, walled_field_numerics class\",\n \"field.ndim == 3: field_numerics.grad_3d(field, grad, dx) else: raise Exception('Grad not implemented in this\",\n \"density_field(self, r): return density(r, self.L, self.dx) def r_to_i(self, r): return lattice.r_to_i(r, self.L, self.dx)\",\n \"in this dimension\\\") return grad_i def _grad(field, dx): assert dx > 0.0 grad\",\n \"walled_field_numerics.grad_2d(field, grad, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_3d(field, grad, dx, walls) else:\",\n \"field.shape == walls.shape assert dx > 0.0 laplace = np.empty_like(field) if field.ndim ==\",\n \"('D', self.D), ('dt', self.dt), ('a_0', self.a_0)] return make_repr_str(self, fs) def density(r, L, dx):\",\n \"L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0) def\",\n \"@property def A_i(self): return self.M ** self.dim @property def dA(self): return self.dx **\",\n \"walls # Make field zero-valued where obstructed self.a *= np.logical_not(self.walls) def grad(self): return\",\n \"to fields of all kinds. \\\"\\\"\\\" from __future__ import print_function, division import numpy\",\n \"+ (field.ndim,), dtype=field.dtype) if field.ndim == 1: walled_field_numerics.grad_1d(field, grad, dx, walls) elif field.ndim\",\n \"** self.dim @property def dA(self): return self.dx ** self.dim def density_field(self, r): return\",\n \"elif field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3:\",\n \"1: field_numerics.grad_1d(field, grad, dx) elif field.ndim == 2: field_numerics.grad_2d(field, grad, dx) elif field.ndim\",\n \"/ self.M @property def A_i(self): return self.M ** self.dim @property def dA(self): return\",\n \"inds.shape[1] grad_i = np.empty(inds.shape, dtype=field.dtype) if field.ndim == 1: field_numerics.grad_i_1d(field, inds, grad_i, dx)\",\n \"np.empty(field.shape + (field.ndim,), dtype=field.dtype) if field.ndim == 1: field_numerics.grad_1d(field, grad, dx) elif field.ndim\",\n \"self.a_0)] return make_repr_str(self, fs) class WalledScalar(Scalar): def __init__(self, L, dim, dx, walls, a_0=0.0):\",\n \"= lattice.r_to_i(r, L, dx) f = np.zeros(r.shape[1] * (M,), dtype=np.int) if f.ndim ==\",\n \"return self.L / self.M @property def A_i(self): return self.M ** self.dim @property def\",\n \"field.ndim == 2: walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field,\",\n \"== 2: field_numerics.laplace_2d(field, laplace, dx) elif field.ndim == 3: field_numerics.laplace_3d(field, laplace, dx) else:\",\n \"on wikipedia and how python handles it) class WalledDiffusing(WalledScalar, Diffusing): def __init__(self, L,\",\n \"self.L), ('dim', self.dim), ('dx', self.dx)] return make_repr_str(self, fs) class Scalar(Field): def __init__(self, L,\",\n \"self.a *= np.logical_not(self.walls) def grad(self): return _walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return\",\n \"walls) elif field.ndim == 3: walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls) else: raise Exception(\\\"Walled\",\n \"def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self): return _walled_laplace(self.a, self.dx,\",\n \"if self.D > self.dx ** 2 / (2.0 * self.dim * self.dt): raise\",\n \"self.walls), ('a_0', self.a_0)] return make_repr_str(self, fs) # Note, inheritance order matters to get\",\n \"fs) class Scalar(Field): def __init__(self, L, dim, dx, a_0=0.0): Field.__init__(self, L, dim, dx)\",\n \"D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0) WalledScalar.__init__(self, L, dim,\",\n \"_walled_grad(self.a, self.dx, self.walls) def grad_i(self, r): return _walled_grad_i(self.a, self.r_to_i(r), self.dx, self.walls) def laplacian(self):\"\n]","isConversational":false}}},{"rowIdx":2584,"cells":{"ngram":{"kind":"list like","value":["variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import","\"\"\" ASGI config for example_django project. It exposes the ASGI callable as a","information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application","a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/","``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from","as a module-level variable named ``application``. For more information on this file, see","from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3","project. It exposes the ASGI callable as a module-level variable named ``application``. For","django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3 /opt/code/manage.py","named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os","\"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3","example_django project. It exposes the ASGI callable as a module-level variable named ``application``.","import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3 /opt/code/manage.py \"","get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3 /opt/code/manage.py \" \"loaddata","os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\") os.system(\"/usr/bin/python3 /opt/code/manage.py \" \"loaddata /opt/code/blog/fixtures/default_articles.json\")","module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\"","It exposes the ASGI callable as a module-level variable named ``application``. For more","ASGI config for example_django project. It exposes the ASGI callable as a module-level","For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi","exposes the ASGI callable as a module-level variable named ``application``. For more information","more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import","ASGI callable as a module-level variable named ``application``. For more information on this","for example_django project. It exposes the ASGI callable as a module-level variable named","callable as a module-level variable named ``application``. For more information on this file,","see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application =","this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\")","file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application","on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\",","https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application()","config for example_django project. It exposes the ASGI callable as a module-level variable","the ASGI callable as a module-level variable named ``application``. For more information on","os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py migrate\")","import os from django.core.asgi import get_asgi_application os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"example_django.settings\") application = get_asgi_application() os.system(\"/usr/bin/python3 /opt/code/manage.py"],"string":"[\n \"variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import\",\n \"\\\"\\\"\\\" ASGI config for example_django project. It exposes the ASGI callable as a\",\n \"information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application\",\n \"a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/\",\n \"``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from\",\n \"as a module-level variable named ``application``. For more information on this file, see\",\n \"from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\") os.system(\\\"/usr/bin/python3\",\n \"project. It exposes the ASGI callable as a module-level variable named ``application``. For\",\n \"django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\") os.system(\\\"/usr/bin/python3 /opt/code/manage.py\",\n \"named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os\",\n \"\\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3\",\n \"example_django project. It exposes the ASGI callable as a module-level variable named ``application``.\",\n \"import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\") os.system(\\\"/usr/bin/python3 /opt/code/manage.py \\\"\",\n \"get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\") os.system(\\\"/usr/bin/python3 /opt/code/manage.py \\\" \\\"loaddata\",\n \"os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\") os.system(\\\"/usr/bin/python3 /opt/code/manage.py \\\" \\\"loaddata /opt/code/blog/fixtures/default_articles.json\\\")\",\n \"module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\"\",\n \"It exposes the ASGI callable as a module-level variable named ``application``. For more\",\n \"ASGI config for example_django project. It exposes the ASGI callable as a module-level\",\n \"For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi\",\n \"exposes the ASGI callable as a module-level variable named ``application``. For more information\",\n \"more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import\",\n \"ASGI callable as a module-level variable named ``application``. For more information on this\",\n \"for example_django project. It exposes the ASGI callable as a module-level variable named\",\n \"callable as a module-level variable named ``application``. For more information on this file,\",\n \"see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application =\",\n \"this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\")\",\n \"file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application\",\n \"on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\",\",\n \"https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \\\"\\\"\\\" import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application()\",\n \"config for example_django project. It exposes the ASGI callable as a module-level variable\",\n \"the ASGI callable as a module-level variable named ``application``. For more information on\",\n \"os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py migrate\\\")\",\n \"import os from django.core.asgi import get_asgi_application os.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"example_django.settings\\\") application = get_asgi_application() os.system(\\\"/usr/bin/python3 /opt/code/manage.py\"\n]","isConversational":false}}},{"rowIdx":2585,"cells":{"ngram":{"kind":"list like","value":["function for combinations of discount factor and present bias values. The goal is","function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of","{\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\":","to study the bivariate distribution of the time preference parameters around the combination","function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model","\"value\"] = delta val = crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\":","calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice","in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated Moments criterion function for","criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values","from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from","distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01),","pd.DataFrame \"\"\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ =","# load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"]","\"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution,","with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans,","is to study the bivariate distribution of the time preference parameters around the","__name__ == \"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\",","import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import","_temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments,","bivariate distribution of the time preference parameters around the combination of true parameter","= {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ ==","Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\"","from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from","get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = {","grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model","import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import","get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75,","values. The goal is to study the bivariate distribution of the time preference","\"\"\" import itertools import numpy as np import pandas as pd import respy","src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments","import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import","= pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load","combination of true parameter values. \"\"\" import itertools import numpy as np import","[] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"]","factor and present bias values. The goal is to study the bivariate distribution","params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) #","options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) #","calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors","get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, )","_temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of","grid_beta): \"\"\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters.","present-bias parameter. Returns: pd.DataFrame \"\"\" results = [] for beta, delta in tqdm(itertools.product(grid_beta,","Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution","\"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution,","calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\":","ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution","model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias","load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get empirical","for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] =","src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm","the bivariate distribution of the time preference parameters around the combination of true","Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params,","options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results =","matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities","goal is to study the bivariate distribution of the time preference parameters around","\"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\",","containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of","time preference parameters around the combination of true parameter values. \"\"\" import itertools","study the bivariate distribution of the time preference parameters around the combination of","project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments","calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities","grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results = [] for","index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as","empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix =","Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, }","calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options,","value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict):","= params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val","beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_) result = {\"beta\": beta,","= yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get","from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def","} with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments,",") # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625,","\"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options:","values of Method of Simulated Moments criterion function. Given observed moments and weighting","DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values","params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results","Method of Simulated Moments criterion function. Given observed moments and weighting matrix in","load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] =","params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val =","src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments","val = crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return","\"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_) result =","\"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], )","# get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025),","empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params,","crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing","of true parameter values. \"\"\" import itertools import numpy as np import pandas","true parameter values. \"\"\" import itertools import numpy as np import pandas as","from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta):","= [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"),","Simulated Moments criterion function for combinations of discount factor and present bias values.","= delta val = crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\": val}","weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945,","Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution","results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params params = pd.read_csv(","containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of","of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary","grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns:","import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import","import respy as rp import yaml from bld.project_paths import project_paths_join as ppj from","get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame","\"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load","(dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array):","Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage","= get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), ) results.to_csv(ppj(\"OUT_ANALYSIS\", \"heatmap.csv\"))","_load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities,","rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans","of present-bias parameter. Returns: pd.DataFrame \"\"\" results = [] for beta, delta in","\"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities","of Simulated Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`,","and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated Moments","Method of Simulated Moments criterion function for combinations of discount factor and present","of the time preference parameters around the combination of true parameter values. \"\"\"","import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func,","(np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame","import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import","from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion","the time preference parameters around the combination of true parameter values. \"\"\" import","beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta","\"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments","(np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results = [] for beta,","Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method","src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping","as np import pandas as pd import respy as rp import yaml from","calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution","calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory","of Method of Simulated Moments criterion function for combinations of discount factor and","Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"):","weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated Moments criterion","generate values of Method of Simulated Moments criterion function for combinations of discount","weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate","rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results","from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from","as pd import respy as rp import yaml from bld.project_paths import project_paths_join as","= _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very Restricted\":","moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated","<filename>lectures/extensions/hyperbolic_discounting/replication_code/src/analysis/get_bivariate_distr_data.py \"\"\"Generate values of Method of Simulated Moments criterion function. Given observed moments","from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import","of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results","params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_)","delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params","{ \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\":","preference parameters around the combination of true parameter values. \"\"\" import itertools import","params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float)","= beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_) result = {\"beta\":","options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\"))","the combination of true parameter values. \"\"\" import itertools import numpy as np","calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\":","import numpy as np import pandas as pd import respy as rp import","\"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution,","delta val = crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result)","ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options with","weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very","bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities","# get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix","ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice","\"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get","\"\"\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func","params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta","val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params params =","from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from","grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] =","result = {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__","calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm","import itertools import numpy as np import pandas as pd import respy as","criterion function for combinations of discount factor and present bias values. The goal","calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle","parameter values. \"\"\" import itertools import numpy as np import pandas as pd","as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import","calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution","from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from","# get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments =","Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage","src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping","factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results = []","present bias values. The goal is to study the bivariate distribution of the","if __name__ == \"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\",","calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): #","params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) #","== \"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"],","Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage","crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta","\"msm_estimation\", generate values of Method of Simulated Moments criterion function for combinations of","Simulated Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\",","of Simulated Moments criterion function for combinations of discount factor and present bias","model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount","params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_) result = {\"beta\": beta, \"delta\":","# load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get","(pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array):","\"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params params","np import pandas as pd import respy as rp import yaml from bld.project_paths","criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing","criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) #",") calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities,","_load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta,",") params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options","weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice","yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments","parameters around the combination of true parameter values. \"\"\" import itertools import numpy","Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options.","from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from","beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": #","crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) if","Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very","Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function","tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function.","import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from","= crit_func(params_) result = {\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results)","import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from","parameter. Returns: pd.DataFrame \"\"\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)):","in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"),","around the combination of true parameter values. \"\"\" import itertools import numpy as","calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution(","Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values","results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), )","numpy as np import pandas as pd import respy as rp import yaml","of discount factor and present bias values. The goal is to study the","pandas as pd import respy as rp import yaml from bld.project_paths import project_paths_join","src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments","import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args:","\"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta val = crit_func(params_) result","Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func(","Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution","src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params,","for combinations of discount factor and present bias values. The goal is to","of Method of Simulated Moments criterion function. Given observed moments and weighting matrix","params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"] = delta","params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options =","options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get empirical moments","Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with","= rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution","sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\"))","function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get","values. \"\"\" import itertools import numpy as np import pandas as pd import","src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute","as rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import","empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\",","Moments criterion function for combinations of discount factor and present bias values. The","import pandas as pd import respy as rp import yaml from bld.project_paths import","\"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\",","yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting","_load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\")","\"weighting_matrix_hyp.pickle\") ) calc_moments = { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\":","values of Method of Simulated Moments criterion function for combinations of discount factor","\"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion function weighted_sum_squared_errors =","bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05,","`OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated Moments criterion function for combinations","import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value","pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"),","Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate","src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments","\"\"\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy()","discount factor and present bias values. The goal is to study the bivariate","delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\",","options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter.","distribution of the time preference parameters around the combination of true parameter values.","\"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"weighting_matrix_hyp.pickle\") )","open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments =","Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results = [] for beta, delta","tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\", \"beta\"), \"value\"] = beta params_.loc[(\"delta\", \"delta\"), \"value\"]","results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), ) results.to_csv(ppj(\"OUT_ANALYSIS\",","The goal is to study the bivariate distribution of the time preference parameters","\"delta\"), \"value\"] = delta val = crit_func(params_) result = {\"beta\": beta, \"delta\": delta,","def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args: params (pd.DataFrame):","_replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities","tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \"\"\"Compute value of criterion function. Args: params","combinations of discount factor and present bias values. The goal is to study","and present bias values. The goal is to study the bivariate distribution of","pd import respy as rp import yaml from bld.project_paths import project_paths_join as ppj","return pd.DataFrame.from_dict(results) if __name__ == \"__main__\": # load params params = pd.read_csv( ppj(\"IN_MODEL_SPECS\",","as options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\",","moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle(","bias values. The goal is to study the bivariate distribution of the time","Returns: pd.DataFrame \"\"\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_","Distribution Restricted\": calc_restricted_wage_distribution, \"Wage Distribution Unrestricted\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\"heatmap\"): # get criterion","replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors,","# get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix,","respy as rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments","import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import","\"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities,","= params[\"value\"].astype(float) # load options with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options)","import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import","\"\"\"Generate values of Method of Simulated Moments criterion function. Given observed moments and","observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of","matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of Method of Simulated Moments criterion function","discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \"\"\" results =","results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\"beta\",","pd.read_csv( ppj(\"IN_MODEL_SPECS\", \"params_hyp.csv\"), sep=\";\", index_col=[\"category\", \"name\"], ) params[\"value\"] = params[\"value\"].astype(float) # load options","with open(ppj(\"IN_MODEL_SPECS\", \"options_hyp.yaml\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments","get empirical moments empirical_moments = _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix","= _load_pickle(ppj(\"OUT_ANALYSIS\", \"msm_estimation\", \"moments_hyp.pickle\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\"OUT_ANALYSIS\", \"msm_estimation\",","= { \"Choice Probabilities Very Restricted\": calc_very_restricted_choice_probabilities, \"Choice Probabilities Restricted\": calc_restricted_choice_probabilities, \"Choice Probabilities","parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor.","calc_restricted_choice_probabilities, \"Choice Probabilities Unrestricted\": calc_unrestricted_choice_probabilities, \"Wage Distribution Very Restricted\": calc_very_restricted_wage_distribution, \"Wage Distribution Restricted\":","itertools import numpy as np import pandas as pd import respy as rp","from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from"],"string":"[\n \"function for combinations of discount factor and present bias values. The goal is\",\n \"function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of\",\n \"{\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\":\",\n \"to study the bivariate distribution of the time preference parameters around the combination\",\n \"function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model\",\n \"\\\"value\\\"] = delta val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\":\",\n \"calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice\",\n \"in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated Moments criterion function for\",\n \"criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values\",\n \"from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from\",\n \"distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01),\",\n \"pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ =\",\n \"# load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"]\",\n \"\\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution,\",\n \"with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans,\",\n \"is to study the bivariate distribution of the time preference parameters around the\",\n \"__name__ == \\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\",\",\n \"import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import\",\n \"_temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments,\",\n \"bivariate distribution of the time preference parameters around the combination of true parameter\",\n \"= {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ ==\",\n \"Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\"\",\n \"from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from\",\n \"get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = {\",\n \"grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model\",\n \"import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import\",\n \"get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75,\",\n \"values. The goal is to study the bivariate distribution of the time preference\",\n \"\\\"\\\"\\\" import itertools import numpy as np import pandas as pd import respy\",\n \"src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments\",\n \"import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import\",\n \"= pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load\",\n \"combination of true parameter values. \\\"\\\"\\\" import itertools import numpy as np import\",\n \"[] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"]\",\n \"factor and present bias values. The goal is to study the bivariate distribution\",\n \"params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) #\",\n \"options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) #\",\n \"calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors\",\n \"get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, )\",\n \"_temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of\",\n \"grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters.\",\n \"present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta in tqdm(itertools.product(grid_beta,\",\n \"Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution\",\n \"\\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution,\",\n \"calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\":\",\n \"ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution\",\n \"model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias\",\n \"load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get empirical\",\n \"for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] =\",\n \"src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm\",\n \"the bivariate distribution of the time preference parameters around the combination of true\",\n \"Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params,\",\n \"options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results =\",\n \"matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities\",\n \"goal is to study the bivariate distribution of the time preference parameters around\",\n \"\\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\",\",\n \"containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of\",\n \"time preference parameters around the combination of true parameter values. \\\"\\\"\\\" import itertools\",\n \"study the bivariate distribution of the time preference parameters around the combination of\",\n \"project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments\",\n \"calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities\",\n \"grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for\",\n \"index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as\",\n \"empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix =\",\n \"Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, }\",\n \"calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options,\",\n \"value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict):\",\n \"= params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val\",\n \"beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result = {\\\"beta\\\": beta,\",\n \"= yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get\",\n \"from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def\",\n \"} with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments,\",\n \") # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625,\",\n \"\\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options:\",\n \"values of Method of Simulated Moments criterion function. Given observed moments and weighting\",\n \"DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values\",\n \"params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results\",\n \"Method of Simulated Moments criterion function. Given observed moments and weighting matrix in\",\n \"load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] =\",\n \"params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val =\",\n \"src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments\",\n \"val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return\",\n \"\\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result =\",\n \"\\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], )\",\n \"# get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025),\",\n \"empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params,\",\n \"crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing\",\n \"of true parameter values. \\\"\\\"\\\" import itertools import numpy as np import pandas\",\n \"true parameter values. \\\"\\\"\\\" import itertools import numpy as np import pandas as\",\n \"from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta):\",\n \"= [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"),\",\n \"Simulated Moments criterion function for combinations of discount factor and present bias values.\",\n \"= delta val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val}\",\n \"weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945,\",\n \"Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution\",\n \"results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params params = pd.read_csv(\",\n \"containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of\",\n \"of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary\",\n \"grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns:\",\n \"import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import\",\n \"import respy as rp import yaml from bld.project_paths import project_paths_join as ppj from\",\n \"get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame\",\n \"\\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load\",\n \"(dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array):\",\n \"Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage\",\n \"= get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), ) results.to_csv(ppj(\\\"OUT_ANALYSIS\\\", \\\"heatmap.csv\\\"))\",\n \"_load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities,\",\n \"rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans\",\n \"of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta in\",\n \"\\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities\",\n \"of Simulated Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`,\",\n \"and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated Moments\",\n \"Method of Simulated Moments criterion function for combinations of discount factor and present\",\n \"of the time preference parameters around the combination of true parameter values. \\\"\\\"\\\"\",\n \"import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func,\",\n \"(np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame\",\n \"import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import\",\n \"from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion\",\n \"the time preference parameters around the combination of true parameter values. \\\"\\\"\\\" import\",\n \"beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta\",\n \"\\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments\",\n \"(np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta,\",\n \"Given observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method\",\n \"src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping\",\n \"as np import pandas as pd import respy as rp import yaml from\",\n \"calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution\",\n \"calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory\",\n \"of Method of Simulated Moments criterion function for combinations of discount factor and\",\n \"Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"):\",\n \"weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated Moments criterion\",\n \"generate values of Method of Simulated Moments criterion function for combinations of discount\",\n \"weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate\",\n \"rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results\",\n \"from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from\",\n \"as pd import respy as rp import yaml from bld.project_paths import project_paths_join as\",\n \"= _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very Restricted\\\":\",\n \"moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated\",\n \"<filename>lectures/extensions/hyperbolic_discounting/replication_code/src/analysis/get_bivariate_distr_data.py \\\"\\\"\\\"Generate values of Method of Simulated Moments criterion function. Given observed moments\",\n \"from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import\",\n \"of discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results\",\n \"params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_)\",\n \"delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params\",\n \"{ \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\":\",\n \"preference parameters around the combination of true parameter values. \\\"\\\"\\\" import itertools import\",\n \"params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float)\",\n \"= beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result = {\\\"beta\\\":\",\n \"options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\"))\",\n \"the combination of true parameter values. \\\"\\\"\\\" import itertools import numpy as np\",\n \"calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\":\",\n \"import numpy as np import pandas as pd import respy as rp import\",\n \"\\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution,\",\n \"delta val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result)\",\n \"ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with\",\n \"weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very\",\n \"bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities\",\n \"# get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix\",\n \"ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice\",\n \"\\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get\",\n \"\\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func\",\n \"params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta\",\n \"val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params params =\",\n \"from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from\",\n \"grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] =\",\n \"result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__\",\n \"calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm\",\n \"import itertools import numpy as np import pandas as pd import respy as\",\n \"criterion function for combinations of discount factor and present bias values. The goal\",\n \"calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle\",\n \"parameter values. \\\"\\\"\\\" import itertools import numpy as np import pandas as pd\",\n \"as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import\",\n \"calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution\",\n \"from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from\",\n \"# get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments =\",\n \"Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage\",\n \"src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping\",\n \"factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = []\",\n \"present bias values. The goal is to study the bivariate distribution of the\",\n \"if __name__ == \\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\",\",\n \"calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): #\",\n \"params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) #\",\n \"== \\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"],\",\n \"Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage\",\n \"crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta\",\n \"\\\"msm_estimation\\\", generate values of Method of Simulated Moments criterion function for combinations of\",\n \"Simulated Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\",\",\n \"of Simulated Moments criterion function for combinations of discount factor and present bias\",\n \"model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount\",\n \"params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\":\",\n \"# load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get\",\n \"(pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array):\",\n \"\\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params params\",\n \"np import pandas as pd import respy as rp import yaml from bld.project_paths\",\n \"criterion function. Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing\",\n \"criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) #\",\n \") calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities,\",\n \"_load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta,\",\n \") params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options\",\n \"weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice\",\n \"yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments\",\n \"parameters around the combination of true parameter values. \\\"\\\"\\\" import itertools import numpy\",\n \"Args: params (pd.DataFrame): DataFrame containing model parameters. crit_func (dict): Dictionary containing model options.\",\n \"from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from\",\n \"beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": #\",\n \"crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results) if\",\n \"Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very\",\n \"Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function\",\n \"tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function.\",\n \"import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import _replace_nans from\",\n \"= crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta, \\\"val\\\": val} results.append(result) return pd.DataFrame.from_dict(results)\",\n \"import project_paths_join as ppj from src.library.compute_moments import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from\",\n \"parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)):\",\n \"in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"),\",\n \"around the combination of true parameter values. \\\"\\\"\\\" import itertools import numpy as\",\n \"calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution(\",\n \"Dictionary containing model options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values\",\n \"results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), )\",\n \"numpy as np import pandas as pd import respy as rp import yaml\",\n \"of discount factor and present bias values. The goal is to study the\",\n \"pandas as pd import respy as rp import yaml from bld.project_paths import project_paths_join\",\n \"src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments\",\n \"import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args:\",\n \"\\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result\",\n \"Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func(\",\n \"Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution\",\n \"src.library.housekeeping import _load_pickle from src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params,\",\n \"for combinations of discount factor and present bias values. The goal is to\",\n \"of Method of Simulated Moments criterion function. Given observed moments and weighting matrix\",\n \"params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"] = delta\",\n \"params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options =\",\n \"options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get empirical moments\",\n \"Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with\",\n \"= rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution\",\n \"sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\"))\",\n \"function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get\",\n \"values. \\\"\\\"\\\" import itertools import numpy as np import pandas as pd import\",\n \"src.library.housekeeping import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute\",\n \"as rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments import\",\n \"empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\",\",\n \"Moments criterion function for combinations of discount factor and present bias values. The\",\n \"import pandas as pd import respy as rp import yaml from bld.project_paths import\",\n \"\\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\",\",\n \"yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting\",\n \"_load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\")\",\n \"\\\"weighting_matrix_hyp.pickle\\\") ) calc_moments = { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\":\",\n \"values of Method of Simulated Moments criterion function for combinations of discount factor\",\n \"\\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion function weighted_sum_squared_errors =\",\n \"bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05,\",\n \"`OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated Moments criterion function for combinations\",\n \"import _temporary_working_directory from tqdm import tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value\",\n \"pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"),\",\n \"Moments criterion function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate\",\n \"src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities from src.library.compute_moments import calc_unrestricted_wage_distribution from src.library.compute_moments\",\n \"\\\"\\\"\\\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy()\",\n \"discount factor and present bias values. The goal is to study the bivariate\",\n \"delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\",\",\n \"options. grid_delta (np.array): Values of discount factor. grid_beta (np.array): Values of present-bias parameter.\",\n \"distribution of the time preference parameters around the combination of true parameter values.\",\n \"\\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"weighting_matrix_hyp.pickle\\\") )\",\n \"open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments =\",\n \"Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta\",\n \"tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\", \\\"beta\\\"), \\\"value\\\"] = beta params_.loc[(\\\"delta\\\", \\\"delta\\\"), \\\"value\\\"]\",\n \"results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors, params=params, grid_delta=np.arange(0.945, 0.9625, 0.0025), grid_beta=np.arange(0.75, 1.05, 0.01), ) results.to_csv(ppj(\\\"OUT_ANALYSIS\\\",\",\n \"The goal is to study the bivariate distribution of the time preference parameters\",\n \"\\\"delta\\\"), \\\"value\\\"] = delta val = crit_func(params_) result = {\\\"beta\\\": beta, \\\"delta\\\": delta,\",\n \"def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params (pd.DataFrame):\",\n \"_replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import calc_unrestricted_choice_probabilities\",\n \"tqdm def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta): \\\"\\\"\\\"Compute value of criterion function. Args: params\",\n \"combinations of discount factor and present bias values. The goal is to study\",\n \"and present bias values. The goal is to study the bivariate distribution of\",\n \"pd import respy as rp import yaml from bld.project_paths import project_paths_join as ppj\",\n \"return pd.DataFrame.from_dict(results) if __name__ == \\\"__main__\\\": # load params params = pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\",\",\n \"as options: options = yaml.safe_load(options) # get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\",\",\n \"moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle(\",\n \"bias values. The goal is to study the bivariate distribution of the time\",\n \"Returns: pd.DataFrame \\\"\\\"\\\" results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_\",\n \"Distribution Restricted\\\": calc_restricted_wage_distribution, \\\"Wage Distribution Unrestricted\\\": calc_unrestricted_wage_distribution, } with _temporary_working_directory(snippet=\\\"heatmap\\\"): # get criterion\",\n \"replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix, ) # get bivariate distribution results results = get_bivariate_distribution( crit_func=weighted_sum_squared_errors,\",\n \"# get criterion function weighted_sum_squared_errors = rp.get_moment_errors_func( params=params, options=options, calc_moments=calc_moments, replace_nans=_replace_nans, empirical_moments=empirical_moments, weighting_matrix=weighting_matrix,\",\n \"respy as rp import yaml from bld.project_paths import project_paths_join as ppj from src.library.compute_moments\",\n \"import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from src.library.housekeeping import\",\n \"\\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities,\",\n \"= params[\\\"value\\\"].astype(float) # load options with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options)\",\n \"import _replace_nans from src.library.compute_moments import calc_restricted_choice_probabilities from src.library.compute_moments import calc_restricted_wage_distribution from src.library.compute_moments import\",\n \"\\\"\\\"\\\"Generate values of Method of Simulated Moments criterion function. Given observed moments and\",\n \"observed moments and weighting matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of\",\n \"matrix in `OUT_ANALYSIS`, \\\"msm_estimation\\\", generate values of Method of Simulated Moments criterion function\",\n \"discount factor. grid_beta (np.array): Values of present-bias parameter. Returns: pd.DataFrame \\\"\\\"\\\" results =\",\n \"results = [] for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)): params_ = params.copy() params_.loc[(\\\"beta\\\",\",\n \"pd.read_csv( ppj(\\\"IN_MODEL_SPECS\\\", \\\"params_hyp.csv\\\"), sep=\\\";\\\", index_col=[\\\"category\\\", \\\"name\\\"], ) params[\\\"value\\\"] = params[\\\"value\\\"].astype(float) # load options\",\n \"with open(ppj(\\\"IN_MODEL_SPECS\\\", \\\"options_hyp.yaml\\\")) as options: options = yaml.safe_load(options) # get empirical moments empirical_moments\",\n \"get empirical moments empirical_moments = _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix\",\n \"= _load_pickle(ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\", \\\"moments_hyp.pickle\\\")) # get weighting matrix weighting_matrix = _load_pickle( ppj(\\\"OUT_ANALYSIS\\\", \\\"msm_estimation\\\",\",\n \"= { \\\"Choice Probabilities Very Restricted\\\": calc_very_restricted_choice_probabilities, \\\"Choice Probabilities Restricted\\\": calc_restricted_choice_probabilities, \\\"Choice Probabilities\",\n \"parameters. crit_func (dict): Dictionary containing model options. grid_delta (np.array): Values of discount factor.\",\n \"calc_restricted_choice_probabilities, \\\"Choice Probabilities Unrestricted\\\": calc_unrestricted_choice_probabilities, \\\"Wage Distribution Very Restricted\\\": calc_very_restricted_wage_distribution, \\\"Wage Distribution Restricted\\\":\",\n \"itertools import numpy as np import pandas as pd import respy as rp\",\n \"from src.library.compute_moments import calc_very_restricted_choice_probabilities from src.library.compute_moments import calc_very_restricted_wage_distribution from src.library.housekeeping import _load_pickle from\"\n]","isConversational":false}}},{"rowIdx":2586,"cells":{"ngram":{"kind":"list like","value":["import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event):","dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event): key_id:","from dataclasses import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass","from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event): key_id: KeyId","dataclasses import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class"],"string":"[\n \"import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event):\",\n \"dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event): key_id:\",\n \"from dataclasses import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass\",\n \"from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event): key_id: KeyId\",\n \"dataclasses import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class\"\n]","isConversational":false}}},{"rowIdx":2587,"cells":{"ngram":{"kind":"list like","value":["= frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading =","supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading","logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config):","+ message.get(\"user\") + \" Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading))","import pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT])","message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while","message): heading = message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \" Message:","message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \" Message: \" + message.get(\"body\")","\"User: \" + message.get(\"user\") + \" Message: \" + message.get(\"body\") try: myTeamsMessage =","send(self, message): heading = message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \"","config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message = \"User:","__init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message =","try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while sending","= config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message = \"User: \" +","def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message","TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def","+ message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured","= \"User: \" + message.get(\"user\") + \" Message: \" + message.get(\"body\") try: myTeamsMessage","<gh_stars>100-1000 import pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports =","import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook']","from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook","\" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue","def send(self, message): heading = message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") +","frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\"subject\")","message.get(\"user\") + \" Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message))","myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while sending message to teams messenger\")","heading = message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \" Message: \"","pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def","import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self,","self.webhook = config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message = \"User: \"","final_message = \"User: \" + message.get(\"user\") + \" Message: \" + message.get(\"body\") try:","myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while sending message","= pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while sending message to","= message.get(\"subject\") final_message = \"User: \" + message.get(\"user\") + \" Message: \" +","pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An issue occured while sending message to teams","class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self,","+ \" Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send()","\" + message.get(\"user\") + \" Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook)","\" Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except:","Message: \" + message.get(\"body\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\"An","config['webhook'] def send(self, message): heading = message.get(\"subject\") final_message = \"User: \" + message.get(\"user\")","oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook =","teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message):"],"string":"[\n \"= frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading =\",\n \"supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading\",\n \"logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config):\",\n \"+ message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading))\",\n \"import pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT])\",\n \"message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while\",\n \"message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message:\",\n \"message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\")\",\n \"\\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage =\",\n \"send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\"\",\n \"config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User:\",\n \"__init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message =\",\n \"try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while sending\",\n \"= config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" +\",\n \"def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message\",\n \"TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def\",\n \"+ message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured\",\n \"= \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage\",\n \"<gh_stars>100-1000 import pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports =\",\n \"import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook']\",\n \"from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook\",\n \"\\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue\",\n \"def send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") +\",\n \"frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\")\",\n \"message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message))\",\n \"myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while sending message to teams messenger\\\")\",\n \"heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\"\",\n \"pymsteams import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def\",\n \"import logging from oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self,\",\n \"self.webhook = config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\"\",\n \"final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try:\",\n \"myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while sending message\",\n \"= pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while sending message to\",\n \"= message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\") + \\\" Message: \\\" +\",\n \"pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An issue occured while sending message to teams\",\n \"class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self,\",\n \"+ \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send()\",\n \"\\\" + message.get(\\\"user\\\") + \\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook)\",\n \"\\\" Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except:\",\n \"Message: \\\" + message.get(\\\"body\\\") try: myTeamsMessage = pymsteams.connectorcard(self.webhook) myTeamsMessage.title(str(heading)) myTeamsMessage.text(str(final_message)) myTeamsMessage.send() except: logging.info(\\\"An\",\n \"config['webhook'] def send(self, message): heading = message.get(\\\"subject\\\") final_message = \\\"User: \\\" + message.get(\\\"user\\\")\",\n \"oncall.constants import TEAMS_SUPPORT class teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook =\",\n \"teams_messenger(object): supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message):\"\n]","isConversational":false}}},{"rowIdx":2588,"cells":{"ngram":{"kind":"list like","value":["test/valid data. \"\"\" if data is None: raise ValueError(\"Input invalid data of None\")","save(self, checkpoint_file): \"\"\" Save the best model after HPO. :param checkpoint_file: The location","OR CONDITIONS OF ANY KIND, either exp' # ress or implied. # See","sync training results and checkpoints. It defaults to None and doesn't take effects","Unless required by applicable law or agreed to in writing, software # distributed","is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric","if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg,",":param metrics: A list contains metrics for test/valid data. \"\"\" if data is","by default. Setting this option to False to speed up the process. :param","a pandas dataframe with Td rows, and 2 columns, with column 'ds' indicating","expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2, \"The training","The location you want to save the best model, should be a json","RuntimeError( \"You must call fit or restore first before calling predict!\") return self.best_model.predict(horizon=horizon,","unreliable frequency will be infer implicitly. :param metric_threshold: a trial will be terminated","expect_horizon is the horizon you may need to use once the mode is","if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp","cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1. :param","training data, and expect_horizon is the horizon you may need to use once","the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best Prophet model.","total. Defaults to 16. If hp.grid_search is in search_space, the grid will be","is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode,",":param metric: String. The evaluation metric name to optimize. e.g. \"mse\" :param logs_dir:","0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs","predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best model after HPO. :param","cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir. The","freq is None: assert len(data) >= 2, \"The training dataframe should contains more","Predict using the best model after HPO. :param horizon: the number of steps","checkpoint_file): \"\"\" Restore the best model after HPO. :param checkpoint_file: The checkpoint file","changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation","a stopping condition is met. :param search_alg: str, all supported searcher provided by","validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling","model after HPO. :param data: evaluation data, a pandas dataframe with Td rows,","local. While running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the","# ress or implied. # See the License for the specific language governing","this file except in compliance with the License. # You may obtain a","\"\"\" if data is None: raise ValueError(\"Input invalid data of None\") if self.best_model.model","is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2,","pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import","the time dimension :param cross_validation: bool, if the eval result comes from cross_validation.","bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale","else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale,","to save logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number","searcher mode :param scheduler: str, all supported scheduler provided by ray tune :param","`freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str","- class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1,","def save(self, checkpoint_file): \"\"\" Save the best model after HPO. :param checkpoint_file: The","up the process. :param expect_horizon: int, validation data will be automatically splited from","String. Remote directory to sync training results and checkpoints. It defaults to None","None, where 10% of training data will be taken as the validation data.","cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space,","a trial will be terminated when metric threshold is met :param n_sampling: Number","in search_space, the grid will be run n_sampling of trials and round up","search_space, metric and searcher mode :param scheduler: str, all supported scheduler provided by","Td rows, and 2 columns, with column 'ds' indicating date and column 'y'","Load the ckpt from load_dir. The value defaults to None. :param prophet_config: Other","n_sampling: Number of trials to evaluate in total. Defaults to 16. If hp.grid_search","\"\"\" Automatically fit the model and search for the best hyperparameters. :param data:","calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best model after HPO.","directory to sync training results and checkpoints. It defaults to None and doesn't","to fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data)","self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est =","None: assert len(data) >= 2, \"The training dataframe should contains more than 2","refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer","e.g. \"mse\" :param logs_dir: Local directory to save logs and results. It defaults","from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an","pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency","cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet Model. User","AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]`","the best model, should be a json file \"\"\" if self.best_model.model is None:","\"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if","search for the best hyperparameters. :param data: training data, a pandas dataframe with","up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples are","self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq *","best config to fit a new prophet model on whole data self.best_model =","pandas dataframe with Td rows, and 2 columns, with column 'ds' indicating date","cross_validation. The value is set to True by default. Setting this option to","is the horizon you may need to use once the mode is fitted.","the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling","logs_dir: Local directory to save logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param","trial will be terminated when metric threshold is met :param n_sampling: Number of","n_sampling of trials and round up n_sampling according to hp.grid_search. If this is","metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config","The checkpoint file location you want to load the best model. \"\"\" self.best_model.restore(checkpoint_file)","of None\") if self.best_model.model is None: raise RuntimeError( \"You must call fit or","training results and checkpoints. It defaults to None and doesn't take effects while","self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the","and checkpoints. It defaults to None and doesn't take effects while running in","HPO. :param data: evaluation data, a pandas dataframe with Td rows, and 2",":param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode:","holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95)","the best model after HPO. :param horizon: the number of steps forward to","import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1,","None and doesn't take effects while running in local. While running in cluster,","ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\"","be a json file \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must","for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for","\"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if","License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel","cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit`","cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation","if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling =","to speed up the process. :param expect_horizon: int, validation data will be automatically","use the best config to fit a new prophet model on whole data","predicted dataframe, defaulted to day(\"D\"), the frequency can be anything from the pandas",":param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale:","if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale","permissions and # limitations under the License. # import pandas as pd import","def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None,","metrics: A list contains metrics for test/valid data. \"\"\" if data is None:","2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0","\"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for searcher algorithm besides search_space,","configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial},","ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit` function.\") def fit(self, data,","horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best model after HPO. :param horizon:","frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer","best model after HPO. :param horizon: the number of steps forward to predict","to day(\"D\"), the frequency can be anything from the pandas list of frequency","Local directory to save logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial:","of the predicted dataframe, defaulted to day(\"D\"), the frequency can be anything from","HPO. :param checkpoint_file: The location you want to save the best model, should","governing permissions and # limitations under the License. # import pandas as pd","self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data","checkpoint_file: The location you want to save the best model, should be a","Setting this option to False to speed up the process. :param expect_horizon: int,","for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the","supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\",","= ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space","search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model and search for the",":param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range:","training data will be taken as the validation data. :param freq: the freqency","Td is the time dimension :param cross_validation: bool, if the eval result comes","the ckpt from load_dir. The value defaults to None. :param prophet_config: Other Prophet","\"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters","The value defaults to None, where 10% of training data will be taken","and Td is the time dimension :param metrics: A list contains metrics for","call fit or restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self,","stopping condition is met. :param search_alg: str, all supported searcher provided by ray","calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using",":param scheduler: str, all supported scheduler provided by ray tune :param scheduler_params: parameters","to specify either the exact value or the search space of the Prophet","= ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best","the best model after HPO. :param checkpoint_file: The checkpoint file location you want","name: name of the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String. Remote","scheduler_params=None, ): \"\"\" Automatically fit the model and search for the best hyperparameters.","the Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to","config to fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config())","software # distributed under the License is distributed on an \"AS IS\" BASIS,","here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly. :param","of steps forward to predict :param freq: the freqency of the predicted dataframe,","model after HPO. :param checkpoint_file: The checkpoint file location you want to load","None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2, \"The","Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator","hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator","evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best model after","\"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for searcher algorithm besides","forward to predict :param freq: the freqency of the predicted dataframe, defaulted to","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","scheduler_params: parameters for scheduler \"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp'","restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save","date and column 'y' indicating value and Td is the time dimension :param","validation data. :param freq: the freqency of the training dataframe. the frequency can","2, \"The training dataframe should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\","model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize.","dataframe that has 1 column 'ds' indicating date. \"\"\" if self.best_model.model is None:","'ds' col should be in datetime 64 type, or you need to set","predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the","e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive',",":param data: training data, a pandas dataframe with Td rows, and 2 columns,","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\":","searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\",","hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None","hp sampling function from an integer space for hyperparameter changepoint_prior_scale for the Prophet","be run n_sampling of trials and round up n_sampling according to hp.grid_search. If","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","len(data) >= 2, \"The training dataframe should contains more than 2 records.\" assert","It defaults to \"auto_prophet\" :param remote_dir: String. Remote directory to sync training results","predict :param freq: the freqency of the predicted dataframe, defaulted to day(\"D\"), the","data of None\") if self.best_model.model is None: raise RuntimeError( \"You must call fit","raise RuntimeError( \"You must call fit or restore first before calling evaluate!\") return","seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\"",":param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.","None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder","= str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation","import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils","and expect_horizon is the horizon you may need to use once the mode",":param freq: the freqency of the predicted dataframe, defaulted to day(\"D\"), the frequency","0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if","freq: the freqency of the predicted dataframe, defaulted to day(\"D\"), the frequency can","changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details.","metric name to optimize. e.g. \"mse\" :param logs_dir: Local directory to save logs","holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else","None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\":","data, and expect_horizon is the horizon you may need to use once the","data: training data, a pandas dataframe with Td rows, and 2 columns, with","with Td rows, and 2 columns, with column 'ds' indicating date and column","required by applicable law or agreed to in writing, software # distributed under","list contains metrics for test/valid data. \"\"\" if data is None: raise ValueError(\"Input","raise RuntimeError( \"You must call fit or restore first before calling save!\") self.best_model.save(checkpoint_file)","hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter","of the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String. Remote directory to","freqency of the training dataframe. the frequency can be anything from the pandas","applicable law or agreed to in writing, software # distributed under the License","0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0,","ds_data=None): \"\"\" Predict using the best model after HPO. :param horizon: the number","'y' indicating value and Td is the time dimension :param metrics: A list","for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g.","use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None,","10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is","restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best","search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher mode :param","results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for each","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or","will be automatically splited from training data, and expect_horizon is the horizon you","or agreed to in writing, software # distributed under the License is distributed","language governing permissions and # limitations under the License. # import pandas as","changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric =","first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best model","hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range","save the best model, should be a json file \"\"\" if self.best_model.model is","data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically","model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best Prophet model. \"\"\" return","mode :param scheduler: str, all supported scheduler provided by ray tune :param scheduler_params:","AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None,","mode is fitted. The value defaults to None, where 10% of training data","is None: assert len(data) >= 2, \"The training dataframe should contains more than","Create an automated Prophet Model. User need to specify either the exact value","will be infer implicitly. :param metric_threshold: a trial will be terminated when metric","expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if","new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1,","install `bigdl-orca[automl]` to use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None,","records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in datetime 64 type,","set to True by default. Setting this option to False to speed up","best model after HPO. :param checkpoint_file: The location you want to save the","import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self,","else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive',","splited from training data, and expect_horizon is the horizon you may need to","to 16. If hp.grid_search is in search_space, the grid will be run n_sampling","for the best hyperparameters. :param data: training data, a pandas dataframe with Td","else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data,",":param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher mode","under the Apache License, Version 2.0 (the \"License\"); # you may not use","(virtually) infinite samples are generated until a stopping condition is met. :param search_alg:","evaluation metric name to optimize. e.g. \"mse\" :param logs_dir: Local directory to save","# Copyright 2016 The BigDL Authors. # # Licensed under the Apache License,","writing, software # distributed under the License is distributed on an \"AS IS\"","under the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import","`bigdl-orca[automl]` to use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16,","data: evaluation data, a pandas dataframe with Td rows, and 2 columns, with","the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet","file \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call fit or","seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter","bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the","metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model and","ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best model after HPO.","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating date. \"\"\"","the time dimension :param metrics: A list contains metrics for test/valid data. \"\"\"","License. # You may obtain a copy of the License at # #","self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use","from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe",":param name: name of the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String.","'ds' indicating date. \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call","automatically splited from training data, and expect_horizon is the horizon you may need","self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir,","'ds' indicating date and column 'y' indicating value and Td is the time","Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function","will be terminated when metric threshold is met :param n_sampling: Number of trials","fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def","will be run n_sampling of trials and round up n_sampling according to hp.grid_search.","assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in datetime 64 type, or","seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for","need to use once the mode is fitted. The value defaults to None,","freq=\"D\", ds_data=None): \"\"\" Predict using the best model after HPO. :param horizon: the","except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit` function.\") def fit(self,","Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.","https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer space for","the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name","compliance with the License. # You may obtain a copy of the License","name to optimize. e.g. \"mse\" :param logs_dir: Local directory to save logs and","hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize. e.g. \"mse\"","None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\":","or you need to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0]","\"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for searcher algorithm","column 'y' indicating value and Td is the time dimension :param metrics: A","+ # # Copyright 2016 The BigDL Authors. # # Licensed under the",":param freq: the freqency of the training dataframe. the frequency can be anything","ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best model","if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None","where 10% of training data will be taken as the validation data. :param","hp.grid_search is in search_space, the grid will be run n_sampling of trials and","be in datetime 64 type, or you need to set `freq` in fit.\"","best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best Prophet model. \"\"\"","dataframe with Td rows, and 2 columns, with column 'ds' indicating date and","see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for","hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1,",":param logs_dir: Local directory to save logs and results. It defaults to \"/tmp/auto_prophet_logs\"","= { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale,","day(\"D\"), the frequency can be anything from the pandas list of frequency strings","restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']):","list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1","or restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\"","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","def restore(self, checkpoint_file): \"\"\" Restore the best model after HPO. :param checkpoint_file: The","take effects while running in local. While running in cluster, it defaults to","effects while running in local. While running in cluster, it defaults to \"hdfs:///tmp/{name}\".","trial. It defaults to 1. :param name: name of the AutoProphet. It defaults","self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress","\"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import","str, all supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\",","= data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon)","n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params,","Evaluate using the best model after HPO. :param data: evaluation data, a pandas","data is None: raise ValueError(\"Input invalid data of None\") if self.best_model.model is None:","from load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \"\"\"",":param changepoint_prior_scale: Int or hp sampling function from an integer space for hyperparameter","ANY KIND, either exp' # ress or implied. # See the License for","ress or implied. # See the License for the specific language governing permissions","bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05,","be infer implicitly. :param metric_threshold: a trial will be terminated when metric threshold","of the Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer","result comes from cross_validation. The value is set to True by default. Setting","you need to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else:","model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError:","-1, (virtually) infinite samples are generated until a stopping condition is met. :param","\"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra","frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds'","indicating value and Td is the time dimension :param metrics: A list contains","Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to","and column 'y' indicating value and Td is the time dimension :param metrics:","of the training dataframe. the frequency can be anything from the pandas list","= int(0.1*len(data)) if freq is None: assert len(data) >= 2, \"The training dataframe","training data, a pandas dataframe with Td rows, and 2 columns, with column","and 2 columns, with column 'ds' indicating date and column 'y' indicating value","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or implied.","of cpus for each trial. It defaults to 1. :param name: name of","0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if","not use this file except in compliance with the License. # You may","else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder =","json file \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call fit","you want to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get","e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01,","anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a","bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def","location you want to save the best model, should be a json file","self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp","and doesn't take effects while running in local. While running in cluster, it","if the eval result comes from cross_validation. The value is set to True","indicating value and Td is the time dimension :param cross_validation: bool, if the","data. \"\"\" if data is None: raise ValueError(\"Input invalid data of None\") if","License, Version 2.0 (the \"License\"); # you may not use this file except","else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params )","remote_dir: String. Remote directory to sync training results and checkpoints. It defaults to","changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder()","prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from","each trial. It defaults to 1. :param name: name of the AutoProphet. It","expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None","def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ):","scheduler: str, all supported scheduler provided by ray tune :param scheduler_params: parameters for","first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\"","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated","expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data","fit or restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore","restore(self, checkpoint_file): \"\"\" Restore the best model after HPO. :param checkpoint_file: The checkpoint","if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None","calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best model","to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir. The value defaults to","and Td is the time dimension :param cross_validation: bool, if the eval result","Save the best model after HPO. :param checkpoint_file: The location you want to","more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model.","indicating date. \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call fit","condition is met. :param search_alg: str, all supported searcher provided by ray tune","model after HPO. :param horizon: the number of steps forward to predict :param","# you may not use this file except in compliance with the License.","defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir. The value defaults","hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']).","n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples are generated","scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a new prophet","seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative'])","cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit","in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir.","agreed to in writing, software # distributed under the License is distributed on","= metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name)","} self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est","seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for","The value is set to True by default. Setting this option to False","metric and searcher mode :param scheduler: str, all supported scheduler provided by ray","\"\"\" Save the best model after HPO. :param checkpoint_file: The location you want","value defaults to None, where 10% of training data will be taken as","the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable","be taken as the validation data. :param freq: the freqency of the training","seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else","(the \"License\"); # you may not use this file except in compliance with","ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need","# import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from","64 type, or you need to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1]","round up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples","model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g.","model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param","list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will","holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter","implicitly. :param metric_threshold: a trial will be terminated when metric threshold is met","datetime 64 type, or you need to set `freq` in fit.\" self._freq =","is set to True by default. Setting this option to False to speed","checkpoint_file): \"\"\" Save the best model after HPO. :param checkpoint_file: The location you","exp' # ress or implied. # See the License for the specific language","space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp","of training data will be taken as the validation data. :param freq: the","in datetime 64 type, or you need to set `freq` in fit.\" self._freq","\"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update","hyperparameters. :param data: training data, a pandas dataframe with Td rows, and 2","self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best model after HPO. :param checkpoint_file:","# Unless required by applicable law or agreed to in writing, software #","dataframe, defaulted to day(\"D\"), the frequency can be anything from the pandas list","integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see","is fitted. The value defaults to None, where 10% of training data will","def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best model after HPO.","is the time dimension :param metrics: A list contains metrics for test/valid data.","by applicable law or agreed to in writing, software # distributed under the","\"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric","metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best model after HPO. :param checkpoint_file:","best model after HPO. :param checkpoint_file: The checkpoint file location you want to","The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir:","self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data =","rows, and 2 columns, with column 'ds' indicating date and column 'y' indicating","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","all supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler \"\"\"","name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit` function.\") def","indicating date and column 'y' indicating value and Td is the time dimension","frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases","data, metrics=['mse']): \"\"\" Evaluate using the best model after HPO. :param data: evaluation","defaults to None, where 10% of training data will be taken as the","the best config to fit a new prophet model on whole data self.best_model","as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling","n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to","\"You must call fit or restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq,","best hyperparameters. :param data: training data, a pandas dataframe with Td rows, and","file location you want to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self):","name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet Model. User need","for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric","need to specify either the exact value or the search space of the","self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to","a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self,",":param cross_validation: bool, if the eval result comes from cross_validation. The value is","after HPO. :param checkpoint_file: The checkpoint file location you want to load the","evaluate in total. Defaults to 16. If hp.grid_search is in search_space, the grid","freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best model after","file except in compliance with the License. # You may obtain a copy","\"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params:","is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range","and # limitations under the License. # import pandas as pd import warnings","want to save the best model, should be a json file \"\"\" if","else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling)","column 'y' indicating value and Td is the time dimension :param cross_validation: bool,","must call fit or restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data)","update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir,","value is set to True by default. Setting this option to False to","provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\",","License for the specific language governing permissions and # limitations under the License.","\"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param","= recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold,","None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else","in local. While running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load","load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet Model. User need to specify","to in writing, software # distributed under the License is distributed on an","BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\");","Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model.","hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale","hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95).","metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet","If this is -1, (virtually) infinite samples are generated until a stopping condition","search_alg: str, all supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\",","str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation else","horizon: the number of steps forward to predict :param freq: the freqency of","implied. # See the License for the specific language governing permissions and #","if self.best_model.model is None: raise RuntimeError( \"You must call fit or restore first","function from an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For","\"License\"); # you may not use this file except in compliance with the","from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005,","scheduler_params=scheduler_params ) # use the best config to fit a new prophet model","expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >=","before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best model after","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","the eval result comes from cross_validation. The value is set to True by","until a stopping condition is met. :param search_alg: str, all supported searcher provided","to None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel()","for searcher algorithm besides search_space, metric and searcher mode :param scheduler: str, all","hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an","hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The","specify either the exact value or the search space of the Prophet model","bool, if the eval result comes from cross_validation. The value is set to","option to False to speed up the process. :param expect_horizon: int, validation data","of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column","than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in datetime","seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter","scheduler provided by ray tune :param scheduler_params: parameters for scheduler \"\"\" if expect_horizon","want to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the","to hp.grid_search. If this is -1, (virtually) infinite samples are generated until a","= AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install","to None, where an unreliable frequency will be infer implicitly. :param metric_threshold: a","hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more","{ \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\":","be terminated when metric threshold is met :param n_sampling: Number of trials to","the best model after HPO. :param data: evaluation data, a pandas dataframe with","None: raise RuntimeError( \"You must call fit or restore first before calling evaluate!\")","or implied. # See the License for the specific language governing permissions and","of trials and round up n_sampling according to hp.grid_search. If this is -1,","load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best Prophet","or restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the","Prophet Model. User need to specify either the exact value or the search","metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except","Apache License, Version 2.0 (the \"License\"); # you may not use this file","bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None,","Automatically fit the model and search for the best hyperparameters. :param data: training","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should","RuntimeError( \"You must call fit or restore first before calling evaluate!\") return self.best_model.evaluate(target=data,","ray tune :param scheduler_params: parameters for scheduler \"\"\" if expect_horizon is None: expect_horizon","of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp","details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g.","specific language governing permissions and # limitations under the License. # import pandas","function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None,","contains metrics for test/valid data. \"\"\" if data is None: raise ValueError(\"Input invalid","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale","if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else","in writing, software # distributed under the License is distributed on an \"AS","a json file \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call","metric threshold is met :param n_sampling: Number of trials to evaluate in total.","load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as","model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g.","according to hp.grid_search. If this is -1, (virtually) infinite samples are generated until","hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode","contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be","None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range }","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","and column 'y' indicating value and Td is the time dimension :param cross_validation:","for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the","Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model.","ValueError(\"Input invalid data of None\") if self.best_model.model is None: raise RuntimeError( \"You must","col should be in datetime 64 type, or you need to set `freq`","save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\" Restore the best model after HPO. :param","WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or implied. #","either exp' # ress or implied. # See the License for the specific","search space of the Prophet model hyperparameters. For details of the Prophet model","\"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for searcher","search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit","ds_data: a dataframe that has 1 column 'ds' indicating date. \"\"\" if self.best_model.model","after HPO. :param checkpoint_file: The location you want to save the best model,","date. \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call fit or","OF ANY KIND, either exp' # ress or implied. # See the License","try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\":","e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8,","model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g.","doesn't take effects while running in local. While running in cluster, it defaults","the validation data. :param freq: the freqency of the training dataframe. the frequency","import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None,","the freqency of the training dataframe. the frequency can be anything from the","validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the","A list contains metrics for test/valid data. \"\"\" if data is None: raise","if data is None: raise ValueError(\"Input invalid data of None\") if self.best_model.model is","If hp.grid_search is in search_space, the grid will be run n_sampling of trials","int, validation data will be automatically splited from training data, and expect_horizon is","`fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None,","model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None):","\"auto_prophet\" :param remote_dir: String. Remote directory to sync training results and checkpoints. It","infer implicitly. :param metric_threshold: a trial will be terminated when metric threshold is","remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet Model. User need to","0.95). :param metric: String. The evaluation metric name to optimize. e.g. \"mse\" :param","True by default. Setting this option to False to speed up the process.","None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir)","defaults to None and doesn't take effects while running in local. While running","the best model after HPO. :param checkpoint_file: The location you want to save","must call fit or restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file):","the Apache License, Version 2.0 (the \"License\"); # you may not use this","you may not use this file except in compliance with the License. #",":param n_sampling: Number of trials to evaluate in total. Defaults to 16. If","fit the model and search for the best hyperparameters. :param data: training data,","dimension :param cross_validation: bool, if the eval result comes from cross_validation. The value","grid will be run n_sampling of trials and round up n_sampling according to","model, should be a json file \"\"\" if self.best_model.model is None: raise RuntimeError(","model after HPO. :param checkpoint_file: The location you want to save the best","here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating date.","model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from","changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is","horizon you may need to use once the mode is fitted. The value","scheduler \"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None:","exact value or the search space of the Prophet model hyperparameters. For details","return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best","number of steps forward to predict :param freq: the freqency of the predicted","\"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to","\"\"\" if self.best_model.model is None: raise RuntimeError( \"You must call fit or restore","to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best","fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\"","logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an automated Prophet Model.","e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01,","are generated until a stopping condition is met. :param search_alg: str, all supported","freq: the freqency of the training dataframe. the frequency can be anything from","KIND, either exp' # ress or implied. # See the License for the","use once the mode is fitted. The value defaults to None, where 10%","ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space =","before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate","use this file except in compliance with the License. # You may obtain","first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the","to None, where 10% of training data will be taken as the validation","comes from cross_validation. The value is set to True by default. Setting this","where an unreliable frequency will be infer implicitly. :param metric_threshold: a trial will","infinite samples are generated until a stopping condition is met. :param search_alg: str,","is in search_space, the grid will be run n_sampling of trials and round","metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best","if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None","on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\"","RuntimeError( \"You must call fit or restore first before calling save!\") self.best_model.save(checkpoint_file) def","Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5).",":param search_alg: str, all supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\",","CONDITIONS OF ANY KIND, either exp' # ress or implied. # See the","import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if","will be taken as the validation data. :param freq: the freqency of the","scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model and search for the best","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","run n_sampling of trials and round up n_sampling according to hp.grid_search. If this","besides search_space, metric and searcher mode :param scheduler: str, all supported scheduler provided","cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1","metrics=['mse']): \"\"\" Evaluate using the best model after HPO. :param data: evaluation data,","checkpoint_file: The checkpoint file location you want to load the best model. \"\"\"","changepoint_prior_scale: Int or hp sampling function from an integer space for hyperparameter changepoint_prior_scale","holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for","load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if","): \"\"\" Create an automated Prophet Model. User need to specify either the","to True by default. Setting this option to False to speed up the","# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache","evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best model after HPO. :param data:","While running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt","e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize. e.g.","or hp sampling function from an integer space for hyperparameter changepoint_prior_scale for the","speed up the process. :param expect_horizon: int, validation data will be automatically splited","pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has","samples are generated until a stopping condition is met. :param search_alg: str, all","for each trial. It defaults to 1. :param name: name of the AutoProphet.","the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that","2.0 (the \"License\"); # you may not use this file except in compliance",">= 2, \"The training dataframe should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes),","remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit` function.\")","be automatically splited from training data, and expect_horizon is the horizon you may","value and Td is the time dimension :param metrics: A list contains metrics","return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best model after HPO.","when metric threshold is met :param n_sampling: Number of trials to evaluate in","\"The training dataframe should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The","as the validation data. :param freq: the freqency of the training dataframe. the","None, where an unreliable frequency will be infer implicitly. :param metric_threshold: a trial","pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling #","for the specific language governing permissions and # limitations under the License. #","HPO. :param checkpoint_file: The checkpoint file location you want to load the best","int(0.1*len(data)) if freq is None: assert len(data) >= 2, \"The training dataframe should","is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None","else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config)","dataframe should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col","seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else","a dataframe that has 1 column 'ds' indicating date. \"\"\" if self.best_model.model is","data. :param freq: the freqency of the training dataframe. the frequency can be","using the best model after HPO. :param data: evaluation data, a pandas dataframe","parameters for scheduler \"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq","# use the best config to fit a new prophet model on whole","The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the","trials and round up n_sampling according to hp.grid_search. If this is -1, (virtually)","self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best model","running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from","10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param","\"\"\" Create an automated Prophet Model. User need to specify either the exact","dataframe. the frequency can be anything from the pandas list of frequency strings","# # Unless required by applicable law or agreed to in writing, software","0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param","n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model and search","fitted. The value defaults to None, where 10% of training data will be","be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None,","the frequency can be anything from the pandas list of frequency strings here:","changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String.","* expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon]","= pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data =","the predicted dataframe, defaulted to day(\"D\"), the frequency can be anything from the","best model, should be a json file \"\"\" if self.best_model.model is None: raise","Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model.","steps forward to predict :param freq: the freqency of the predicted dataframe, defaulted","optimize. e.g. \"mse\" :param logs_dir: Local directory to save logs and results. It","metrics for test/valid data. \"\"\" if data is None: raise ValueError(\"Input invalid data","this option to False to speed up the process. :param expect_horizon: int, validation","self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using the best model after","and round up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite","all supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\",","is None: raise ValueError(\"Input invalid data of None\") if self.best_model.model is None: raise","should be a json file \"\"\" if self.best_model.model is None: raise RuntimeError( \"You","as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is","this is -1, (virtually) infinite samples are generated until a stopping condition is","taken as the validation data. :param freq: the freqency of the training dataframe.","need to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq","hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other","is met. :param search_alg: str, all supported searcher provided by ray tune (i.e.\"variant_generator\",","process. :param expect_horizon: int, validation data will be automatically splited from training data,",":param load_dir: Load the ckpt from load_dir. The value defaults to None. :param","must call fit or restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def","to save the best model, should be a json file \"\"\" if self.best_model.model","the training dataframe. the frequency can be anything from the pandas list of","to evaluate in total. Defaults to 16. If hp.grid_search is in search_space, the","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","metric: String. The evaluation metric name to optimize. e.g. \"mse\" :param logs_dir: Local","data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data,","else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8,","- data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str,","automated Prophet Model. User need to specify either the exact value or the","generated until a stopping condition is met. :param search_alg: str, all supported searcher","data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\":","prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\",","value or the search space of the Prophet model hyperparameters. For details of",":param scheduler_params: parameters for scheduler \"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data))","is the time dimension :param cross_validation: bool, if the eval result comes from","after HPO. :param data: evaluation data, a pandas dataframe with Td rows, and","strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating","to use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None,","default. Setting this option to False to speed up the process. :param expect_horizon:","Defaults to 16. If hp.grid_search is in search_space, the grid will be run","need to install `bigdl-orca[automl]` to use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None,","time dimension :param metrics: A list contains metrics for test/valid data. \"\"\" if","Restore the best model after HPO. :param checkpoint_file: The checkpoint file location you","threshold is met :param n_sampling: Number of trials to evaluate in total. Defaults","if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data)","from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet:","= data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:]","warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class","'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param","https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly. :param metric_threshold:","supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler \"\"\" if","the License. # You may obtain a copy of the License at #","is -1, (virtually) infinite samples are generated until a stopping condition is met.","ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None,","pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation}) train_data = data","details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or","'multiplicative']) if seasonality_mode is None else seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is","load_dir: Load the ckpt from load_dir. The value defaults to None. :param prophet_config:","# distributed under the License is distributed on an \"AS IS\" BASIS, #","None: raise RuntimeError( \"You must call fit or restore first before calling save!\")","**prophet_config ): \"\"\" Create an automated Prophet Model. User need to specify either","best model after HPO. :param data: evaluation data, a pandas dataframe with Td","10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is",":param data: evaluation data, a pandas dataframe with Td rows, and 2 columns,","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","Int. Number of cpus for each trial. It defaults to 1. :param name:","freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model","data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling","hp.grid_search. If this is -1, (virtually) infinite samples are generated until a stopping","to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer space","For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int","the best hyperparameters. :param data: training data, a pandas dataframe with Td rows,","tune :param scheduler_params: parameters for scheduler \"\"\" if expect_horizon is None: expect_horizon =","to use once the mode is fitted. The value defaults to None, where","fit or restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file):","to install `bigdl-orca[automl]` to use `fit` function.\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None,","1 column 'ds' indicating date. \"\"\" if self.best_model.model is None: raise RuntimeError( \"You","for test/valid data. \"\"\" if data is None: raise ValueError(\"Input invalid data of","if freq is None: assert len(data) >= 2, \"The training dataframe should contains","metric_threshold: a trial will be terminated when metric threshold is met :param n_sampling:","directory to save logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int.","columns, with column 'ds' indicating date and column 'y' indicating value and Td","Number of trials to evaluate in total. Defaults to 16. If hp.grid_search is","data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if","\"\"\" Predict using the best model after HPO. :param horizon: the number of","None: raise ValueError(\"Input invalid data of None\") if self.best_model.model is None: raise RuntimeError(","10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param","has 1 column 'ds' indicating date. \"\"\" if self.best_model.model is None: raise RuntimeError(","User need to specify either the exact value or the search space of","Td is the time dimension :param metrics: A list contains metrics for test/valid",":param checkpoint_file: The checkpoint file location you want to load the best model.","n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric,","defaults to \"auto_prophet\" :param remote_dir: String. Remote directory to sync training results and","to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq =","with the License. # You may obtain a copy of the License at","of trials to evaluate in total. Defaults to 16. If hp.grid_search is in","self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = {","the number of steps forward to predict :param freq: the freqency of the",":param metric_threshold: a trial will be terminated when metric threshold is met :param","\"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert","\"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01,",") # use the best config to fit a new prophet model on","can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param","is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale,",":param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric:","save logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of","the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder,","from an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","or the search space of the Prophet model hyperparameters. For details of the","warnings.warn(\"You need to install `bigdl-orca[automl]` to use `fit` function.\") def fit(self, data, cross_validation=True,","16. If hp.grid_search is in search_space, the grid will be run n_sampling of","frequency will be infer implicitly. :param metric_threshold: a trial will be terminated when","should be in datetime 64 type, or you need to set `freq` in","1. :param name: name of the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir:","Remote directory to sync training results and checkpoints. It defaults to None and","data will be automatically splited from training data, and expect_horizon is the horizon","parameters for searcher algorithm besides search_space, metric and searcher mode :param scheduler: str,","call fit or restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def","import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # -","eval result comes from cross_validation. The value is set to True by default.","raise ValueError(\"Input invalid data of None\") if self.best_model.model is None: raise RuntimeError( \"You","trials to evaluate in total. Defaults to 16. If hp.grid_search is in search_space,","by ray tune :param scheduler_params: parameters for scheduler \"\"\" if expect_horizon is None:","\\ \"The 'ds' col should be in datetime 64 type, or you need","law or agreed to in writing, software # distributed under the License is","is met :param n_sampling: Number of trials to evaluate in total. Defaults to","be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data:","the License for the specific language governing permissions and # limitations under the","# limitations under the License. # import pandas as pd import warnings from","the exact value or the search space of the Prophet model hyperparameters. For","HPO. :param horizon: the number of steps forward to predict :param freq: the","sampling function from an integer space for hyperparameter changepoint_prior_scale for the Prophet model.","in total. Defaults to 16. If hp.grid_search is in search_space, the grid will","validation data will be automatically splited from training data, and expect_horizon is the","checkpoint file location you want to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def","__init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config","): \"\"\" Automatically fit the model and search for the best hyperparameters. :param","column 'ds' indicating date. \"\"\" if self.best_model.model is None: raise RuntimeError( \"You must","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","an unreliable frequency will be infer implicitly. :param metric_threshold: a trial will be","\"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\" Get the best Prophet model. \"\"\" return self.best_model.model","algorithm besides search_space, metric and searcher mode :param scheduler: str, all supported scheduler","an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling,","\"mse\" :param logs_dir: Local directory to save logs and results. It defaults to","search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a","model and search for the best hyperparameters. :param data: training data, a pandas",":param remote_dir: String. Remote directory to sync training results and checkpoints. It defaults","cross_validation: bool, if the eval result comes from cross_validation. The value is set","checkpoints. It defaults to None and doesn't take effects while running in local.","assert len(data) >= 2, \"The training dataframe should contains more than 2 records.\"","to False to speed up the process. :param expect_horizon: int, validation data will","ckpt from load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters.","once the mode is fitted. The value defaults to None, where 10% of","holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create","in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str =","hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale:","while running in local. While running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param","running in local. While running in cluster, it defaults to \"hdfs:///tmp/{name}\". :param load_dir:","Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import","hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \"seasonality_mode\": hp.choice(['additive', 'multiplicative']) if seasonality_mode","logs and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus","searcher algorithm besides search_space, metric and searcher mode :param scheduler: str, all supported","in compliance with the License. # You may obtain a copy of the","AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String. Remote directory to sync training","AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5])","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling,","raise RuntimeError( \"You must call fit or restore first before calling predict!\") return","whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict","can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to","Number of cpus for each trial. It defaults to 1. :param name: name","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","value and Td is the time dimension :param cross_validation: bool, if the eval","String. The evaluation metric name to optimize. e.g. \"mse\" :param logs_dir: Local directory","by ray tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\",",":param horizon: the number of steps forward to predict :param freq: the freqency","set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq)","is None: raise RuntimeError( \"You must call fit or restore first before calling","Int or hp sampling function from an integer space for hyperparameter changepoint_prior_scale for","more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in","or restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data,","# + # # Copyright 2016 The BigDL Authors. # # Licensed under","See the License for the specific language governing permissions and # limitations under","(i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\")","if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric","with column 'ds' indicating date and column 'y' indicating value and Td is","else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\": cross_validation})","1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10) if holidays_prior_scale","to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults","the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet","space of the Prophet model hyperparameters. For details of the Prophet model hyperparameters,","name of the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String. Remote directory","\"sigopt\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher","search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a new","to predict :param freq: the freqency of the predicted dataframe, defaulted to day(\"D\"),","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","= None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling !=","str, all supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler","for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","to \"auto_prophet\" :param remote_dir: String. Remote directory to sync training results and checkpoints.","the freqency of the predicted dataframe, defaulted to day(\"D\"), the frequency can be","and search for the best hyperparameters. :param data: training data, a pandas dataframe","defaulted to day(\"D\"), the frequency can be anything from the pandas list of","after HPO. :param horizon: the number of steps forward to predict :param freq:","the horizon you may need to use once the mode is fitted. The","provided by ray tune :param scheduler_params: parameters for scheduler \"\"\" if expect_horizon is","self.best_model.model is None: raise RuntimeError( \"You must call fit or restore first before","-1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params","an automated Prophet Model. User need to specify either the exact value or","\"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir. The value defaults to None.","defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for each trial. It","results and checkpoints. It defaults to None and doesn't take effects while running","column 'ds' indicating date and column 'y' indicating value and Td is the","strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly.","The evaluation metric name to optimize. e.g. \"mse\" :param logs_dir: Local directory to","Model. User need to specify either the exact value or the search space","location you want to load the best model. \"\"\" self.best_model.restore(checkpoint_file) def get_best_model(self): \"\"\"","hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10).","class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\",","the process. :param expect_horizon: int, validation data will be automatically splited from training","the specific language governing permissions and # limitations under the License. # import","hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10).","None\") if self.best_model.model is None: raise RuntimeError( \"You must call fit or restore","terminated when metric threshold is met :param n_sampling: Number of trials to evaluate","!= -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler,","expect_horizon: int, validation data will be automatically splited from training data, and expect_horizon","\"You must call fit or restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self,","seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ): \"\"\" Create an","it defaults to \"hdfs:///tmp/{name}\". :param load_dir: Load the ckpt from load_dir. The value","2 columns, with column 'ds' indicating date and column 'y' indicating value and","frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted",":param expect_horizon: int, validation data will be automatically splited from training data, and","-1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) #","\"The 'ds' col should be in datetime 64 type, or you need to","None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1","you may need to use once the mode is fitted. The value defaults","anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where","using the best model after HPO. :param horizon: the number of steps forward","False to speed up the process. :param expect_horizon: int, validation data will be","of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be","# - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\",","10% of training data will be taken as the validation data. :param freq:","Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version","Version 2.0 (the \"License\"); # you may not use this file except in","model hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param","to sync training results and checkpoints. It defaults to None and doesn't take","except in compliance with the License. # You may obtain a copy of","training dataframe should contains more than 2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds'","data, a pandas dataframe with Td rows, and 2 columns, with column 'ds'","dimension :param metrics: A list contains metrics for test/valid data. \"\"\" if data","the mode is fitted. The value defaults to None, where 10% of training","pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in datetime 64 type, or you","hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range","It defaults to 1. :param name: name of the AutoProphet. It defaults to","expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the","fit or restore first before calling predict!\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self,","n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space,","It defaults to None and doesn't take effects while running in local. While","and results. It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for","call fit or restore first before calling save!\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \"\"\"","recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse',","met. :param search_alg: str, all supported searcher provided by ray tune (i.e.\"variant_generator\", \"random\",","seasonality_mode, \"changepoint_range\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) #","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\"D\", ds_data=None): \"\"\" Predict using","from cross_validation. The value is set to True by default. Setting this option","to optimize. e.g. \"mse\" :param logs_dir: Local directory to save logs and results.",":param checkpoint_file: The location you want to save the best model, should be","evaluation data, a pandas dataframe with Td rows, and 2 columns, with column","to None and doesn't take effects while running in local. While running in","\"\"\" Restore the best model after HPO. :param checkpoint_file: The checkpoint file location","'y' indicating value and Td is the time dimension :param cross_validation: bool, if","time dimension :param cross_validation: bool, if the eval result comes from cross_validation. The","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' #","and \"sigopt\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and","limitations under the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet","for scheduler \"\"\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is","train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else","may need to use once the mode is fitted. The value defaults to","\"changepoint_prior_scale\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01,","training dataframe. the frequency can be anything from the pandas list of frequency","fit.\" self._freq = data[\"ds\"].iloc[1] - data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq","freqency of the predicted dataframe, defaulted to day(\"D\"), the frequency can be anything","self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best model after HPO. :param","from training data, and expect_horizon is the horizon you may need to use","the search space of the Prophet model hyperparameters. For details of the Prophet","changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\":","= ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You","hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \"holidays_prior_scale\": hp.loguniform(0.01, 10)","that has 1 column 'ds' indicating date. \"\"\" if self.best_model.model is None: raise",":param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try:","type, or you need to set `freq` in fit.\" self._freq = data[\"ds\"].iloc[1] -","data[\"ds\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\"expect_horizon\": expect_horizon_str, \"cross_validation\":","and searcher mode :param scheduler: str, all supported scheduler provided by ray tune","defaults to None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model =","the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet","It defaults to \"/tmp/auto_prophet_logs\" :param cpus_per_trial: Int. Number of cpus for each trial.","data will be taken as the validation data. :param freq: the freqency of","search_space, the grid will be run n_sampling of trials and round up n_sampling",":param ds_data: a dataframe that has 1 column 'ds' indicating date. \"\"\" if","0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \"seasonality_prior_scale\": hp.grid_search([0.01, 0.1, 1.0, 10.0])","extra parameters for searcher algorithm besides search_space, metric and searcher mode :param scheduler:","None: raise RuntimeError( \"You must call fit or restore first before calling predict!\")","\"\"\" Evaluate using the best model after HPO. :param data: evaluation data, a","before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \"\"\" Save the best","tune (i.e.\"variant_generator\", \"random\", \"ax\", \"dragonfly\", \"skopt\", \"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and","met :param n_sampling: Number of trials to evaluate in total. Defaults to 16.","For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale:","the grid will be run n_sampling of trials and round up n_sampling according","invalid data of None\") if self.best_model.model is None: raise RuntimeError( \"You must call","ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None,","for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet","# update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder,","logs_dir=logs_dir, resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to","\"cross_validation\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if","either the exact value or the search space of the Prophet model hyperparameters.","the model and search for the best hyperparameters. :param data: training data, a","defaults to 1. :param name: name of the AutoProphet. It defaults to \"auto_prophet\"","the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001,","resources_per_trial={\"cpu\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\"You need to install `bigdl-orca[automl]` to use","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","you want to save the best model, should be a json file \"\"\"","the AutoProphet. It defaults to \"auto_prophet\" :param remote_dir: String. Remote directory to sync","\"hyperopt\", \"bayesopt\", \"bohb\", \"nevergrad\", \"optuna\", \"zoopt\" and \"sigopt\") :param search_alg_params: extra parameters for","value defaults to None. :param prophet_config: Other Prophet hyperparameters. \"\"\" if load_dir: self.best_model","to 1. :param name: name of the AutoProphet. It defaults to \"auto_prophet\" :param","\"You must call fit or restore first before calling evaluate!\") return self.best_model.evaluate(target=data, metrics=metrics)","cpus for each trial. It defaults to 1. :param name: name of the","changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\"/tmp/auto_prophet_logs\", cpus_per_trial=1, name=\"auto_prophet\", remote_dir=None, load_dir=None, **prophet_config ):","from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None,","def evaluate(self, data, metrics=['mse']): \"\"\" Evaluate using the best model after HPO. :param","2 records.\" assert pd.api.types.is_datetime64_any_dtype(data[\"ds\"].dtypes), \\ \"The 'ds' col should be in datetime 64","search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \"\"\" Automatically fit the model and search for"],"string":"[\n \"test/valid data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid data of None\\\")\",\n \"save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after HPO. :param checkpoint_file: The location\",\n \"OR CONDITIONS OF ANY KIND, either exp' # ress or implied. # See\",\n \"sync training results and checkpoints. It defaults to None and doesn't take effects\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric\",\n \"if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg,\",\n \":param metrics: A list contains metrics for test/valid data. \\\"\\\"\\\" if data is\",\n \"by default. Setting this option to False to speed up the process. :param\",\n \"a pandas dataframe with Td rows, and 2 columns, with column 'ds' indicating\",\n \"expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2, \\\"The training\",\n \"The location you want to save the best model, should be a json\",\n \"RuntimeError( \\\"You must call fit or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon,\",\n \"unreliable frequency will be infer implicitly. :param metric_threshold: a trial will be terminated\",\n \"expect_horizon is the horizon you may need to use once the mode is\",\n \"if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp\",\n \"cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1. :param\",\n \"training data, and expect_horizon is the horizon you may need to use once\",\n \"the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best Prophet model.\",\n \"total. Defaults to 16. If hp.grid_search is in search_space, the grid will be\",\n \"is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode,\",\n \":param metric: String. The evaluation metric name to optimize. e.g. \\\"mse\\\" :param logs_dir:\",\n \"0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs\",\n \"predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model after HPO. :param\",\n \"cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir. The\",\n \"freq is None: assert len(data) >= 2, \\\"The training dataframe should contains more\",\n \"Predict using the best model after HPO. :param horizon: the number of steps\",\n \"checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO. :param checkpoint_file: The checkpoint file\",\n \"changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation\",\n \"a stopping condition is met. :param search_alg: str, all supported searcher provided by\",\n \"validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling\",\n \"model after HPO. :param data: evaluation data, a pandas dataframe with Td rows,\",\n \"local. While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the\",\n \"# ress or implied. # See the License for the specific language governing\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"\\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model\",\n \"is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2,\",\n \"pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import\",\n \"the time dimension :param cross_validation: bool, if the eval result comes from cross_validation.\",\n \"bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale\",\n \"else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale,\",\n \"to save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number\",\n \"searcher mode :param scheduler: str, all supported scheduler provided by ray tune :param\",\n \"`freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str\",\n \"- class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1,\",\n \"def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after HPO. :param checkpoint_file: The\",\n \"up the process. :param expect_horizon: int, validation data will be automatically splited from\",\n \"String. Remote directory to sync training results and checkpoints. It defaults to None\",\n \"None, where 10% of training data will be taken as the validation data.\",\n \"cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space,\",\n \"a trial will be terminated when metric threshold is met :param n_sampling: Number\",\n \"in search_space, the grid will be run n_sampling of trials and round up\",\n \"search_space, metric and searcher mode :param scheduler: str, all supported scheduler provided by\",\n \"Td rows, and 2 columns, with column 'ds' indicating date and column 'y'\",\n \"Load the ckpt from load_dir. The value defaults to None. :param prophet_config: Other\",\n \"n_sampling: Number of trials to evaluate in total. Defaults to 16. If hp.grid_search\",\n \"\\\"\\\"\\\" Automatically fit the model and search for the best hyperparameters. :param data:\",\n \"calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO.\",\n \"directory to sync training results and checkpoints. It defaults to None and doesn't\",\n \"to fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data)\",\n \"self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est =\",\n \"None: assert len(data) >= 2, \\\"The training dataframe should contains more than 2\",\n \"refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer\",\n \"e.g. \\\"mse\\\" :param logs_dir: Local directory to save logs and results. It defaults\",\n \"from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an\",\n \"pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency\",\n \"cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model. User\",\n \"AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]`\",\n \"the best model, should be a json file \\\"\\\"\\\" if self.best_model.model is None:\",\n \"\\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if\",\n \"search for the best hyperparameters. :param data: training data, a pandas dataframe with\",\n \"up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples are\",\n \"self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq *\",\n \"best config to fit a new prophet model on whole data self.best_model =\",\n \"pandas dataframe with Td rows, and 2 columns, with column 'ds' indicating date\",\n \"cross_validation. The value is set to True by default. Setting this option to\",\n \"is the horizon you may need to use once the mode is fitted.\",\n \"the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling\",\n \"logs_dir: Local directory to save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param\",\n \"trial will be terminated when metric threshold is met :param n_sampling: Number of\",\n \"n_sampling of trials and round up n_sampling according to hp.grid_search. If this is\",\n \"metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config\",\n \"The checkpoint file location you want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file)\",\n \"of None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or\",\n \"training results and checkpoints. It defaults to None and doesn't take effects while\",\n \"self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the\",\n \"and checkpoints. It defaults to None and doesn't take effects while running in\",\n \"HPO. :param data: evaluation data, a pandas dataframe with Td rows, and 2\",\n \":param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode:\",\n \"holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95)\",\n \"the best model after HPO. :param horizon: the number of steps forward to\",\n \"import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1,\",\n \"None and doesn't take effects while running in local. While running in cluster,\",\n \"ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\"\",\n \"be a json file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must\",\n \"for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for\",\n \"\\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if\",\n \"License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel\",\n \"cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit`\",\n \"cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation\",\n \"if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling =\",\n \"to speed up the process. :param expect_horizon: int, validation data will be automatically\",\n \"use the best config to fit a new prophet model on whole data\",\n \"predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can be anything from the pandas\",\n \":param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale:\",\n \"if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale\",\n \"permissions and # limitations under the License. # import pandas as pd import\",\n \"def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None,\",\n \"metrics: A list contains metrics for test/valid data. \\\"\\\"\\\" if data is None:\",\n \"2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0\",\n \"\\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm besides search_space,\",\n \"configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial},\",\n \"ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data,\",\n \"horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model after HPO. :param horizon:\",\n \"frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer\",\n \"best model after HPO. :param horizon: the number of steps forward to predict\",\n \"to day(\\\"D\\\"), the frequency can be anything from the pandas list of frequency\",\n \"Local directory to save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial:\",\n \"of the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can be anything from\",\n \"HPO. :param checkpoint_file: The location you want to save the best model, should\",\n \"governing permissions and # limitations under the License. # import pandas as pd\",\n \"self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data\",\n \"checkpoint_file: The location you want to save the best model, should be a\",\n \"Setting this option to False to speed up the process. :param expect_horizon: int,\",\n \"for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the\",\n \"supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\",\",\n \"= ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space\",\n \"search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and search for the\",\n \":param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range:\",\n \"training data will be taken as the validation data. :param freq: the freqency\",\n \"Td is the time dimension :param cross_validation: bool, if the eval result comes\",\n \"the ckpt from load_dir. The value defaults to None. :param prophet_config: Other Prophet\",\n \"\\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters\",\n \"The value defaults to None, where 10% of training data will be taken\",\n \"and Td is the time dimension :param metrics: A list contains metrics for\",\n \"call fit or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self,\",\n \"stopping condition is met. :param search_alg: str, all supported searcher provided by ray\",\n \"calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using\",\n \":param scheduler: str, all supported scheduler provided by ray tune :param scheduler_params: parameters\",\n \"to specify either the exact value or the search space of the Prophet\",\n \"= ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best\",\n \"the best model after HPO. :param checkpoint_file: The checkpoint file location you want\",\n \"name: name of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote\",\n \"scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and search for the best hyperparameters.\",\n \"the Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to\",\n \"config to fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config())\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly. :param\",\n \"of steps forward to predict :param freq: the freqency of the predicted dataframe,\",\n \"model after HPO. :param checkpoint_file: The checkpoint file location you want to load\",\n \"None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >= 2, \\\"The\",\n \"Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator\",\n \"hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator\",\n \"evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after\",\n \"\\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm besides\",\n \"forward to predict :param freq: the freqency of the predicted dataframe, defaulted to\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp'\",\n \"restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save\",\n \"date and column 'y' indicating value and Td is the time dimension :param\",\n \"validation data. :param freq: the freqency of the training dataframe. the frequency can\",\n \"2, \\\"The training dataframe should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\\",\n \"model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize.\",\n \"dataframe that has 1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None:\",\n \"'ds' col should be in datetime 64 type, or you need to set\",\n \"predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the\",\n \"e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive',\",\n \":param data: training data, a pandas dataframe with Td rows, and 2 columns,\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"Authors. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\":\",\n \"searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\",\",\n \"hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None\",\n \"hp sampling function from an integer space for hyperparameter changepoint_prior_scale for the Prophet\",\n \"be run n_sampling of trials and round up n_sampling according to hp.grid_search. If\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"len(data) >= 2, \\\"The training dataframe should contains more than 2 records.\\\" assert\",\n \"It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync training results\",\n \"predict :param freq: the freqency of the predicted dataframe, defaulted to day(\\\"D\\\"), the\",\n \"data of None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You must call fit\",\n \"raise RuntimeError( \\\"You must call fit or restore first before calling evaluate!\\\") return\",\n \"seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\"\",\n \":param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.\",\n \"None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder\",\n \"= str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation\",\n \"import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils\",\n \"and expect_horizon is the horizon you may need to use once the mode\",\n \":param freq: the freqency of the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency\",\n \"0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if\",\n \"freq: the freqency of the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can\",\n \"changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details.\",\n \"metric name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to save logs\",\n \"holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else\",\n \"None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\":\",\n \"data, and expect_horizon is the horizon you may need to use once the\",\n \"data: training data, a pandas dataframe with Td rows, and 2 columns, with\",\n \"with Td rows, and 2 columns, with column 'ds' indicating date and column\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"list contains metrics for test/valid data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input\",\n \"raise RuntimeError( \\\"You must call fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file)\",\n \"hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter\",\n \"of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to\",\n \"freqency of the training dataframe. the frequency can be anything from the pandas\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0,\",\n \"ds_data=None): \\\"\\\"\\\" Predict using the best model after HPO. :param horizon: the number\",\n \"'y' indicating value and Td is the time dimension :param metrics: A list\",\n \"for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g.\",\n \"use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None,\",\n \"10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is\",\n \"restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best\",\n \"search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher mode :param\",\n \"results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for each\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or\",\n \"will be automatically splited from training data, and expect_horizon is the horizon you\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"language governing permissions and # limitations under the License. # import pandas as\",\n \"changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric =\",\n \"first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model\",\n \"hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range\",\n \"save the best model, should be a json file \\\"\\\"\\\" if self.best_model.model is\",\n \"data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically\",\n \"model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best Prophet model. \\\"\\\"\\\" return\",\n \"mode :param scheduler: str, all supported scheduler provided by ray tune :param scheduler_params:\",\n \"AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None,\",\n \"mode is fitted. The value defaults to None, where 10% of training data\",\n \"is None: assert len(data) >= 2, \\\"The training dataframe should contains more than\",\n \"Create an automated Prophet Model. User need to specify either the exact value\",\n \"will be infer implicitly. :param metric_threshold: a trial will be terminated when metric\",\n \"expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if\",\n \"new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1,\",\n \"install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None,\",\n \"records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in datetime 64 type,\",\n \"set to True by default. Setting this option to False to speed up\",\n \"best model after HPO. :param checkpoint_file: The location you want to save the\",\n \"import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self,\",\n \"else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive',\",\n \"splited from training data, and expect_horizon is the horizon you may need to\",\n \"to 16. If hp.grid_search is in search_space, the grid will be run n_sampling\",\n \"for the best hyperparameters. :param data: training data, a pandas dataframe with Td\",\n \"else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data,\",\n \":param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher mode\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"(virtually) infinite samples are generated until a stopping condition is met. :param search_alg:\",\n \"evaluation metric name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to save\",\n \"# Copyright 2016 The BigDL Authors. # # Licensed under the Apache License,\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"under the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import\",\n \"`bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16,\",\n \"data: evaluation data, a pandas dataframe with Td rows, and 2 columns, with\",\n \"the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet\",\n \"file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or\",\n \"seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter\",\n \"bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the\",\n \"metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and\",\n \"ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after HPO.\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating date. \\\"\\\"\\\"\",\n \"the time dimension :param metrics: A list contains metrics for test/valid data. \\\"\\\"\\\"\",\n \"License. # You may obtain a copy of the License at # #\",\n \"self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use\",\n \"from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe\",\n \":param name: name of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String.\",\n \"'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call\",\n \"automatically splited from training data, and expect_horizon is the horizon you may need\",\n \"self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir,\",\n \"'ds' indicating date and column 'y' indicating value and Td is the time\",\n \"Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function\",\n \"will be terminated when metric threshold is met :param n_sampling: Number of trials\",\n \"fit a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def\",\n \"will be run n_sampling of trials and round up n_sampling according to hp.grid_search.\",\n \"assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in datetime 64 type, or\",\n \"seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for\",\n \"need to use once the mode is fitted. The value defaults to None,\",\n \"freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model after HPO. :param horizon: the\",\n \"except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self,\",\n \"Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.\",\n \"https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer space for\",\n \"the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to save logs and\",\n \"hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize. e.g. \\\"mse\\\"\",\n \"None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\":\",\n \"or you need to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0]\",\n \"\\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm\",\n \"column 'y' indicating value and Td is the time dimension :param metrics: A\",\n \"+ # # Copyright 2016 The BigDL Authors. # # Licensed under the\",\n \":param freq: the freqency of the training dataframe. the frequency can be anything\",\n \"ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model\",\n \"if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None\",\n \"where 10% of training data will be taken as the validation data. :param\",\n \"hp.grid_search is in search_space, the grid will be run n_sampling of trials and\",\n \"be in datetime 64 type, or you need to set `freq` in fit.\\\"\",\n \"best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best Prophet model. \\\"\\\"\\\"\",\n \"dataframe with Td rows, and 2 columns, with column 'ds' indicating date and\",\n \"see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for\",\n \"hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1,\",\n \":param logs_dir: Local directory to save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\"\",\n \"= { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale,\",\n \"day(\\\"D\\\"), the frequency can be anything from the pandas list of frequency strings\",\n \"restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']):\",\n \"list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1\",\n \"or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\"\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO. :param checkpoint_file: The\",\n \"take effects while running in local. While running in cluster, it defaults to\",\n \"effects while running in local. While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\".\",\n \"trial. It defaults to 1. :param name: name of the AutoProphet. It defaults\",\n \"self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress\",\n \"\\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import\",\n \"str, all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\",\",\n \"= data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon)\",\n \"n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params,\",\n \"Evaluate using the best model after HPO. :param data: evaluation data, a pandas\",\n \"data is None: raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is None:\",\n \"from load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\"\",\n \":param changepoint_prior_scale: Int or hp sampling function from an integer space for hyperparameter\",\n \"ANY KIND, either exp' # ress or implied. # See the License for\",\n \"ress or implied. # See the License for the specific language governing permissions\",\n \"bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05,\",\n \"be infer implicitly. :param metric_threshold: a trial will be terminated when metric threshold\",\n \"of the Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer\",\n \"result comes from cross_validation. The value is set to True by default. Setting\",\n \"you need to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else:\",\n \"model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError:\",\n \"-1, (virtually) infinite samples are generated until a stopping condition is met. :param\",\n \"\\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra\",\n \"frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds'\",\n \"indicating value and Td is the time dimension :param metrics: A list contains\",\n \"Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to\",\n \"and column 'y' indicating value and Td is the time dimension :param metrics:\",\n \"of the training dataframe. the frequency can be anything from the pandas list\",\n \"= int(0.1*len(data)) if freq is None: assert len(data) >= 2, \\\"The training dataframe\",\n \"training data, a pandas dataframe with Td rows, and 2 columns, with column\",\n \"and 2 columns, with column 'ds' indicating date and column 'y' indicating value\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or implied.\",\n \"of cpus for each trial. It defaults to 1. :param name: name of\",\n \"0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if\",\n \"not use this file except in compliance with the License. # You may\",\n \"else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder =\",\n \"json file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call fit\",\n \"you want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get\",\n \"e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01,\",\n \"anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a\",\n \"bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def\",\n \"location you want to save the best model, should be a json file\",\n \"self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp\",\n \"and doesn't take effects while running in local. While running in cluster, it\",\n \"if the eval result comes from cross_validation. The value is set to True\",\n \"indicating value and Td is the time dimension :param cross_validation: bool, if the\",\n \"data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid data of None\\\") if\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params )\",\n \"remote_dir: String. Remote directory to sync training results and checkpoints. It defaults to\",\n \"changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder()\",\n \"prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from\",\n \"each trial. It defaults to 1. :param name: name of the AutoProphet. It\",\n \"expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None\",\n \"def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ):\",\n \"scheduler: str, all supported scheduler provided by ray tune :param scheduler_params: parameters for\",\n \"first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\"\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated\",\n \"expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data\",\n \"fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore\",\n \"restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO. :param checkpoint_file: The checkpoint\",\n \"if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None\",\n \"calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model\",\n \"to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir. The value defaults to\",\n \"and Td is the time dimension :param cross_validation: bool, if the eval result\",\n \"Save the best model after HPO. :param checkpoint_file: The location you want to\",\n \"more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model.\",\n \"indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call fit\",\n \"condition is met. :param search_alg: str, all supported searcher provided by ray tune\",\n \"model after HPO. :param horizon: the number of steps forward to predict :param\",\n \"# you may not use this file except in compliance with the License.\",\n \"defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir. The value defaults\",\n \"hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']).\",\n \"n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples are generated\",\n \"scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a new prophet\",\n \"seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative'])\",\n \"cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit\",\n \"in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir.\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"= metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name)\",\n \"} self.search_space.update(prophet_config) # update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est\",\n \"seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for\",\n \"The value is set to True by default. Setting this option to False\",\n \"metric and searcher mode :param scheduler: str, all supported scheduler provided by ray\",\n \"\\\"\\\"\\\" Save the best model after HPO. :param checkpoint_file: The location you want\",\n \"value defaults to None, where 10% of training data will be taken as\",\n \"the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable\",\n \"be taken as the validation data. :param freq: the freqency of the training\",\n \"seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need\",\n \"# import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from\",\n \"64 type, or you need to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1]\",\n \"round up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite samples\",\n \"model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g.\",\n \"model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param\",\n \"list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will\",\n \"holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter\",\n \"implicitly. :param metric_threshold: a trial will be terminated when metric threshold is met\",\n \"datetime 64 type, or you need to set `freq` in fit.\\\" self._freq =\",\n \"is set to True by default. Setting this option to False to speed\",\n \"checkpoint_file): \\\"\\\"\\\" Save the best model after HPO. :param checkpoint_file: The location you\",\n \"exp' # ress or implied. # See the License for the specific language\",\n \"space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp\",\n \"of training data will be taken as the validation data. :param freq: the\",\n \"in datetime 64 type, or you need to set `freq` in fit.\\\" self._freq\",\n \"\\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update\",\n \"hyperparameters. :param data: training data, a pandas dataframe with Td rows, and 2\",\n \"self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO. :param checkpoint_file:\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"dataframe, defaulted to day(\\\"D\\\"), the frequency can be anything from the pandas list\",\n \"integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see\",\n \"is fitted. The value defaults to None, where 10% of training data will\",\n \"def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model after HPO.\",\n \"is the time dimension :param metrics: A list contains metrics for test/valid data.\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"\\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric\",\n \"metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after HPO. :param checkpoint_file:\",\n \"best model after HPO. :param checkpoint_file: The checkpoint file location you want to\",\n \"The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir:\",\n \"self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data =\",\n \"rows, and 2 columns, with column 'ds' indicating date and column 'y' indicating\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"all supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\"\",\n \"name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit` function.\\\") def\",\n \"indicating date and column 'y' indicating value and Td is the time dimension\",\n \"frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases\",\n \"data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after HPO. :param data: evaluation\",\n \"defaults to None, where 10% of training data will be taken as the\",\n \"the best config to fit a new prophet model on whole data self.best_model\",\n \"as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling\",\n \"n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to\",\n \"\\\"You must call fit or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq,\",\n \"best hyperparameters. :param data: training data, a pandas dataframe with Td rows, and\",\n \"file location you want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self):\",\n \"name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model. User need\",\n \"for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric\",\n \"need to specify either the exact value or the search space of the\",\n \"self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to\",\n \"a new prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self,\",\n \":param cross_validation: bool, if the eval result comes from cross_validation. The value is\",\n \"after HPO. :param checkpoint_file: The checkpoint file location you want to load the\",\n \"evaluate in total. Defaults to 16. If hp.grid_search is in search_space, the grid\",\n \"freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"\\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params:\",\n \"is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range\",\n \"and # limitations under the License. # import pandas as pd import warnings\",\n \"want to save the best model, should be a json file \\\"\\\"\\\" if\",\n \"else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling)\",\n \"column 'y' indicating value and Td is the time dimension :param cross_validation: bool,\",\n \"must call fit or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data)\",\n \"update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir,\",\n \"value is set to True by default. Setting this option to False to\",\n \"provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\",\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"\\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param\",\n \"= recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold,\",\n \"None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else\",\n \"in local. While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load\",\n \"load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model. User need to specify\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model.\",\n \"hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale\",\n \"hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95).\",\n \"metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet\",\n \"If this is -1, (virtually) infinite samples are generated until a stopping condition\",\n \"search_alg: str, all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\",\",\n \"str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else\",\n \"horizon: the number of steps forward to predict :param freq: the freqency of\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or restore first\",\n \"function from an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005,\",\n \"scheduler_params=scheduler_params ) # use the best config to fit a new prophet model\",\n \"expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data) >=\",\n \"before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"the eval result comes from cross_validation. The value is set to True by\",\n \"until a stopping condition is met. :param search_alg: str, all supported searcher provided\",\n \"to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel()\",\n \"for searcher algorithm besides search_space, metric and searcher mode :param scheduler: str, all\",\n \"hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an\",\n \"hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String. The\",\n \"specify either the exact value or the search space of the Prophet model\",\n \"bool, if the eval result comes from cross_validation. The value is set to\",\n \"option to False to speed up the process. :param expect_horizon: int, validation data\",\n \"of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column\",\n \"than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in datetime\",\n \"seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter\",\n \"scheduler provided by ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon\",\n \"want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the\",\n \"to hp.grid_search. If this is -1, (virtually) infinite samples are generated until a\",\n \"= AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install\",\n \"to None, where an unreliable frequency will be infer implicitly. :param metric_threshold: a\",\n \"hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more\",\n \"{ \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\":\",\n \"be terminated when metric threshold is met :param n_sampling: Number of trials to\",\n \"the best model after HPO. :param data: evaluation data, a pandas dataframe with\",\n \"None: raise RuntimeError( \\\"You must call fit or restore first before calling evaluate!\\\")\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"of trials and round up n_sampling according to hp.grid_search. If this is -1,\",\n \"load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best Prophet\",\n \"or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the\",\n \"Prophet Model. User need to specify either the exact value or the search\",\n \"metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None,\",\n \"Automatically fit the model and search for the best hyperparameters. :param data: training\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should\",\n \"RuntimeError( \\\"You must call fit or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data,\",\n \"ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon\",\n \"of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp\",\n \"details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g.\",\n \"specific language governing permissions and # limitations under the License. # import pandas\",\n \"function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None,\",\n \"contains metrics for test/valid data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale\",\n \"if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"a json file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call\",\n \"metric threshold is met :param n_sampling: Number of trials to evaluate in total.\",\n \"load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as\",\n \"model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g.\",\n \"according to hp.grid_search. If this is -1, (virtually) infinite samples are generated until\",\n \"hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode\",\n \"contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be\",\n \"None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range }\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"and column 'y' indicating value and Td is the time dimension :param cross_validation:\",\n \"for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the\",\n \"Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model.\",\n \"ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You must\",\n \"col should be in datetime 64 type, or you need to set `freq`\",\n \"save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\" Restore the best model after HPO. :param\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either exp' # ress or implied. #\",\n \"either exp' # ress or implied. # See the License for the specific\",\n \"search space of the Prophet model hyperparameters. For details of the Prophet model\",\n \"\\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher\",\n \"search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit\",\n \"ds_data: a dataframe that has 1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model\",\n \"after HPO. :param checkpoint_file: The location you want to save the best model,\",\n \"date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or\",\n \"OF ANY KIND, either exp' # ress or implied. # See the License\",\n \"try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\":\",\n \"e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8,\",\n \"model. e.g. hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g.\",\n \"doesn't take effects while running in local. While running in cluster, it defaults\",\n \"the validation data. :param freq: the freqency of the training dataframe. the frequency\",\n \"import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None,\",\n \"the freqency of the training dataframe. the frequency can be anything from the\",\n \"validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the\",\n \"A list contains metrics for test/valid data. \\\"\\\"\\\" if data is None: raise\",\n \"if data is None: raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is\",\n \"If hp.grid_search is in search_space, the grid will be run n_sampling of trials\",\n \"int, validation data will be automatically splited from training data, and expect_horizon is\",\n \"`fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None,\",\n \"model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None):\",\n \"\\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync training results and checkpoints. It\",\n \"infer implicitly. :param metric_threshold: a trial will be terminated when metric threshold is\",\n \"remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model. User need to\",\n \"0.95). :param metric: String. The evaluation metric name to optimize. e.g. \\\"mse\\\" :param\",\n \"True by default. Setting this option to False to speed up the process.\",\n \"None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir)\",\n \"defaults to None and doesn't take effects while running in local. While running\",\n \"the best model after HPO. :param checkpoint_file: The location you want to save\",\n \"must call fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file):\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"you may not use this file except in compliance with the License. #\",\n \":param n_sampling: Number of trials to evaluate in total. Defaults to 16. If\",\n \"fit the model and search for the best hyperparameters. :param data: training data,\",\n \"dimension :param cross_validation: bool, if the eval result comes from cross_validation. The value\",\n \"grid will be run n_sampling of trials and round up n_sampling according to\",\n \"model, should be a json file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError(\",\n \"model after HPO. :param checkpoint_file: The location you want to save the best\",\n \"here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating date.\",\n \"model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from\",\n \"changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is\",\n \"horizon you may need to use once the mode is fitted. The value\",\n \"scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None:\",\n \"exact value or the search space of the Prophet model hyperparameters. For details\",\n \"return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best\",\n \"number of steps forward to predict :param freq: the freqency of the predicted\",\n \"\\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults to\",\n \"\\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or restore\",\n \"to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best\",\n \"fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\"\",\n \"logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model.\",\n \"e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01,\",\n \"are generated until a stopping condition is met. :param search_alg: str, all supported\",\n \"freq: the freqency of the training dataframe. the frequency can be anything from\",\n \"KIND, either exp' # ress or implied. # See the License for the\",\n \"use once the mode is fitted. The value defaults to None, where 10%\",\n \"ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space =\",\n \"before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the\",\n \"to None, where 10% of training data will be taken as the validation\",\n \"comes from cross_validation. The value is set to True by default. Setting this\",\n \"where an unreliable frequency will be infer implicitly. :param metric_threshold: a trial will\",\n \"infinite samples are generated until a stopping condition is met. :param search_alg: str,\",\n \"is in search_space, the grid will be run n_sampling of trials and round\",\n \"metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best\",\n \"if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None\",\n \"on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\"\",\n \"RuntimeError( \\\"You must call fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def\",\n \"Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5).\",\n \":param search_alg: str, all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\",\",\n \"CONDITIONS OF ANY KIND, either exp' # ress or implied. # See the\",\n \"import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if\",\n \"will be taken as the validation data. :param freq: the freqency of the\",\n \"scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and search for the best\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"run n_sampling of trials and round up n_sampling according to hp.grid_search. If this\",\n \"besides search_space, metric and searcher mode :param scheduler: str, all supported scheduler provided\",\n \"cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1\",\n \"metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after HPO. :param data: evaluation data,\",\n \"checkpoint_file: The checkpoint file location you want to load the best model. \\\"\\\"\\\"\",\n \"changepoint_prior_scale: Int or hp sampling function from an integer space for hyperparameter changepoint_prior_scale\",\n \"holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for\",\n \"load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if\",\n \"): \\\"\\\"\\\" Create an automated Prophet Model. User need to specify either the\",\n \"to True by default. Setting this option to False to speed up the\",\n \"# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache\",\n \"evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after HPO. :param data:\",\n \"While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt\",\n \"e.g. hp.uniform(0.8, 0.95). :param metric: String. The evaluation metric name to optimize. e.g.\",\n \"or hp sampling function from an integer space for hyperparameter changepoint_prior_scale for the\",\n \"speed up the process. :param expect_horizon: int, validation data will be automatically splited\",\n \"pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has\",\n \"samples are generated until a stopping condition is met. :param search_alg: str, all\",\n \"for each trial. It defaults to 1. :param name: name of the AutoProphet.\",\n \"the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \">= 2, \\\"The training dataframe should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes),\",\n \"remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit` function.\\\")\",\n \"be automatically splited from training data, and expect_horizon is the horizon you may\",\n \"value and Td is the time dimension :param metrics: A list contains metrics\",\n \"return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after HPO.\",\n \"when metric threshold is met :param n_sampling: Number of trials to evaluate in\",\n \"\\\"The training dataframe should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The\",\n \"as the validation data. :param freq: the freqency of the training dataframe. the\",\n \"None, where an unreliable frequency will be infer implicitly. :param metric_threshold: a trial\",\n \"pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling #\",\n \"for the specific language governing permissions and # limitations under the License. #\",\n \"HPO. :param checkpoint_file: The checkpoint file location you want to load the best\",\n \"int(0.1*len(data)) if freq is None: assert len(data) >= 2, \\\"The training dataframe should\",\n \"is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None\",\n \"else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config)\",\n \"dataframe should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col\",\n \"seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else\",\n \"a dataframe that has 1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is\",\n \"data. :param freq: the freqency of the training dataframe. the frequency can be\",\n \"using the best model after HPO. :param data: evaluation data, a pandas dataframe\",\n \"parameters for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq\",\n \"# use the best config to fit a new prophet model on whole\",\n \"The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the\",\n \"trials and round up n_sampling according to hp.grid_search. If this is -1, (virtually)\",\n \"self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model\",\n \"running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from\",\n \"10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param\",\n \"\\\"\\\"\\\" Create an automated Prophet Model. User need to specify either the exact\",\n \"dataframe. the frequency can be anything from the pandas list of frequency strings\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param\",\n \"n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and search\",\n \"fitted. The value defaults to None, where 10% of training data will be\",\n \"be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None,\",\n \"the frequency can be anything from the pandas list of frequency strings here:\",\n \"changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric: String.\",\n \"* expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon]\",\n \"= pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data =\",\n \"the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can be anything from the\",\n \"best model, should be a json file \\\"\\\"\\\" if self.best_model.model is None: raise\",\n \"Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet model.\",\n \"steps forward to predict :param freq: the freqency of the predicted dataframe, defaulted\",\n \"optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to save logs and results. It\",\n \"metrics for test/valid data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid data\",\n \"this option to False to speed up the process. :param expect_horizon: int, validation\",\n \"self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using the best model after\",\n \"and round up n_sampling according to hp.grid_search. If this is -1, (virtually) infinite\",\n \"all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\",\",\n \"is None: raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is None: raise\",\n \"should be a json file \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You\",\n \"as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is\",\n \"this is -1, (virtually) infinite samples are generated until a stopping condition is\",\n \"taken as the validation data. :param freq: the freqency of the training dataframe.\",\n \"need to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq\",\n \"hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other\",\n \"is met. :param search_alg: str, all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\",\",\n \"process. :param expect_horizon: int, validation data will be automatically splited from training data,\",\n \":param load_dir: Load the ckpt from load_dir. The value defaults to None. :param\",\n \"must call fit or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def\",\n \"to save the best model, should be a json file \\\"\\\"\\\" if self.best_model.model\",\n \"the training dataframe. the frequency can be anything from the pandas list of\",\n \"to evaluate in total. Defaults to 16. If hp.grid_search is in search_space, the\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"metric: String. The evaluation metric name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local\",\n \"data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data,\",\n \"else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8,\",\n \"- data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str,\",\n \"automated Prophet Model. User need to specify either the exact value or the\",\n \"generated until a stopping condition is met. :param search_alg: str, all supported searcher\",\n \"data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\":\",\n \"prophet model on whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\",\",\n \"value or the search space of the Prophet model hyperparameters. For details of\",\n \":param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data))\",\n \"is the time dimension :param cross_validation: bool, if the eval result comes from\",\n \"after HPO. :param data: evaluation data, a pandas dataframe with Td rows, and\",\n \"strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data: a dataframe that has 1 column 'ds' indicating\",\n \"to use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None,\",\n \"default. Setting this option to False to speed up the process. :param expect_horizon:\",\n \"Defaults to 16. If hp.grid_search is in search_space, the grid will be run\",\n \"need to install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None,\",\n \"time dimension :param metrics: A list contains metrics for test/valid data. \\\"\\\"\\\" if\",\n \"Restore the best model after HPO. :param checkpoint_file: The checkpoint file location you\",\n \"threshold is met :param n_sampling: Number of trials to evaluate in total. Defaults\",\n \"if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert len(data)\",\n \"from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet:\",\n \"= data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:]\",\n \"warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class\",\n \"'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param\",\n \"https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly. :param metric_threshold:\",\n \"supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if\",\n \"the License. # You may obtain a copy of the License at #\",\n \"is -1, (virtually) infinite samples are generated until a stopping condition is met.\",\n \"ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None,\",\n \"pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation}) train_data = data\",\n \"details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or\",\n \"'multiplicative']) if seasonality_mode is None else seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is\",\n \"load_dir: Load the ckpt from load_dir. The value defaults to None. :param prophet_config:\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"None: raise RuntimeError( \\\"You must call fit or restore first before calling save!\\\")\",\n \"**prophet_config ): \\\"\\\"\\\" Create an automated Prophet Model. User need to specify either\",\n \"best model after HPO. :param data: evaluation data, a pandas dataframe with Td\",\n \"10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode is\",\n \":param data: evaluation data, a pandas dataframe with Td rows, and 2 columns,\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"Int. Number of cpus for each trial. It defaults to 1. :param name:\",\n \"freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model\",\n \"data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling\",\n \"hp.grid_search. If this is -1, (virtually) infinite samples are generated until a stopping\",\n \"to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int or hp sampling function from an integer space\",\n \"For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale: Int\",\n \"the best hyperparameters. :param data: training data, a pandas dataframe with Td rows,\",\n \"tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon =\",\n \"to use once the mode is fitted. The value defaults to None, where\",\n \"fit or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file):\",\n \"to install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data, cross_validation=True, expect_horizon=None, freq=None,\",\n \"1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You\",\n \"for test/valid data. \\\"\\\"\\\" if data is None: raise ValueError(\\\"Input invalid data of\",\n \"if freq is None: assert len(data) >= 2, \\\"The training dataframe should contains\",\n \"metric_threshold: a trial will be terminated when metric threshold is met :param n_sampling:\",\n \"directory to save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int.\",\n \"columns, with column 'ds' indicating date and column 'y' indicating value and Td\",\n \"Number of trials to evaluate in total. Defaults to 16. If hp.grid_search is\",\n \"data[:len(data)-expect_horizon] validation_data = None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if\",\n \"\\\"\\\"\\\" Predict using the best model after HPO. :param horizon: the number of\",\n \"None: raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is None: raise RuntimeError(\",\n \"10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10). :param\",\n \"has 1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError(\",\n \"User need to specify either the exact value or the search space of\",\n \"Td is the time dimension :param metrics: A list contains metrics for test/valid\",\n \":param checkpoint_file: The checkpoint file location you want to load the best model.\",\n \"n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric,\",\n \"defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync training results and\",\n \"to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq =\",\n \"with the License. # You may obtain a copy of the License at\",\n \"of trials to evaluate in total. Defaults to 16. If hp.grid_search is in\",\n \"self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = {\",\n \"the number of steps forward to predict :param freq: the freqency of the\",\n \":param metric_threshold: a trial will be terminated when metric threshold is met :param\",\n \"\\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is None: assert\",\n \"\\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01,\",\n \") # use the best config to fit a new prophet model on\",\n \"can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param\",\n \"is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale,\",\n \":param changepoint_range: hyperparameter changepoint_range for the Prophet model. e.g. hp.uniform(0.8, 0.95). :param metric:\",\n \"save logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of\",\n \"the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet import ProphetBuilder,\",\n \"from an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"or the search space of the Prophet model hyperparameters. For details of the\",\n \"warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use `fit` function.\\\") def fit(self, data, cross_validation=True,\",\n \"16. If hp.grid_search is in search_space, the grid will be run n_sampling of\",\n \"frequency will be infer implicitly. :param metric_threshold: a trial will be terminated when\",\n \"should be in datetime 64 type, or you need to set `freq` in\",\n \"1. :param name: name of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir:\",\n \"Remote directory to sync training results and checkpoints. It defaults to None and\",\n \"data will be automatically splited from training data, and expect_horizon is the horizon\",\n \"parameters for searcher algorithm besides search_space, metric and searcher mode :param scheduler: str,\",\n \"call fit or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def\",\n \"import warnings from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # -\",\n \"eval result comes from cross_validation. The value is set to True by default.\",\n \"raise ValueError(\\\"Input invalid data of None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You\",\n \"trials to evaluate in total. Defaults to 16. If hp.grid_search is in search_space,\",\n \"by ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is None:\",\n \"\\\\ \\\"The 'ds' col should be in datetime 64 type, or you need\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"is met :param n_sampling: Number of trials to evaluate in total. Defaults to\",\n \"be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases :param ds_data:\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"# limitations under the License. # import pandas as pd import warnings from\",\n \"the exact value or the search space of the Prophet model hyperparameters. For\",\n \"HPO. :param horizon: the number of steps forward to predict :param freq: the\",\n \"sampling function from an integer space for hyperparameter changepoint_prior_scale for the Prophet model.\",\n \"in total. Defaults to 16. If hp.grid_search is in search_space, the grid will\",\n \"validation data will be automatically splited from training data, and expect_horizon is the\",\n \"checkpoint file location you want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def\",\n \"__init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config\",\n \"): \\\"\\\"\\\" Automatically fit the model and search for the best hyperparameters. :param\",\n \"column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise RuntimeError( \\\"You must\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"an unreliable frequency will be infer implicitly. :param metric_threshold: a trial will be\",\n \"\\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\" Get the best Prophet model. \\\"\\\"\\\" return self.best_model.model\",\n \"algorithm besides search_space, metric and searcher mode :param scheduler: str, all supported scheduler\",\n \"an integer space for hyperparameter changepoint_prior_scale for the Prophet model. For hp sampling,\",\n \"\\\"mse\\\" :param logs_dir: Local directory to save logs and results. It defaults to\",\n \"search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a\",\n \"model and search for the best hyperparameters. :param data: training data, a pandas\",\n \":param remote_dir: String. Remote directory to sync training results and checkpoints. It defaults\",\n \"cross_validation: bool, if the eval result comes from cross_validation. The value is set\",\n \"checkpoints. It defaults to None and doesn't take effects while running in local.\",\n \"assert len(data) >= 2, \\\"The training dataframe should contains more than 2 records.\\\"\",\n \"to False to speed up the process. :param expect_horizon: int, validation data will\",\n \"ckpt from load_dir. The value defaults to None. :param prophet_config: Other Prophet hyperparameters.\",\n \"once the mode is fitted. The value defaults to None, where 10% of\",\n \"holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create\",\n \"in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str =\",\n \"hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param changepoint_prior_scale:\",\n \"while running in local. While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param\",\n \"running in local. While running in cluster, it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir:\",\n \"Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try: from bigdl.orca.automl.auto_estimator import\",\n \"hp.loguniform(0.01, 10) if holidays_prior_scale is None else holidays_prior_scale, \\\"seasonality_mode\\\": hp.choice(['additive', 'multiplicative']) if seasonality_mode\",\n \"logs and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus\",\n \"searcher algorithm besides search_space, metric and searcher mode :param scheduler: str, all supported\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync training\",\n \"AutoEstimator import bigdl.orca.automl.hp as hp self.search_space = { \\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5])\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling,\",\n \"raise RuntimeError( \\\"You must call fit or restore first before calling predict!\\\") return\",\n \"whole data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict\",\n \"can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to\",\n \"Number of cpus for each trial. It defaults to 1. :param name: name\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"value and Td is the time dimension :param cross_validation: bool, if the eval\",\n \"String. The evaluation metric name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory\",\n \"by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\",\",\n \":param horizon: the number of steps forward to predict :param freq: the freqency\",\n \"set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq)\",\n \"is None: raise RuntimeError( \\\"You must call fit or restore first before calling\",\n \"Int or hp sampling function from an integer space for hyperparameter changepoint_prior_scale for\",\n \"more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in\",\n \"or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self, data,\",\n \"# + # # Copyright 2016 The BigDL Authors. # # Licensed under\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"(i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\")\",\n \"if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) # update other configs self.metric\",\n \"with column 'ds' indicating date and column 'y' indicating value and Td is\",\n \"else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\": cross_validation})\",\n \"1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10) if holidays_prior_scale\",\n \"to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for each trial. It defaults\",\n \"the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range for the Prophet\",\n \"space of the Prophet model hyperparameters. For details of the Prophet model hyperparameters,\",\n \"name of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory\",\n \"\\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and searcher\",\n \"search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) # use the best config to fit a new\",\n \"to predict :param freq: the freqency of the predicted dataframe, defaulted to day(\\\"D\\\"),\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"= None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling !=\",\n \"str, all supported scheduler provided by ray tune :param scheduler_params: parameters for scheduler\",\n \"for the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync training results and checkpoints.\",\n \"the freqency of the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can be\",\n \"and search for the best hyperparameters. :param data: training data, a pandas dataframe\",\n \"defaulted to day(\\\"D\\\"), the frequency can be anything from the pandas list of\",\n \"after HPO. :param horizon: the number of steps forward to predict :param freq:\",\n \"the horizon you may need to use once the mode is fitted. The\",\n \"provided by ray tune :param scheduler_params: parameters for scheduler \\\"\\\"\\\" if expect_horizon is\",\n \"self.best_model.model is None: raise RuntimeError( \\\"You must call fit or restore first before\",\n \"-1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params\",\n \"an automated Prophet Model. User need to specify either the exact value or\",\n \"\\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir. The value defaults to None.\",\n \"defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for each trial. It\",\n \"results and checkpoints. It defaults to None and doesn't take effects while running\",\n \"column 'ds' indicating date and column 'y' indicating value and Td is the\",\n \"strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be infer implicitly.\",\n \"The evaluation metric name to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to\",\n \"Model. User need to specify either the exact value or the search space\",\n \"location you want to load the best model. \\\"\\\"\\\" self.best_model.restore(checkpoint_file) def get_best_model(self): \\\"\\\"\\\"\",\n \"hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10).\",\n \"class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\",\",\n \"the process. :param expect_horizon: int, validation data will be automatically splited from training\",\n \"the specific language governing permissions and # limitations under the License. # import\",\n \"hp.loguniform(0.01, 10). :param holidays_prior_scale: hyperparameter holidays_prior_scale for the Prophet model. e.g. hp.loguniform(0.01, 10).\",\n \"None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You must call fit or restore\",\n \"terminated when metric threshold is met :param n_sampling: Number of trials to evaluate\",\n \"!= -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler,\",\n \"expect_horizon: int, validation data will be automatically splited from training data, and expect_horizon\",\n \"\\\"You must call fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self,\",\n \"seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ): \\\"\\\"\\\" Create an\",\n \"it defaults to \\\"hdfs:///tmp/{name}\\\". :param load_dir: Load the ckpt from load_dir. The value\",\n \"2 columns, with column 'ds' indicating date and column 'y' indicating value and\",\n \"frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted\",\n \":param expect_horizon: int, validation data will be automatically splited from training data, and\",\n \"-1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space, search_alg=search_alg, search_alg_params=search_alg_params, scheduler=scheduler, scheduler_params=scheduler_params ) #\",\n \"\\\"The 'ds' col should be in datetime 64 type, or you need to\",\n \"None if cross_validation else data[len(data)-expect_horizon:] n_sampling = recalculate_n_sampling(self.search_space, n_sampling) if n_sampling != -1\",\n \"you may need to use once the mode is fitted. The value defaults\",\n \"anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where\",\n \"using the best model after HPO. :param horizon: the number of steps forward\",\n \"False to speed up the process. :param expect_horizon: int, validation data will be\",\n \"of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted to None, where an unreliable frequency will be\",\n \"# - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\",\",\n \"10% of training data will be taken as the validation data. :param freq:\",\n \"Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"model hyperparameters. For details of the Prophet model hyperparameters, refer to https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning. :param\",\n \"to sync training results and checkpoints. It defaults to None and doesn't take\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"training dataframe should contains more than 2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds'\",\n \"data, a pandas dataframe with Td rows, and 2 columns, with column 'ds'\",\n \"dimension :param metrics: A list contains metrics for test/valid data. \\\"\\\"\\\" if data\",\n \"the mode is fitted. The value defaults to None, where 10% of training\",\n \"pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in datetime 64 type, or you\",\n \"hyperparameter seasonality_mode for the Prophet model. e.g. hp.choice(['additive', 'multiplicative']). :param changepoint_range: hyperparameter changepoint_range\",\n \"It defaults to 1. :param name: name of the AutoProphet. It defaults to\",\n \"expect_horizon=None, freq=None, metric_threshold=None, n_sampling=16, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the\",\n \"fit or restore first before calling predict!\\\") return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data) def evaluate(self,\",\n \"n_sampling) if n_sampling != -1 else -1 self.auto_est.fit(data=train_data, validation_data=validation_data, metric=self.metric, metric_threshold=metric_threshold, n_sampling=n_sampling, search_space=self.search_space,\",\n \"It defaults to None and doesn't take effects while running in local. While\",\n \"and results. It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for\",\n \"call fit or restore first before calling save!\\\") self.best_model.save(checkpoint_file) def restore(self, checkpoint_file): \\\"\\\"\\\"\",\n \"recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse',\",\n \"met. :param search_alg: str, all supported searcher provided by ray tune (i.e.\\\"variant_generator\\\", \\\"random\\\",\",\n \"seasonality_mode, \\\"changepoint_range\\\": hp.uniform(0.8, 0.95) if changepoint_range is None else changepoint_range } self.search_space.update(prophet_config) #\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"data self.best_model = ProphetBuilder().build(self.auto_est.get_best_config()) self.best_model.model.fit(data) def predict(self, horizon=1, freq=\\\"D\\\", ds_data=None): \\\"\\\"\\\" Predict using\",\n \"from cross_validation. The value is set to True by default. Setting this option\",\n \"to optimize. e.g. \\\"mse\\\" :param logs_dir: Local directory to save logs and results.\",\n \":param checkpoint_file: The location you want to save the best model, should be\",\n \"evaluation data, a pandas dataframe with Td rows, and 2 columns, with column\",\n \"to None and doesn't take effects while running in local. While running in\",\n \"\\\"\\\"\\\" Restore the best model after HPO. :param checkpoint_file: The checkpoint file location\",\n \"'y' indicating value and Td is the time dimension :param cross_validation: bool, if\",\n \"time dimension :param cross_validation: bool, if the eval result comes from cross_validation. The\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp' #\",\n \"and \\\"sigopt\\\") :param search_alg_params: extra parameters for searcher algorithm besides search_space, metric and\",\n \"limitations under the License. # import pandas as pd import warnings from bigdl.chronos.model.prophet\",\n \"for scheduler \\\"\\\"\\\" if expect_horizon is None: expect_horizon = int(0.1*len(data)) if freq is\",\n \"train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if cross_validation else\",\n \"may need to use once the mode is fitted. The value defaults to\",\n \"\\\"changepoint_prior_scale\\\": hp.grid_search([0.005, 0.05, 0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01,\",\n \"training dataframe. the frequency can be anything from the pandas list of frequency\",\n \"fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] - data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq\",\n \"freqency of the predicted dataframe, defaulted to day(\\\"D\\\"), the frequency can be anything\",\n \"self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best model after HPO. :param\",\n \"from training data, and expect_horizon is the horizon you may need to use\",\n \"the search space of the Prophet model hyperparameters. For details of the Prophet\",\n \"changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\":\",\n \"= ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder, logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You\",\n \"hp.grid_search([0.01, 0.1, 1.0, 10.0]) if seasonality_prior_scale is None else seasonality_prior_scale, \\\"holidays_prior_scale\\\": hp.loguniform(0.01, 10)\",\n \"that has 1 column 'ds' indicating date. \\\"\\\"\\\" if self.best_model.model is None: raise\",\n \":param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model = ProphetModel() self.best_model.restore(load_dir) try:\",\n \"type, or you need to set `freq` in fit.\\\" self._freq = data[\\\"ds\\\"].iloc[1] -\",\n \"data[\\\"ds\\\"].iloc[0] else: self._freq = pd.Timedelta(freq) expect_horizon_str = str(self._freq * expect_horizon) self.search_space.update({\\\"expect_horizon\\\": expect_horizon_str, \\\"cross_validation\\\":\",\n \"and searcher mode :param scheduler: str, all supported scheduler provided by ray tune\",\n \"defaults to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model =\",\n \"the Prophet model. e.g. hp.loguniform(0.01, 10). :param seasonality_mode: hyperparameter seasonality_mode for the Prophet\",\n \"It defaults to \\\"/tmp/auto_prophet_logs\\\" :param cpus_per_trial: Int. Number of cpus for each trial.\",\n \"data will be taken as the validation data. :param freq: the freqency of\",\n \"search_space, the grid will be run n_sampling of trials and round up n_sampling\",\n \":param ds_data: a dataframe that has 1 column 'ds' indicating date. \\\"\\\"\\\" if\",\n \"0.1, 0.5]) if changepoint_prior_scale is None else changepoint_prior_scale, \\\"seasonality_prior_scale\\\": hp.grid_search([0.01, 0.1, 1.0, 10.0])\",\n \"extra parameters for searcher algorithm besides search_space, metric and searcher mode :param scheduler:\",\n \"None: raise RuntimeError( \\\"You must call fit or restore first before calling predict!\\\")\",\n \"\\\"\\\"\\\" Evaluate using the best model after HPO. :param data: evaluation data, a\",\n \"before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics) def save(self, checkpoint_file): \\\"\\\"\\\" Save the best\",\n \"tune (i.e.\\\"variant_generator\\\", \\\"random\\\", \\\"ax\\\", \\\"dragonfly\\\", \\\"skopt\\\", \\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and\",\n \"met :param n_sampling: Number of trials to evaluate in total. Defaults to 16.\",\n \"For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale:\",\n \"the grid will be run n_sampling of trials and round up n_sampling according\",\n \"invalid data of None\\\") if self.best_model.model is None: raise RuntimeError( \\\"You must call\",\n \"ProphetBuilder, ProphetModel from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None,\",\n \"for more details. e.g. hp.loguniform(0.001, 0.5). :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the Prophet\",\n \"# update other configs self.metric = metric model_builder = ProphetBuilder() self.auto_est = AutoEstimator(model_builder=model_builder,\",\n \"logs_dir=logs_dir, resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to\",\n \"\\\"cross_validation\\\": cross_validation}) train_data = data if cross_validation else data[:len(data)-expect_horizon] validation_data = None if\",\n \"either the exact value or the search space of the Prophet model hyperparameters.\",\n \"the model and search for the best hyperparameters. :param data: training data, a\",\n \"defaults to 1. :param name: name of the AutoProphet. It defaults to \\\"auto_prophet\\\"\",\n \"the Prophet model. For hp sampling, see bigdl.chronos.orca.automl.hp for more details. e.g. hp.loguniform(0.001,\",\n \"resources_per_trial={\\\"cpu\\\": cpus_per_trial}, remote_dir=remote_dir, name=name) except ImportError: warnings.warn(\\\"You need to install `bigdl-orca[automl]` to use\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"you want to save the best model, should be a json file \\\"\\\"\\\"\",\n \"the AutoProphet. It defaults to \\\"auto_prophet\\\" :param remote_dir: String. Remote directory to sync\",\n \"\\\"hyperopt\\\", \\\"bayesopt\\\", \\\"bohb\\\", \\\"nevergrad\\\", \\\"optuna\\\", \\\"zoopt\\\" and \\\"sigopt\\\") :param search_alg_params: extra parameters for\",\n \"value defaults to None. :param prophet_config: Other Prophet hyperparameters. \\\"\\\"\\\" if load_dir: self.best_model\",\n \"to 1. :param name: name of the AutoProphet. It defaults to \\\"auto_prophet\\\" :param\",\n \"\\\"You must call fit or restore first before calling evaluate!\\\") return self.best_model.evaluate(target=data, metrics=metrics)\",\n \"cpus for each trial. It defaults to 1. :param name: name of the\",\n \"changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None, seasonality_mode=None, changepoint_range=None, metric='mse', logs_dir=\\\"/tmp/auto_prophet_logs\\\", cpus_per_trial=1, name=\\\"auto_prophet\\\", remote_dir=None, load_dir=None, **prophet_config ):\",\n \"from bigdl.chronos.autots.utils import recalculate_n_sampling # - class AutoProphet: def __init__(self, changepoint_prior_scale=None, seasonality_prior_scale=None, holidays_prior_scale=None,\",\n \"def evaluate(self, data, metrics=['mse']): \\\"\\\"\\\" Evaluate using the best model after HPO. :param\",\n \"2 records.\\\" assert pd.api.types.is_datetime64_any_dtype(data[\\\"ds\\\"].dtypes), \\\\ \\\"The 'ds' col should be in datetime 64\",\n \"search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, ): \\\"\\\"\\\" Automatically fit the model and search for\"\n]","isConversational":false}}},{"rowIdx":2589,"cells":{"ngram":{"kind":"list like","value":["= unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B) log_det","@ self.w, 1) + self.b z = x + u * self.h(lin) phi","nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self, x):","x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def","1) W, H = 2 * self.B * W, 2 * self.B *","- 1 / 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x)","== 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:,","forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return","+ beta * h) + \\ torch.log(1 + beta * h - \\","self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D =","+ \\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow.","/ torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @ self.w,","// 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def","F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det +=","self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det","torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @ L @","W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x","in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(),","// 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper","2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2,","+ scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin","H, D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim","ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim","// 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim","function must be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x),","= torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0],","= torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype =","NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan et al. 2019] \"\"\" def","out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det +=","s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper)","reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0])","3 * self.K - 1) W, H, D = torch.split(out, self.K, dim =","inverse = True, tail_bound = self.B) log_det += ld return x, log_det class","self.u)) - self.w @ self.u - 1 u = self.u + scal *","def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P,","x + β h(α, r)(z − z0) [Rezende and Mohamed 2015] \"\"\" def","inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim //","= unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound = self.B)","self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param,","= torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0:","W, H, D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W,","\"\"\" def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim))","z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z =","self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed =","= self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x =","self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z","hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param =","math import numpy as np import scipy as sp import scipy.linalg import torch","1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det =","dim self.K = K self.B = B self.f1 = base_network(dim // 2, (3","inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return","F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse =","in range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters()","log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self,","flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z = f(x)","log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z -","\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim","= 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2))","x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper","= f(x) = = x + β h(α, r)(z − z0) [Rezende and","z[:, i], W, H, D, inverse = True, tail_bound = self.B) log_det +=","ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0])","1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:,","hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1) * dim","ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B)","self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim):","log_det += ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det","log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i ==","D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B)","= torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim = 2),","nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def","dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper","OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim):","x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det","init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and the","for i in range(1, dim): self.layers += [base_network(i, 3 * K - 1,","\"\"\" Planar flow. z = f(x) = x + u h(wᵀx + b)","@ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @","z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal =","t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed)","+= ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det =","1)) for i in range(1, dim): self.layers += [base_network(i, 3 * K -","8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim //","W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H","Given x, returns z and the log-determinant log|df/dx|. \"\"\" m, n = x.shape","* self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z,","for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 *","nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def","dim = 2), torch.softmax(H, dim = 2) W, H = 2 * self.B","torch.softmax(H, dim = 2) W, H = 2 * self.B * W, 2","r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \"\"\"","lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) }","= nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def","z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return","def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x):","i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det +=","torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None","import numpy as np import scipy as sp import scipy.linalg import torch import","2) W, H = 2 * self.B * W, 2 * self.B *","F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x)","= torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i])","\"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__()","- t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1)","2, (3 * K - 1) * dim // 2, hidden_dim) def forward(self,","self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def","= self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x","self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower,","+= [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x):","= None def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim))","spline flow, auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K =","lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu:","i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i -","1), torch.softmax(H, dim = 1) W, H = 2 * self.B * W,","= self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper],","x + beta * h * (x - self.x0) log_det = (n -","torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed,","h - \\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return","log_det += alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and","dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim =","------- \"\"\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h ==","return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z","True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper],","-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given","tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2,","+ 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow has no","- torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x <","else: raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @ self.w, 1) +","Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def __init__(self, dim, hidden_dim =","torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z","self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1)","i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self,","i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound","def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1))","log_det def backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module):","= dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim):","torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1)","= torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha =","return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et al.","-1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P","return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for","def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim):","self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers +=","/ (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z =","torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float))","NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z =","= dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype =","torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return","= (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class","self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1)","torch.log(1 + beta * h - \\ beta * r / (torch.exp(self.log_alpha) +","convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim","// 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2","dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2],","h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 +","= self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:,","and the log-determinant log|df/dx|. \"\"\" m, n = x.shape r = torch.norm(x -","= torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H = 2","= (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det","= 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal =","= -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x","2, (3 * K - 1) * dim // 2, hidden_dim) self.f2 =","= x + β h(α, r)(z − z0) [Rezende and Mohamed 2015] \"\"\"","= dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param =","log_det = (n - 1) * torch.log(1 + beta * h) + \\","(x - self.x0) log_det = (n - 1) * torch.log(1 + beta *","self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i]) W, H,","flow. z = f(x) = x + u h(wᵀx + b) [<NAME> Mohamed,","-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z","def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) +","self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K -","= torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return","dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper =","x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal","x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda","- \\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z,","W, 2 * self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS(","Non-volume preserving flow. [Dinh et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim =","torch.exp(self.beta)) z = x + beta * h * (x - self.x0) log_det","__init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList()","i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha return x,","K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K -","def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def","= torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z - self.mu)","= torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv =","auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K = 5, B","dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim //","(lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper =","x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim,","def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U =","neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim,","\\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det","layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim","alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0])","= (n - 1) * torch.log(1 + beta * h) + \\ torch.log(1","= 2), torch.softmax(H, dim = 2) W, H = 2 * self.B *","import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note that","= torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim = 1),","for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1]","torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out","supported non-linearities: note that the function must be invertible functional_derivatives = { torch.tanh:","== torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1","log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,))","1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 *","= -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W =","def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed","* dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper","\"\"\" Non-volume preserving flow. [Dinh et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim","- self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\"","self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0],","backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if","1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2)","2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W,","else: out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K,","self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det","def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim):","2), torch.softmax(H, dim = 2) W, H = 2 * self.B * W,","self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def","hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x)","2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed","as np import scipy as sp import scipy.linalg import torch import torch.nn as","= F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse","= base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2,","* H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W,","self.x0) log_det = (n - 1) * torch.log(1 + beta * h) +","= torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S =","z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det","self.K, dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim","= self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper],","2) return z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\"","* K - 1) * dim // 2, hidden_dim) def forward(self, x): log_det","* self.K - 1) W, H, D = torch.split(out, self.K, dim = 2)","and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha =","torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L,","self.K - 1) W, H, D = torch.split(init_param, self.K, dim = 1) else:","torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) *","out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i]","r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta","nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim):","D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B)","hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def","= dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 =","log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det","* H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D,","torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0],","* self.B * W, 2 * self.B * H D = F.softplus(D) lower,","return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al. 2017] \"\"\"","= nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1,","z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim))","f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def","W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H","Planar flow. z = f(x) = x + u h(wᵀx + b) [<NAME>","/ (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \"\"\" Simple","log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L","as F from nf.utils import unconstrained_RQS # supported non-linearities: note that the function","for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self):","ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan","diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z =","F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound","forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:,","self.w, 1) + self.b z = x + u * self.h(lin) phi =","z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i","L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L,","= self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed","u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__()","self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim))","dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim)","None def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U","self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S","class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al. 2017] \"\"\" def __init__(self,","lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed","self.w @ self.u - 1 u = self.u + scal * self.w /","self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def","K - 1)) for i in range(1, dim): self.layers += [base_network(i, 3 *","z and the log-determinant log|df/dx|. \"\"\" m, n = x.shape r = torch.norm(x",":self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2,","self.B * W, 2 * self.B * H D = F.softplus(D) z[:, i],","* self.K - 1) W, H, D = torch.split(init_param, self.K, dim = 1)","hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2,",":i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu +","def backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\"","dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K -","backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed =","no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z = f(x) = =","alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha","phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) +","base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim","self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W)","= 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)","+ torch.log(1 + torch.exp(self.beta)) z = x + beta * h * (x","* self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:,","dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U","+ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @ self.P","the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu): u =","self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K -","+ torch.exp(self.beta)) z = x + beta * h * (x - self.x0)","(x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def","// 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3","\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) +","z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural","= 1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv =","self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow,","def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float))","out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim,","log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha","NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z","* torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z):","= out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:,","- 1) W, H, D = torch.split(init_param, self.K, dim = 1) else: out","upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed =","flow. z = f(x) = = x + β h(α, r)(z − z0)","self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i],","* z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm","W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self,","out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H,","import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as","= 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det","elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u","upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det","-0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) *","h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h","x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. Returns ------- \"\"\"","(torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \"\"\" Simple fully","= 2) W, H = 2 * self.B * W, 2 * self.B","5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim =","x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1","log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\" def __init__(self, in_dim,","1) + self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin)","returns z and the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu,","\"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers","class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim):","W, H, D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i","* self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @","in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out","= 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:,","[Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim self.mu","[Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim W,","dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K -","torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self,","torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower,","< 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z = f(x)","= torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype =","// 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2","ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__()","torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x","def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det =","F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01,","3 * self.K - 1) W, H, D = torch.split(init_param, self.K, dim =","i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param,","x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et al. 2019]","= FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.f1","@ self.u - 1 u = self.u + scal * self.w / torch.norm(self.w)","= torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z =","= torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det","= t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det =","= x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim","= self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed","+ self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x =","+= torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 *","unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B) log_det +=","torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @ self.P @","U = torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U","dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param","1) * torch.log(1 + beta * h) + \\ torch.log(1 + beta *","= nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma)","@ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det","(upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed,","nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim),","* torch.log(1 + beta * h) + \\ torch.log(1 + beta * h","x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\"","= z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\"","= self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D","np import scipy as sp import scipy.linalg import torch import torch.nn as nn","W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out =","range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def","out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H,","s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower,","def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma)","super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1))","+ \\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper =","ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld","u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow has","torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i","x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. \"\"\" m, n","+ upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed","B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i","= torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0],","math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in","self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed =","@ self.u)) - self.w @ self.u - 1 u = self.u + scal","super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0,","dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu","= 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim","the function must be invertible functional_derivatives = { torch.tanh: lambda x: 1 -","t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) +","dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L","torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float),","init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x): z =","D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1)","dim = 2) W, H = 2 * self.B * W, 2 *","= nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w,","= self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:,","nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers += [base_network(i,","self.dim = dim self.K = K self.B = B self.f1 = base_network(dim //","(x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z =","= (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper","x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\","alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.]","log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive.","0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z = f(x) =","= nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self,","B self.f1 = base_network(dim // 2, (3 * K - 1) * dim","self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @","backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial","auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def __init__(self, dim, hidden_dim = 8,","if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W,","Given x, returns z and the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h","// 2, 3 * self.K - 1) W, H, D = torch.split(out, self.K,","self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed =","= (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det =","1) z = x @ self.P @ L @ (U + torch.diag(self.S)) log_det","non-linearities: note that the function must be invertible functional_derivatives = { torch.tanh: lambda","2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 *","nf.utils import unconstrained_RQS # supported non-linearities: note that the function must be invertible","b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity","self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x,","/ torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1","2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2,","1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha","return z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\")","= 1) else: out = self.layers[i - 1](x[:, :i]) W, H, D =","NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self,","-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given","// 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)","- 1) * dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0])","= x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det =","D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim","log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim //","= -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta * h","tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1),","x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim //","-math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and the log-determinant","= nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self,","torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z - self.mu) /","x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al. 2017]","= self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim =","torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3","// 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3","+ \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @","self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim //","nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U,","log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan et al. 2019]","et al. 2018] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim","self.f2 = base_network(dim // 2, (3 * K - 1) * dim //","H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H =","W, 2 * self.B * H D = F.softplus(D) x[:, i], ld =","self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1","torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked","2 * self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS(","= nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U =","\\ torch.log(1 + beta * h - \\ beta * r / (torch.exp(self.log_alpha)","D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B)","F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B)","return z, log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma)","= F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det","self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @","functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return","// 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim //","range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5))","+ \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor)","backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:,","{ torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x >","log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param =","def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed","nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return","self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype","1) else: out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out,","− z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 =","upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound =","+ r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x +","<filename>nf/flows.py import math import numpy as np import scipy as sp import scipy.linalg","nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha,","flow, coupling layer. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K =","def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2],","* self.B * W, 2 * self.B * H D = F.softplus(D) upper,","8, base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B =","self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim))","self.K - 1) W, H, D = torch.split(out, self.K, dim = 2) W,","= x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self,","nn import torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS","0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar","2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W,","\"\"\" Given x, returns z and the log-determinant log|df/dx|. \"\"\" m, n =","2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim","dim = 1) W, H = 2 * self.B * W, 2 *","= 5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim","self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H,","(F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @","D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D,","= torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal =","= dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P","fully connected neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network =","H, D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim","= self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D","al. 2019] \"\"\" def __init__(self, dim, K = 5, B = 3, hidden_dim","= nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers +=","torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution.","base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower, upper =","0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D =","self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u -","H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim =","torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u = self.u +","1 / 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det","= x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) +","sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float))","// 2, dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim","log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu): u = self.u","= base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2,","and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim W, _","MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def __init__(self, dim,","W, H, D, inverse = True, tail_bound = self.B) log_det += ld return","t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed)","backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return","h) + \\ torch.log(1 + beta * h - \\ beta * r","i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K","super().__init__() self.dim = dim self.K = K self.B = B self.f1 = base_network(dim","init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns","hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim),","preserving flow. [Dinh et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim = 8,","torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U + torch.diag(self.S))","- 1)) for i in range(1, dim): self.layers += [base_network(i, 3 * K","base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i","self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1)","= nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i,","-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z","* torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed)","x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K","self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z =","- 1 u = self.u + scal * self.w / torch.norm(self.w) else: raise","* h * (x - self.x0) log_det = (n - 1) * torch.log(1","= F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det","-math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i","self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self,","= nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal =","z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x,","torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha)","2017] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim","\"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1","2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower","= functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4)","= out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu) /","Radial flow. z = f(x) = = x + β h(α, r)(z −","beta * h * (x - self.x0) log_det = (n - 1) *","i in range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)]","nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module):","class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def __init__(self,","= unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim =","W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower,","(torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x","1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W,","math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and","lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed","+= torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det def","torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K","x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan et al.","0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i])","= 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)","2 * self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper,","x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim):","* self.B * W, 2 * self.B * H D = F.softplus(D) z[:,","self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in","\"\"\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype =","// 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1)","z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper)","torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F","self.dim = dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1","self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:,","torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out","torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower *","U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype","as sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init as","dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K -","forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U,","self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U","\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z","dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z","-torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et","self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed =","= torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1)","* self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W,","unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)","dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim)","// 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper =","= torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z):","# supported non-linearities: note that the function must be invertible functional_derivatives = {","dim = 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z):","nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving","out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow.","= self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param, self.K,","\"\"\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh:","+ lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1)","1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim","log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param =","dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim","__init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def","unconstrained_RQS # supported non-linearities: note that the function must be invertible functional_derivatives =","2018] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim","(3 * K - 1) * dim // 2, hidden_dim) def forward(self, x):","torch.log(1 + beta * h) + \\ torch.log(1 + beta * h -","i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)),","+ u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh):","+ u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 +","and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim self.mu =","r) ** 2) return z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural","dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x):","D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x","phi @ u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\"Planar","\"\"\" Radial flow. z = f(x) = = x + β h(α, r)(z","raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @ self.w, 1) + self.b","lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x + u","self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x =","+= [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, -","= 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta))","0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det","= True, tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module):","\"\"\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self, dim,","log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:]","alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu)","Radial(nn.Module): \"\"\" Radial flow. z = f(x) = = x + β h(α,","hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:]","K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2,","lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det","\"\"\" Neural spline flow, coupling layer. [Durkan et al. 2019] \"\"\" def __init__(self,","2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim //","dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype","def forward(self, x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. Returns","tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim","torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu,","= x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower)","(U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det =","W, 2 * self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS(","H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z):","as init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities:","- self.x0) log_det = (n - 1) * torch.log(1 + beta * h)","self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim))","} class Planar(nn.Module): \"\"\" Planar flow. z = f(x) = x + u","log-determinant log|df/dx|. \"\"\" m, n = x.shape r = torch.norm(x - self.x0) h","torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv:","log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim //","forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed =","K = 5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__()","dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv","torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H,","scipy.linalg import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional","H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True,","def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma)","= nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self,","\"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u","init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns","network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim),","torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H,","torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param","lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower)","init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\"","* torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\","* W, 2 * self.B * H D = F.softplus(D) upper, ld =","z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i","hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim","torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from nf.utils","= { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x","unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)","log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def","super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in","= K self.B = B self.f1 = base_network(dim // 2, (3 * K","2, 3 * self.K - 1) W, H, D = torch.split(out, self.K, dim","1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu","= unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return","nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta,","backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\","dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive","// 2, (3 * K - 1) * dim // 2, hidden_dim) self.f2","D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim =","L = torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal","+ torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not","2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 =","self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i]","self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim),","unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound = self.B) log_det","__init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim))","* W, 2 * self.B * H D = F.softplus(D) lower, ld =","scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u =","out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -=","dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2],","torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported","return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.]","log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim //","+= alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal,","dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype","self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param, self.K, dim","= unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim =","= K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K","= self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w","> 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\"","upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed +","H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False,","2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim,","H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1,","base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim // 2,","W, H = 2 * self.B * W, 2 * self.B * H","(n - 1) * torch.log(1 + beta * h) + \\ torch.log(1 +","3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K =","ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim","torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def backward(self, z):","@ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z):","= torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta =","forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if","i] log_det += alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma","i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else:","not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U =","= = x + β h(α, r)(z − z0) [Rezende and Mohamed 2015]","torch.unsqueeze(x @ self.w, 1) + self.b z = x + u * self.h(lin)","super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim)","self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x *","self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i],","= z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha =","= sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype =","* -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor)","r)(z − z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0","z, log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det","dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim)","from nf.utils import unconstrained_RQS # supported non-linearities: note that the function must be","n = x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha)","2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 *","x, returns z and the log-determinant log|df/dx|. \"\"\" m, n = x.shape r","H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H,","W, 2 * self.B * H D = F.softplus(D) z[:, i], ld =","D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse =","super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), )","super().__init__() self.dim = dim self.K = K self.B = B self.layers = nn.ModuleList()","z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim","nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume","torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3","z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in","= torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0:","x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i ==","diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W","H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse","tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural","torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor)","if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U","flow. [Papamakarios et al. 2018] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN):","log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3","-torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal,","z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\" def __init__(self,","h(α, r)(z − z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__()","inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x =","log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module):","= z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim","def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self,","note that the function must be invertible functional_derivatives = { torch.tanh: lambda x:","self.K = K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 *","upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out","* dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K","torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u","= torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u = self.u","scipy as sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init","return z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal","H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper],","beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class","torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \"\"\"","L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal =","2 * self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS(","dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3","backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i","= torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def backward(self,","@ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z,","\"\"\" Simple fully connected neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__()","math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and the log-determinant log|df/dx|.","H = 2 * self.B * W, 2 * self.B * H D","nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) +","x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim","Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h","\"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta =","et al. 2019] \"\"\" def __init__(self, dim, K = 5, B = 3,","has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z = f(x) =","** 2) return z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural network.","out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim","1) W, H, D = torch.split(out, self.K, dim = 2) W, H =","z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer.","self.dim // 2, 3 * self.K - 1) W, H, D = torch.split(out,","= torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\"","in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K -","diagonal = 1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv","FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.layers =","= F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det","/ 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det =","log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1)","1) W, H, D = torch.split(init_param, self.K, dim = 1) else: out =","f(x) = = x + β h(α, r)(z − z0) [Rezende and Mohamed","Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1))","torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x,","= F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False,","-torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta * h *","[Durkan et al. 2019] \"\"\" def __init__(self, dim, K = 5, B =","base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim","= nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim))","m, n = x.shape r = torch.norm(x - self.x0) h = 1 /","* K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim //","z and the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu):","F from nf.utils import unconstrained_RQS # supported non-linearities: note that the function must","* h - \\ beta * r / (torch.exp(self.log_alpha) + r) ** 2)","FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.f1 =","dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float))","nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x):","= FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.layers","is not supported.\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z =","z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha","self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim),","self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U,","import scipy.linalg import torch import torch.nn as nn import torch.nn.init as init import","* torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\","if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i","x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r)","range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out =","W, H, D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W,","H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H,","self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H,","= F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True,","2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype","self.B = B self.f1 = base_network(dim // 2, (3 * K - 1)","dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim):","= x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def","U = torch.triu(self.U, diagonal = 1) z = x @ self.P @ L","torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H = 2 *","dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def","2, dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim //","coupling layer. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K = 5,","* torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower","i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def","et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim","not supported.\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x","log_det += ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling","super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U =","torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param","mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x","r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta","[Dinh et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__()","β h(α, r)(z − z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self, dim):","z = x + beta * h * (x - self.x0) log_det =","invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda","connected neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential(","Neural spline flow, coupling layer. [Durkan et al. 2019] \"\"\" def __init__(self, dim,","beta * h - \\ beta * r / (torch.exp(self.log_alpha) + r) **","/ torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x =","x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower)","lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return","- 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] =","F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det +=","= 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)","* K - 1)) for i in range(1, dim): self.layers += [base_network(i, 3","2019] \"\"\" def __init__(self, dim, K = 5, B = 3, hidden_dim =","s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower)","W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P,","(x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module):","z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det","lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) +","K self.B = B self.f1 = base_network(dim // 2, (3 * K -","\"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(),","2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta","x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class","* self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x","= self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed","upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper)","* self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:,","log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in","dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim =","sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init as init","spline flow, coupling layer. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K","self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim)","diagonal = 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal","dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma =","mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu,","that the function must be invertible functional_derivatives = { torch.tanh: lambda x: 1","@ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline","and the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu): u","log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def","1 u = self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity","raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z","class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self,","def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim,","self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible","log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3","import scipy as sp import scipy.linalg import torch import torch.nn as nn import","t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed,","scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin =","log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det","z = f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015]","= 8, base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B","mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha)","def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2],","[base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z","2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z =","self.f1 = base_network(dim // 2, (3 * K - 1) * dim //","lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) +","torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z = f(x) = x +","2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) *","i], W, H, D, inverse = True, tail_bound = self.B) log_det += ld","the log-determinant log|df/dx|. \"\"\" m, n = x.shape r = torch.norm(x - self.x0)","= -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan","i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H,","forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al.","= t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower","al. 2017] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim =","self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers","(U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if","= 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1)","0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x >","\"\"\" Given x, returns z and the log-determinant log|df/dx|. Returns ------- \"\"\" if","upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det","W, H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim","torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U),","torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x","z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim //","1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal = -1)","H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H =","2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper *","x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\"","K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1","upper, W, H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld,","log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma","super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim,","Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim","torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i]) W,","log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def","x, returns z and the log-determinant log|df/dx|. Returns ------- \"\"\" if self.h in","- 1) * torch.log(1 + beta * h) + \\ torch.log(1 + beta","nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1))","* K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2,","+ torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S)))","* torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow. z = f(x) = x","in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w","+ \\ torch.log(1 + beta * h - \\ beta * r /","= torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0:","log|df/dx|. \"\"\" m, n = x.shape r = torch.norm(x - self.x0) h =","D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:,","range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1)","tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2,","functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x:","hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim","init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note","1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha","D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D,","x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self,","upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det","= base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2,","nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class","= B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for","forward(self, x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. Returns -------","= torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if","= nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim))","Planar(nn.Module): \"\"\" Planar flow. z = f(x) = x + u h(wᵀx +","+= torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 *","* (x - self.x0) log_det = (n - 1) * torch.log(1 + beta","= torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:]","self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\")","__init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L,","x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z,","torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et","/ 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i","init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and the","RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al. 2017] \"\"\" def __init__(self, dim,","self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z","1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W,","def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 =","math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x, returns z and","self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u)","= nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters()","nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2,","upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1,","[<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w","self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1,","= torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det","D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim","= z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper)","- self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) +","log_det class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \"\"\"","self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w","import torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS #","h * (x - self.x0) log_det = (n - 1) * torch.log(1 +","L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self,","return z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim","nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x):","inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z = f(x) = = x +","torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H = 2 *","self.B * W, 2 * self.B * H D = F.softplus(D) upper, ld","if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal","self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal","= sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype =","self.dim = dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param","= f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \"\"\"","// 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed)","x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True,","return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018]","z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1)","base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim","+= ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer.","1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W,","1) * dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower,","// 2, (3 * K - 1) * dim // 2, hidden_dim) def","init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param,","[Rezende and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha","self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det","= torch.unsqueeze(x @ self.w, 1) + self.b z = x + u *","1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z","* W, 2 * self.B * H D = F.softplus(D) x[:, i], ld","def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et.","t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed)","import unconstrained_RQS # supported non-linearities: note that the function must be invertible functional_derivatives","z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0],","self.B * W, 2 * self.B * H D = F.softplus(D) x[:, i],","+ b) [<NAME> Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h =","log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det =","dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers","2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W,","// 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)","torch.triu(self.U, diagonal = 1) z = x @ self.P @ L @ (U","t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper -","flow, auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K = 5,","torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L,","= nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b,","+ beta * h - \\ beta * r / (torch.exp(self.log_alpha) + r)","- 1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3","H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1,","ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound =","upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed =","P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L =","(3 * K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim","= mu + torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det","2 * self.B * W, 2 * self.B * H D = F.softplus(D)","= True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower,","- 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 /","unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)","= torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return","def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers =","= 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K","= x + beta * h * (x - self.x0) log_det = (n","returns z and the log-determinant log|df/dx|. \"\"\" m, n = x.shape r =","self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1))","= self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not","True, tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \"\"\"","upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1,","self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal =","dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios et al.","lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld,","* W, 2 * self.B * H D = F.softplus(D) z[:, i], ld","- self.w @ self.u - 1 u = self.u + scal * self.w","\"\"\" m, n = x.shape r = torch.norm(x - self.x0) h = 1","return x, log_det class ActNorm(nn.Module): \"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\"","= self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z","alpha = out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) *","2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) *","L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv","self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1),","s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower,","inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim //","algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow. z = f(x) = = x","+ torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module):","1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for","def forward(self, x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. \"\"\"","torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is not supported.\") lin = torch.unsqueeze(x @ self.w, 1)","\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @ L","\"\"\" Masked auto-regressive flow. [Papamakarios et al. 2018] \"\"\" def __init__(self, dim, hidden_dim","dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det def","= torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U +","beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta *","self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D =","-1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @","@ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def","base_network(dim // 2, (3 * K - 1) * dim // 2, hidden_dim)","(z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module):","H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False,","@ u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow","class Planar(nn.Module): \"\"\" Planar flow. z = f(x) = x + u h(wᵀx","def __init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network","reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x): z","z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K","as nn import torch.nn.init as init import torch.nn.functional as F from nf.utils import","reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self,","2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2,","x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1)","z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim))","3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 /","self.B * W, 2 * self.B * H D = F.softplus(D) lower, ld",") def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh","(x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\","nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim),","== 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D","1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic","2 * self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower,","self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow,","dim, K = 5, B = 3, hidden_dim = 8, base_network = FCNN):","= nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u,","dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P =","// 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower =","self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i]","torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note that the","self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim),","self.network(x) class RealNVP(nn.Module): \"\"\" Non-volume preserving flow. [Dinh et. al. 2017] \"\"\" def","log|df/dx|. Returns ------- \"\"\" if self.h in (F.elu, F.leaky_relu): u = self.u elif","= 1) z = x @ self.P @ L @ (U + torch.diag(self.S))","torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor)","z, log_det def backward(self, z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class","forward(self, x): \"\"\" Given x, returns z and the log-determinant log|df/dx|. \"\"\" m,","[Papamakarios et al. 2018] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__()","= torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L =","> 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x","D, inverse = True, tail_bound = self.B) log_det += ld return x, log_det","= nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim))","torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z)","+ self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) *","torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z,","F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det +=","z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower","= base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower, upper","8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for","= unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim =","+ β h(α, r)(z − z0) [Rezende and Mohamed 2015] \"\"\" def __init__(self,","torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower,","be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu:","self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim //","z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i ==","2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim))","[base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1","beta * h) + \\ torch.log(1 + beta * h - \\ beta","Mohamed, 2015] \"\"\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w =","al. 2018] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim =",":i]) W, H, D = torch.split(out, self.K, dim = 1) W, H =","* H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W,","must be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2),","self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self,","torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu,","* H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D,","// 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim //","upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper","-= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det =","lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out =","W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out =","x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module):","lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out","nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b =","\"\"\" def __init__(self, dim, K = 5, B = 3, hidden_dim = 8,","self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)]","u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi","def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u =","dim = 1) else: out = self.layers[i - 1](x[:, :i]) W, H, D","hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim //","\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \"\"\" Masked auto-regressive flow. [Papamakarios","import math import numpy as np import scipy as sp import scipy.linalg import","< 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x","= self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed",":self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2,","return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0])","* self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W,","ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim =","// 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim //","torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class","hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x):","= dim self.K = K self.B = B self.f1 = base_network(dim // 2,","1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1) W, H","2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1) *","sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float)","dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b","out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1]","K - 1) * dim // 2, hidden_dim) def forward(self, x): log_det =","supported.\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x +","2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0])","H, D, inverse = True, tail_bound = self.B) log_det += ld return x,","x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed +","- 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] =","= 1), torch.softmax(H, dim = 1) W, H = 2 * self.B *","class FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\" def __init__(self, in_dim, out_dim,","2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in","def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for","= self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim =","z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det","* self.B * W, 2 * self.B * H D = F.softplus(D) x[:,","reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x):","_ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype","= torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H = 2","D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim =","ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim","Simple fully connected neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network","init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for","- mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z):","math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x,","2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self,","upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld,","z): raise NotImplementedError(\"Planar flow has no algebraic inverse.\") class Radial(nn.Module): \"\"\" Radial flow.","= torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L =","unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z,","D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim","dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper =","torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim //","self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal = -1) +","t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed)","= 2 * self.B * W, 2 * self.B * H D =","base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B = B","flow. [Dinh et. al. 2017] \"\"\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN):","= B self.f1 = base_network(dim // 2, (3 * K - 1) *","* h) + \\ torch.log(1 + beta * h - \\ beta *","in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5),","self.u - 1 u = self.u + scal * self.w / torch.norm(self.w) else:","torch.log(1 + torch.exp(self.beta)) z = x + beta * h * (x -","return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan et","2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 =","diagonal = 1) z = x @ self.P @ L @ (U +","numpy as np import scipy as sp import scipy.linalg import torch import torch.nn","= 1) W, H = 2 * self.B * W, 2 * self.B","Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim W, _ =","__init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network =","z = f(x) = = x + β h(α, r)(z − z0) [Rezende","u = self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\"Non-linearity is","u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) -","* r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module):","out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha)","// 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:,","- t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper","torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det =","F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u))","class NSF_CL(nn.Module): \"\"\" Neural spline flow, coupling layer. [Durkan et al. 2019] \"\"\"","lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld,","Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim,","= self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \"\"\" Neural spline","self.K, dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim","tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x = torch.zeros_like(z)","H, D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i -","x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha return","dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L","layer. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K = 5, B","nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self,","+ \\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \"\"\" Planar flow.","FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\" def __init__(self, in_dim, out_dim, hidden_dim):","__init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim),","0], out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det","mu + torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class","1x1 convolution. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim =","Neural spline flow, auto-regressive. [Durkan et al. 2019] \"\"\" def __init__(self, dim, K","x @ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return","= x @ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S)))","class Radial(nn.Module): \"\"\" Radial flow. z = f(x) = = x + β","torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K","+ r) ** 2) return z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected","class NSF_AR(nn.Module): \"\"\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \"\"\" def","x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det","lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out =","t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower =","__init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma","torch.softmax(H, dim = 1) W, H = 2 * self.B * W, 2","import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from","= -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \"\"\" Invertible 1x1 convolution. [Kingma and","else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:,","dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self):","z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower -","hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K = K","z = x @ self.P @ L @ (U + torch.diag(self.S)) log_det =","1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W)","B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim","= self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha =","\"\"\" ActNorm layer. [Kingma and Dhariwal, 2018.] \"\"\" def __init__(self, dim): super().__init__() self.dim","hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \"\"\"","__init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim","dim = 1), torch.softmax(H, dim = 1) W, H = 2 * self.B",":i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i]","= base_network(dim // 2, (3 * K - 1) * dim // 2,","hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x)","- 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1) W,","+ beta * h * (x - self.x0) log_det = (n - 1)","self.K = K self.B = B self.f1 = base_network(dim // 2, (3 *","1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x","- 1) W, H, D = torch.split(out, self.K, dim = 2) W, H","+ phi @ u) + 1e-4) return z, log_det def backward(self, z): raise","x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i","return z, log_det class FCNN(nn.Module): \"\"\" Simple fully connected neural network. \"\"\" def","z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim //","(x > 0).type(torch.FloatTensor) + \\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x:","= torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:]","math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\" Given x,","mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i] -","nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim),","= torch.triu(self.U, diagonal = 1) z = x @ self.P @ L @","log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self,","0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\ (x <","inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim =","dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\ torch.sum(-s2_transformed, dim=1) return x, log_det class","self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim //","init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \"\"\"","torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\ torch.sum(s2_transformed,"],"string":"[\n \"= unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B) log_det\",\n \"@ self.w, 1) + self.b z = x + u * self.h(lin) phi\",\n \"nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self, x):\",\n \"x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def\",\n \"1) W, H = 2 * self.B * W, 2 * self.B *\",\n \"- 1 / 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x)\",\n \"== 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:,\",\n \"forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return\",\n \"+ beta * h) + \\\\ torch.log(1 + beta * h - \\\\\",\n \"self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D =\",\n \"+ \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow.\",\n \"/ torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @ self.w,\",\n \"// 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def\",\n \"F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det +=\",\n \"self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det\",\n \"torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @ L @\",\n \"W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x\",\n \"in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(),\",\n \"// 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper\",\n \"2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2,\",\n \"+ scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin\",\n \"H, D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim\",\n \"ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim\",\n \"// 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim\",\n \"function must be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x),\",\n \"= torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0],\",\n \"= torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype =\",\n \"NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def\",\n \"out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det +=\",\n \"s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper)\",\n \"reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0])\",\n \"3 * self.K - 1) W, H, D = torch.split(out, self.K, dim =\",\n \"inverse = True, tail_bound = self.B) log_det += ld return x, log_det class\",\n \"self.u)) - self.w @ self.u - 1 u = self.u + scal *\",\n \"def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P,\",\n \"x + β h(α, r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def\",\n \"inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim //\",\n \"= unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound = self.B)\",\n \"self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param,\",\n \"= torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0:\",\n \"W, H, D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W,\",\n \"\\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim))\",\n \"z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z =\",\n \"self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed =\",\n \"= self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x =\",\n \"self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z\",\n \"hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param =\",\n \"math import numpy as np import scipy as sp import scipy.linalg import torch\",\n \"1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det =\",\n \"dim self.K = K self.B = B self.f1 = base_network(dim // 2, (3\",\n \"inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return\",\n \"F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse =\",\n \"in range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters()\",\n \"log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self,\",\n \"flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x)\",\n \"log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z -\",\n \"\\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim\",\n \"= 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2))\",\n \"x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper\",\n \"= f(x) = = x + β h(α, r)(z − z0) [Rezende and\",\n \"z[:, i], W, H, D, inverse = True, tail_bound = self.B) log_det +=\",\n \"ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0])\",\n \"1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:,\",\n \"hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1) * dim\",\n \"ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B)\",\n \"self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim):\",\n \"log_det += ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det\",\n \"log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i ==\",\n \"D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B)\",\n \"= torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim = 2),\",\n \"nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def\",\n \"dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper\",\n \"OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim):\",\n \"x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det\",\n \"init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and the\",\n \"for i in range(1, dim): self.layers += [base_network(i, 3 * K - 1,\",\n \"\\\"\\\"\\\" Planar flow. z = f(x) = x + u h(wᵀx + b)\",\n \"@ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @\",\n \"z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal =\",\n \"t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed)\",\n \"+= ld return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det =\",\n \"1)) for i in range(1, dim): self.layers += [base_network(i, 3 * K -\",\n \"8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim //\",\n \"W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H\",\n \"Given x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape\",\n \"* self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z,\",\n \"for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 *\",\n \"nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def\",\n \"dim = 2), torch.softmax(H, dim = 2) W, H = 2 * self.B\",\n \"torch.softmax(H, dim = 2) W, H = 2 * self.B * W, 2\",\n \"r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\"\",\n \"lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) }\",\n \"= nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def\",\n \"z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return\",\n \"def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x):\",\n \"i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det +=\",\n \"torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None\",\n \"import numpy as np import scipy as sp import scipy.linalg import torch import\",\n \"2) W, H = 2 * self.B * W, 2 * self.B *\",\n \"F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x)\",\n \"= torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i])\",\n \"\\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__()\",\n \"- t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1)\",\n \"2, (3 * K - 1) * dim // 2, hidden_dim) def forward(self,\",\n \"self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def\",\n \"= self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x\",\n \"self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower,\",\n \"+= [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x):\",\n \"= None def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim))\",\n \"spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K =\",\n \"lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu:\",\n \"i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i -\",\n \"1), torch.softmax(H, dim = 1) W, H = 2 * self.B * W,\",\n \"= self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper],\",\n \"x + beta * h * (x - self.x0) log_det = (n -\",\n \"torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed,\",\n \"h - \\\\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return\",\n \"log_det += alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and\",\n \"dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim =\",\n \"------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h ==\",\n \"return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z\",\n \"True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper],\",\n \"-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given\",\n \"tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2,\",\n \"+ 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has no\",\n \"- torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x <\",\n \"else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) +\",\n \"Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim =\",\n \"torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z\",\n \"self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1)\",\n \"i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self,\",\n \"i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound\",\n \"def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1))\",\n \"log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module):\",\n \"= dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim):\",\n \"torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1)\",\n \"= torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha =\",\n \"return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al.\",\n \"-1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P\",\n \"return z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for\",\n \"def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim):\",\n \"self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers +=\",\n \"/ (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z =\",\n \"torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float))\",\n \"NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z =\",\n \"= dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype =\",\n \"torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return\",\n \"= (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class\",\n \"self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1)\",\n \"torch.log(1 + beta * h - \\\\ beta * r / (torch.exp(self.log_alpha) +\",\n \"convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim\",\n \"// 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2\",\n \"dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2],\",\n \"h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 +\",\n \"= self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:,\",\n \"and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r = torch.norm(x -\",\n \"= torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H = 2\",\n \"= (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det\",\n \"= 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal =\",\n \"= -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x\",\n \"2, (3 * K - 1) * dim // 2, hidden_dim) self.f2 =\",\n \"= x + β h(α, r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\"\",\n \"= dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param =\",\n \"log_det = (n - 1) * torch.log(1 + beta * h) + \\\\\",\n \"(x - self.x0) log_det = (n - 1) * torch.log(1 + beta *\",\n \"self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i]) W, H,\",\n \"flow. z = f(x) = x + u h(wᵀx + b) [<NAME> Mohamed,\",\n \"-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z\",\n \"def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) +\",\n \"self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K -\",\n \"= torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return\",\n \"dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper =\",\n \"x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal\",\n \"x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda\",\n \"- \\\\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z,\",\n \"W, 2 * self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS(\",\n \"Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim =\",\n \"torch.exp(self.beta)) z = x + beta * h * (x - self.x0) log_det\",\n \"__init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList()\",\n \"i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha return x,\",\n \"K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K -\",\n \"def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def\",\n \"= torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z - self.mu)\",\n \"= torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv =\",\n \"auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B\",\n \"dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim //\",\n \"(lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper =\",\n \"x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim,\",\n \"def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U =\",\n \"neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim,\",\n \"\\\\ beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det\",\n \"layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim\",\n \"alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0])\",\n \"= (n - 1) * torch.log(1 + beta * h) + \\\\ torch.log(1\",\n \"= 2), torch.softmax(H, dim = 2) W, H = 2 * self.B *\",\n \"import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note that\",\n \"= torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim = 1),\",\n \"for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1]\",\n \"torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out\",\n \"supported non-linearities: note that the function must be invertible functional_derivatives = { torch.tanh:\",\n \"== torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1\",\n \"log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,))\",\n \"1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 *\",\n \"= -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W =\",\n \"def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed\",\n \"* dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper\",\n \"\\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim\",\n \"- self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\"\",\n \"self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0],\",\n \"backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if\",\n \"1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2)\",\n \"2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W,\",\n \"else: out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K,\",\n \"self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det\",\n \"def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim):\",\n \"2), torch.softmax(H, dim = 2) W, H = 2 * self.B * W,\",\n \"self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def\",\n \"hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x)\",\n \"2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed\",\n \"as np import scipy as sp import scipy.linalg import torch import torch.nn as\",\n \"= F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse\",\n \"= base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2,\",\n \"* H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W,\",\n \"self.x0) log_det = (n - 1) * torch.log(1 + beta * h) +\",\n \"= torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S =\",\n \"z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det\",\n \"self.K, dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim\",\n \"= self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper],\",\n \"2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\"\",\n \"* K - 1) * dim // 2, hidden_dim) def forward(self, x): log_det\",\n \"* self.K - 1) W, H, D = torch.split(out, self.K, dim = 2)\",\n \"and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha =\",\n \"torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L,\",\n \"self.K - 1) W, H, D = torch.split(init_param, self.K, dim = 1) else:\",\n \"torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) *\",\n \"out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i]\",\n \"r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta\",\n \"nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim):\",\n \"D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B)\",\n \"hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def\",\n \"= dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 =\",\n \"log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det\",\n \"* H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D,\",\n \"torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0],\",\n \"* self.B * W, 2 * self.B * H D = F.softplus(D) lower,\",\n \"return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\"\",\n \"= nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1,\",\n \"z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim))\",\n \"f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def\",\n \"W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H\",\n \"Planar flow. z = f(x) = x + u h(wᵀx + b) [<NAME>\",\n \"/ (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple\",\n \"log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L\",\n \"as F from nf.utils import unconstrained_RQS # supported non-linearities: note that the function\",\n \"for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self):\",\n \"ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan\",\n \"diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z =\",\n \"F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound\",\n \"forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:,\",\n \"self.w, 1) + self.b z = x + u * self.h(lin) phi =\",\n \"z, log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i\",\n \"L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L,\",\n \"= self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed\",\n \"u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__()\",\n \"self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim)\",\n \"None def forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U\",\n \"self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S\",\n \"class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self,\",\n \"lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed\",\n \"self.w @ self.u - 1 u = self.u + scal * self.w /\",\n \"self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def\",\n \"K - 1)) for i in range(1, dim): self.layers += [base_network(i, 3 *\",\n \"z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r = torch.norm(x\",\n \":self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2,\",\n \"self.B * W, 2 * self.B * H D = F.softplus(D) z[:, i],\",\n \"* self.K - 1) W, H, D = torch.split(init_param, self.K, dim = 1)\",\n \"hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2,\",\n \":i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu +\",\n \"def backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\"\",\n \"dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K -\",\n \"backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed =\",\n \"no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) = =\",\n \"alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha\",\n \"phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) +\",\n \"base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim\",\n \"self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W)\",\n \"= 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)\",\n \"+ torch.log(1 + torch.exp(self.beta)) z = x + beta * h * (x\",\n \"* self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:,\",\n \"dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U\",\n \"+ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @ self.P\",\n \"the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u =\",\n \"self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K -\",\n \"+ torch.exp(self.beta)) z = x + beta * h * (x - self.x0)\",\n \"(x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def\",\n \"// 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3\",\n \"\\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) +\",\n \"z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural\",\n \"= 1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv =\",\n \"self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow,\",\n \"def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float))\",\n \"out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim,\",\n \"log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu, alpha\",\n \"NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z\",\n \"* torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z):\",\n \"= out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:,\",\n \"- 1) W, H, D = torch.split(init_param, self.K, dim = 1) else: out\",\n \"upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed =\",\n \"flow. z = f(x) = = x + β h(α, r)(z − z0)\",\n \"self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i],\",\n \"* z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm\",\n \"W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self,\",\n \"out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H,\",\n \"import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as\",\n \"= 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det\",\n \"elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u\",\n \"upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det\",\n \"-0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) *\",\n \"h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h\",\n \"x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\"\",\n \"(torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully\",\n \"= 2) W, H = 2 * self.B * W, 2 * self.B\",\n \"5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim =\",\n \"x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1\",\n \"log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim,\",\n \"1) + self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin)\",\n \"returns z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu,\",\n \"\\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers\",\n \"class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim):\",\n \"W, H, D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i\",\n \"* self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @\",\n \"in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out\",\n \"= 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:,\",\n \"[Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim self.mu\",\n \"[Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim W,\",\n \"dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K -\",\n \"torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self,\",\n \"torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower,\",\n \"< 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x)\",\n \"= torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype =\",\n \"// 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2\",\n \"ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__()\",\n \"torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x\",\n \"def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det =\",\n \"F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01,\",\n \"3 * self.K - 1) W, H, D = torch.split(init_param, self.K, dim =\",\n \"i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param,\",\n \"x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al. 2019]\",\n \"= FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.f1\",\n \"@ self.u - 1 u = self.u + scal * self.w / torch.norm(self.w)\",\n \"= torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z =\",\n \"= torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det\",\n \"= t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det =\",\n \"= x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim\",\n \"= self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed\",\n \"+ self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x =\",\n \"+= torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 *\",\n \"unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B) log_det +=\",\n \"torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @ self.P @\",\n \"U = torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U\",\n \"dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param\",\n \"1) * torch.log(1 + beta * h) + \\\\ torch.log(1 + beta *\",\n \"= nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma)\",\n \"@ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det\",\n \"(upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed,\",\n \"nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim),\",\n \"* torch.log(1 + beta * h) + \\\\ torch.log(1 + beta * h\",\n \"x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\"\",\n \"= z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\"\",\n \"= self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D\",\n \"np import scipy as sp import scipy.linalg import torch import torch.nn as nn\",\n \"W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out =\",\n \"range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def\",\n \"out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H,\",\n \"s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower,\",\n \"def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma)\",\n \"super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1))\",\n \"+ \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper =\",\n \"ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld\",\n \"u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has\",\n \"torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i\",\n \"x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n\",\n \"+ upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed\",\n \"B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i\",\n \"= torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0],\",\n \"math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in\",\n \"self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed =\",\n \"@ self.u)) - self.w @ self.u - 1 u = self.u + scal\",\n \"super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0,\",\n \"dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu\",\n \"= 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim\",\n \"the function must be invertible functional_derivatives = { torch.tanh: lambda x: 1 -\",\n \"t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) +\",\n \"dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L\",\n \"torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float),\",\n \"init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x): z =\",\n \"D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1)\",\n \"dim = 2) W, H = 2 * self.B * W, 2 *\",\n \"= nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w,\",\n \"= self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:,\",\n \"nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers += [base_network(i,\",\n \"self.dim = dim self.K = K self.B = B self.f1 = base_network(dim //\",\n \"(x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z =\",\n \"= (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper\",\n \"x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\\",\n \"alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.]\",\n \"log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive.\",\n \"0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) =\",\n \"= nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self,\",\n \"B self.f1 = base_network(dim // 2, (3 * K - 1) * dim\",\n \"self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @\",\n \"backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial\",\n \"auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8,\",\n \"if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W,\",\n \"Given x, returns z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h\",\n \"// 2, 3 * self.K - 1) W, H, D = torch.split(out, self.K,\",\n \"self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed =\",\n \"= (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det =\",\n \"1) z = x @ self.P @ L @ (U + torch.diag(self.S)) log_det\",\n \"non-linearities: note that the function must be invertible functional_derivatives = { torch.tanh: lambda\",\n \"2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 *\",\n \"nf.utils import unconstrained_RQS # supported non-linearities: note that the function must be invertible\",\n \"b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity\",\n \"self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x,\",\n \"/ torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1\",\n \"2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2,\",\n \"1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha\",\n \"return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\")\",\n \"= 1) else: out = self.layers[i - 1](x[:, :i]) W, H, D =\",\n \"NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self,\",\n \"-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given\",\n \"// 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)\",\n \"- 1) * dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0])\",\n \"= x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det =\",\n \"D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim\",\n \"log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim //\",\n \"= -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta * h\",\n \"tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1),\",\n \"x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim //\",\n \"-math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant\",\n \"= nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self,\",\n \"torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z - self.mu) /\",\n \"x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017]\",\n \"= self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim =\",\n \"torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3\",\n \"// 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3\",\n \"+ \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @\",\n \"self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim //\",\n \"nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U,\",\n \"log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et al. 2019]\",\n \"et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim\",\n \"self.f2 = base_network(dim // 2, (3 * K - 1) * dim //\",\n \"H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H =\",\n \"W, 2 * self.B * H D = F.softplus(D) x[:, i], ld =\",\n \"self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1\",\n \"torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked\",\n \"2 * self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS(\",\n \"= nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U =\",\n \"\\\\ torch.log(1 + beta * h - \\\\ beta * r / (torch.exp(self.log_alpha)\",\n \"D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B)\",\n \"F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B)\",\n \"return z, log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma)\",\n \"= F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det\",\n \"self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @\",\n \"functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return\",\n \"// 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim //\",\n \"range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5))\",\n \"+ \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor)\",\n \"backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:,\",\n \"{ torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x >\",\n \"log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param =\",\n \"def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed\",\n \"nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return\",\n \"self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype\",\n \"1) else: out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out,\",\n \"− z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 =\",\n \"upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound =\",\n \"+ r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x +\",\n \"<filename>nf/flows.py import math import numpy as np import scipy as sp import scipy.linalg\",\n \"nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha,\",\n \"flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K =\",\n \"def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2],\",\n \"* self.B * W, 2 * self.B * H D = F.softplus(D) upper,\",\n \"8, base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B =\",\n \"self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"self.K - 1) W, H, D = torch.split(out, self.K, dim = 2) W,\",\n \"= x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self,\",\n \"nn import torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS\",\n \"0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar\",\n \"2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W,\",\n \"\\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n =\",\n \"2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim\",\n \"dim = 1) W, H = 2 * self.B * W, 2 *\",\n \"= 5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim\",\n \"self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H,\",\n \"(F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @\",\n \"D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D,\",\n \"= torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal =\",\n \"= dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P\",\n \"fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network =\",\n \"H, D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim\",\n \"= self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D\",\n \"al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B = 3, hidden_dim\",\n \"= nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers +=\",\n \"torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution.\",\n \"base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower, upper =\",\n \"0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D =\",\n \"self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u -\",\n \"H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim =\",\n \"torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u = self.u +\",\n \"1 / 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det\",\n \"= x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) +\",\n \"sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float))\",\n \"// 2, dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim\",\n \"log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u = self.u\",\n \"= base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2,\",\n \"and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim W, _\",\n \"MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim,\",\n \"W, H, D, inverse = True, tail_bound = self.B) log_det += ld return\",\n \"t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed)\",\n \"backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return\",\n \"h) + \\\\ torch.log(1 + beta * h - \\\\ beta * r\",\n \"i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K\",\n \"super().__init__() self.dim = dim self.K = K self.B = B self.f1 = base_network(dim\",\n \"init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns\",\n \"hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim),\",\n \"preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8,\",\n \"torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U + torch.diag(self.S))\",\n \"- 1)) for i in range(1, dim): self.layers += [base_network(i, 3 * K\",\n \"base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i\",\n \"self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1)\",\n \"= nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i,\",\n \"-math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z\",\n \"* torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed)\",\n \"x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K\",\n \"self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z =\",\n \"- 1 u = self.u + scal * self.w / torch.norm(self.w) else: raise\",\n \"* h * (x - self.x0) log_det = (n - 1) * torch.log(1\",\n \"= F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det\",\n \"-math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i\",\n \"self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self,\",\n \"= nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal =\",\n \"z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x,\",\n \"torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha)\",\n \"2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim\",\n \"\\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1\",\n \"2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower\",\n \"= functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4)\",\n \"= out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu) /\",\n \"Radial flow. z = f(x) = = x + β h(α, r)(z −\",\n \"beta * h * (x - self.x0) log_det = (n - 1) *\",\n \"i in range(1, dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)]\",\n \"nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module):\",\n \"class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self,\",\n \"= unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim =\",\n \"W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower,\",\n \"(torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x\",\n \"1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W,\",\n \"math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and\",\n \"lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed\",\n \"+= torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1), log_det def\",\n \"torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K\",\n \"x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et al.\",\n \"0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i])\",\n \"= 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)\",\n \"2 * self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper,\",\n \"x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim):\",\n \"* self.B * W, 2 * self.B * H D = F.softplus(D) z[:,\",\n \"self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in\",\n \"\\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype =\",\n \"// 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1)\",\n \"z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper)\",\n \"torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F\",\n \"self.dim = dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1\",\n \"self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:,\",\n \"torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out\",\n \"torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower *\",\n \"U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype\",\n \"as sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init as\",\n \"dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K -\",\n \"forward(self, x): L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U,\",\n \"self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U\",\n \"\\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z\",\n \"dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z\",\n \"-torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et\",\n \"self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed =\",\n \"= torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1)\",\n \"* self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W,\",\n \"unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)\",\n \"dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim)\",\n \"// 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper =\",\n \"= torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z):\",\n \"# supported non-linearities: note that the function must be invertible functional_derivatives = {\",\n \"dim = 1) return torch.cat([lower, upper], dim = 1), log_det def backward(self, z):\",\n \"nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving\",\n \"out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow.\",\n \"= self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param, self.K,\",\n \"\\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh:\",\n \"+ lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1)\",\n \"1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim\",\n \"log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param =\",\n \"dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim\",\n \"__init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def\",\n \"unconstrained_RQS # supported non-linearities: note that the function must be invertible functional_derivatives =\",\n \"2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim\",\n \"(3 * K - 1) * dim // 2, hidden_dim) def forward(self, x):\",\n \"torch.log(1 + beta * h) + \\\\ torch.log(1 + beta * h -\",\n \"i] = (x[:, i] - mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)),\",\n \"+ u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh):\",\n \"+ u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 +\",\n \"and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim self.mu =\",\n \"r) ** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural\",\n \"dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x):\",\n \"D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x\",\n \"phi @ u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar\",\n \"\\\"\\\"\\\" Radial flow. z = f(x) = = x + β h(α, r)(z\",\n \"raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) + self.b\",\n \"lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x + u\",\n \"self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x =\",\n \"+= [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, -\",\n \"= 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta))\",\n \"0], out[:, 1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det\",\n \"= True, tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module):\",\n \"\\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim,\",\n \"log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:]\",\n \"alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu)\",\n \"Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) = = x + β h(α,\",\n \"hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:]\",\n \"K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2,\",\n \"lower = t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det\",\n \"\\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self,\",\n \"2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim //\",\n \"dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype\",\n \"def forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. Returns\",\n \"tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim\",\n \"torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu,\",\n \"= x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower)\",\n \"(U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det =\",\n \"W, 2 * self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS(\",\n \"H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z):\",\n \"as init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities:\",\n \"- self.x0) log_det = (n - 1) * torch.log(1 + beta * h)\",\n \"self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"} class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) = x + u\",\n \"log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r = torch.norm(x - self.x0) h\",\n \"torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv:\",\n \"log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim //\",\n \"forward(self, x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed =\",\n \"K = 5, B = 3, hidden_dim = 8, base_network = FCNN): super().__init__()\",\n \"dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv\",\n \"torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H,\",\n \"scipy.linalg import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional\",\n \"H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True,\",\n \"def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma)\",\n \"= nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self,\",\n \"\\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u\",\n \"init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns\",\n \"network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim),\",\n \"torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim = 2), torch.softmax(H,\",\n \"torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param\",\n \"lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower)\",\n \"init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\"\",\n \"* torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\\",\n \"* W, 2 * self.B * H D = F.softplus(D) upper, ld =\",\n \"z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i\",\n \"hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim\",\n \"torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from nf.utils\",\n \"= { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x\",\n \"unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)\",\n \"log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def\",\n \"super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in\",\n \"= K self.B = B self.f1 = base_network(dim // 2, (3 * K\",\n \"2, 3 * self.K - 1) W, H, D = torch.split(out, self.K, dim\",\n \"1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu\",\n \"= unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return\",\n \"nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta,\",\n \"backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\\\\",\n \"dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive\",\n \"// 2, (3 * K - 1) * dim // 2, hidden_dim) self.f2\",\n \"D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W, dim =\",\n \"L = torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal\",\n \"+ torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not\",\n \"2, hidden_dim) self.t2 = base_network(dim // 2, dim // 2, hidden_dim) self.s2 =\",\n \"self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i]\",\n \"self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim),\",\n \"unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound = self.B) log_det\",\n \"__init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim))\",\n \"* W, 2 * self.B * H D = F.softplus(D) lower, ld =\",\n \"scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u =\",\n \"out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det -=\",\n \"dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2],\",\n \"torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported\",\n \"return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.]\",\n \"log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim //\",\n \"+= alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal,\",\n \"dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype\",\n \"self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param, self.K, dim\",\n \"= unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim =\",\n \"= K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K\",\n \"= self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w\",\n \"> 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\"\",\n \"upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed +\",\n \"H D = F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False,\",\n \"2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim,\",\n \"H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1,\",\n \"base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim // 2,\",\n \"W, H = 2 * self.B * W, 2 * self.B * H\",\n \"(n - 1) * torch.log(1 + beta * h) + \\\\ torch.log(1 +\",\n \"3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K =\",\n \"ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim\",\n \"torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def backward(self, z):\",\n \"@ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z):\",\n \"= torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta =\",\n \"forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if\",\n \"i] log_det += alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma\",\n \"i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else:\",\n \"not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U =\",\n \"= = x + β h(α, r)(z − z0) [Rezende and Mohamed 2015]\",\n \"torch.unsqueeze(x @ self.w, 1) + self.b z = x + u * self.h(lin)\",\n \"super().__init__() self.dim = dim self.t1 = base_network(dim // 2, dim // 2, hidden_dim)\",\n \"self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x *\",\n \"self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i],\",\n \"= z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha =\",\n \"= sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype =\",\n \"* -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor)\",\n \"r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0\",\n \"z, log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det\",\n \"dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim)\",\n \"from nf.utils import unconstrained_RQS # supported non-linearities: note that the function must be\",\n \"n = x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha)\",\n \"2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 *\",\n \"x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r\",\n \"H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W, H,\",\n \"W, 2 * self.B * H D = F.softplus(D) z[:, i], ld =\",\n \"D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse =\",\n \"super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), )\",\n \"super().__init__() self.dim = dim self.K = K self.B = B self.layers = nn.ModuleList()\",\n \"z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim\",\n \"nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume\",\n \"torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3\",\n \"z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in\",\n \"= torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0:\",\n \"x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i ==\",\n \"diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W\",\n \"H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse\",\n \"tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural\",\n \"torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor)\",\n \"if not self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U\",\n \"flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN):\",\n \"log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3\",\n \"-torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal,\",\n \"z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def __init__(self,\",\n \"h(α, r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__()\",\n \"inverse=False, tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x =\",\n \"log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module):\",\n \"= z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim\",\n \"def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self,\",\n \"note that the function must be invertible functional_derivatives = { torch.tanh: lambda x:\",\n \"self.K = K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 *\",\n \"upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out\",\n \"* dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K\",\n \"torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u\",\n \"= torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1 u = self.u\",\n \"scipy as sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init\",\n \"return z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal\",\n \"H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper],\",\n \"beta * r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class\",\n \"torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\"\",\n \"L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal =\",\n \"2 * self.B * H D = F.softplus(D) z[:, i], ld = unconstrained_RQS(\",\n \"dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3\",\n \"backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i\",\n \"= torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def backward(self,\",\n \"@ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z,\",\n \"\\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__()\",\n \"math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|.\",\n \"H = 2 * self.B * W, 2 * self.B * H D\",\n \"nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) +\",\n \"x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim\",\n \"Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h\",\n \"\\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta =\",\n \"et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B = 3,\",\n \"has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) =\",\n \"** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network.\",\n \"out = self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim\",\n \"1) W, H, D = torch.split(out, self.K, dim = 2) W, H =\",\n \"z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer.\",\n \"self.dim // 2, 3 * self.K - 1) W, H, D = torch.split(out,\",\n \"= torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\"\",\n \"in range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K -\",\n \"diagonal = 1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv\",\n \"FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.layers =\",\n \"= F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det\",\n \"/ 2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det =\",\n \"log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L, diagonal = -1)\",\n \"1) W, H, D = torch.split(init_param, self.K, dim = 1) else: out =\",\n \"f(x) = = x + β h(α, r)(z − z0) [Rezende and Mohamed\",\n \"Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1))\",\n \"torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x,\",\n \"= F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False,\",\n \"-torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta * h *\",\n \"[Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B =\",\n \"base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim\",\n \"= nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"m, n = x.shape r = torch.norm(x - self.x0) h = 1 /\",\n \"* K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim //\",\n \"z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu):\",\n \"F from nf.utils import unconstrained_RQS # supported non-linearities: note that the function must\",\n \"* h - \\\\ beta * r / (torch.exp(self.log_alpha) + r) ** 2)\",\n \"FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.f1 =\",\n \"dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float))\",\n \"nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float)) def forward(self, x):\",\n \"= FCNN): super().__init__() self.dim = dim self.K = K self.B = B self.layers\",\n \"is not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z =\",\n \"z = z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha\",\n \"self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim),\",\n \"self.W_inv: L = torch.tril(self.L, diagonal = -1) + \\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U,\",\n \"import scipy.linalg import torch import torch.nn as nn import torch.nn.init as init import\",\n \"* torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\\",\n \"if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i\",\n \"x.shape r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r)\",\n \"range(self.dim): if i == 0: mu, alpha = self.initial_param[0], self.initial_param[1] else: out =\",\n \"W, H, D = torch.split(out, self.K, dim = 1) W, H = torch.softmax(W,\",\n \"H D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H,\",\n \"self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H,\",\n \"= F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse = True,\",\n \"2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype\",\n \"self.B = B self.f1 = base_network(dim // 2, (3 * K - 1)\",\n \"dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim):\",\n \"= x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def\",\n \"U = torch.triu(self.U, diagonal = 1) z = x @ self.P @ L\",\n \"torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H = 2 *\",\n \"dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def\",\n \"2, dim // 2, hidden_dim) def forward(self, x): lower, upper = x[:,:self.dim //\",\n \"coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5,\",\n \"* torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower\",\n \"i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z, log_det def\",\n \"et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim\",\n \"not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x\",\n \"log_det += ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling\",\n \"super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U =\",\n \"torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in range(self.dim): if i == 0: init_param\",\n \"mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x\",\n \"r = torch.norm(x - self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta\",\n \"[Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__()\",\n \"β h(α, r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim):\",\n \"z = x + beta * h * (x - self.x0) log_det =\",\n \"invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda\",\n \"connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential(\",\n \"Neural spline flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim,\",\n \"beta * h - \\\\ beta * r / (torch.exp(self.log_alpha) + r) **\",\n \"/ torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x =\",\n \"x): lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower)\",\n \"lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return\",\n \"- 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] =\",\n \"F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det +=\",\n \"= 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)\",\n \"* K - 1)) for i in range(1, dim): self.layers += [base_network(i, 3\",\n \"2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B = 3, hidden_dim =\",\n \"s2_transformed = self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower)\",\n \"W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P,\",\n \"(x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module):\",\n \"z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det\",\n \"lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) +\",\n \"K self.B = B self.f1 = base_network(dim // 2, (3 * K -\",\n \"\\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(),\",\n \"2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta\",\n \"x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class\",\n \"* self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x\",\n \"= self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed\",\n \"upper = t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper)\",\n \"* self.B * H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:,\",\n \"log_det def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(x.shape[0]) for i in\",\n \"dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim =\",\n \"sp import scipy.linalg import torch import torch.nn as nn import torch.nn.init as init\",\n \"spline flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K\",\n \"self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim)\",\n \"diagonal = 1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal\",\n \"dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma =\",\n \"mu, alpha = self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu,\",\n \"that the function must be invertible functional_derivatives = { torch.tanh: lambda x: 1\",\n \"@ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline\",\n \"and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u\",\n \"log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4) return z, log_det def\",\n \"1 u = self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity\",\n \"raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z\",\n \"class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self,\",\n \"def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim,\",\n \"self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible\",\n \"log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim // 2, 3\",\n \"import scipy as sp import scipy.linalg import torch import torch.nn as nn import\",\n \"t2_transformed + lower * torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed,\",\n \"scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin =\",\n \"log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det\",\n \"z = f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015]\",\n \"= 8, base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B\",\n \"mu, alpha = out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha)\",\n \"def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2],\",\n \"[base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z\",\n \"2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z =\",\n \"self.f1 = base_network(dim // 2, (3 * K - 1) * dim //\",\n \"lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) +\",\n \"torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) = x +\",\n \"2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed) *\",\n \"i], W, H, D, inverse = True, tail_bound = self.B) log_det += ld\",\n \"the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r = torch.norm(x - self.x0)\",\n \"= -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan\",\n \"i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H,\",\n \"forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al.\",\n \"= t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower\",\n \"al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim =\",\n \"self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for i in range(1, dim): self.layers\",\n \"(U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if\",\n \"= 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1)\",\n \"0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x >\",\n \"\\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if\",\n \"upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det\",\n \"W, H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld, dim\",\n \"torch.tensor(P, dtype = torch.float) self.L = nn.Parameter(torch.tensor(L, dtype = torch.float)) self.S = nn.Parameter(torch.tensor(np.diag(U),\",\n \"torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x\",\n \"z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim //\",\n \"1)) self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal = -1)\",\n \"H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H =\",\n \"2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed + upper *\",\n \"x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\"\",\n \"K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1\",\n \"upper, W, H, D, inverse = True, tail_bound = self.B) log_det += torch.sum(ld,\",\n \"log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma\",\n \"super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma = nn.Parameter(torch.zeros(dim,\",\n \"Invertible 1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim\",\n \"torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:, :i]) W,\",\n \"log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def\",\n \"x, returns z and the log-determinant log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in\",\n \"- 1) * torch.log(1 + beta * h) + \\\\ torch.log(1 + beta\",\n \"nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1))\",\n \"* K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2,\",\n \"+ torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S)))\",\n \"* torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) = x\",\n \"in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w\",\n \"+ \\\\ torch.log(1 + beta * h - \\\\ beta * r /\",\n \"= torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0:\",\n \"log|df/dx|. \\\"\\\"\\\" m, n = x.shape r = torch.norm(x - self.x0) h =\",\n \"D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i - 1](x[:,\",\n \"range(self.dim): if i == 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1)\",\n \"tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2,\",\n \"functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x:\",\n \"hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim\",\n \"init import torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note\",\n \"1] x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha\",\n \"D = F.softplus(D) z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D,\",\n \"x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self,\",\n \"upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower, upper], dim=1) log_det\",\n \"= base_network(dim // 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2,\",\n \"nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class\",\n \"= B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1)) for\",\n \"forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. Returns -------\",\n \"= torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for i in range(self.dim): if\",\n \"= nn.Parameter(torch.Tensor(dim)) self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) = x + u h(wᵀx +\",\n \"+= torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 *\",\n \"* (x - self.x0) log_det = (n - 1) * torch.log(1 + beta\",\n \"= torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:]\",\n \"self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\")\",\n \"__init__(self, dim): super().__init__() self.dim = dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L,\",\n \"x): z = x * torch.exp(self.log_sigma) + self.mu log_det = torch.sum(self.log_sigma) return z,\",\n \"torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et\",\n \"/ 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i\",\n \"init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and the\",\n \"RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim,\",\n \"self.mu log_det = torch.sum(self.log_sigma) return z, log_det def backward(self, z): x = (z\",\n \"1) out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W,\",\n \"def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 =\",\n \"math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x, returns z and\",\n \"self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi @ u)\",\n \"= nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters()\",\n \"nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2,\",\n \"upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1,\",\n \"[<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w\",\n \"self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1,\",\n \"= torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det\",\n \"D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim\",\n \"= z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper)\",\n \"- self.x0) h = 1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) +\",\n \"log_det class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\"\",\n \"self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w\",\n \"import torch.nn.init as init import torch.nn.functional as F from nf.utils import unconstrained_RQS #\",\n \"h * (x - self.x0) log_det = (n - 1) * torch.log(1 +\",\n \"L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self,\",\n \"return z, log_det def backward(self, z): lower, upper = z[:,:self.dim // 2], z[:,self.dim\",\n \"nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x):\",\n \"inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) = = x +\",\n \"torch.softmax(W, dim = 2), torch.softmax(H, dim = 2) W, H = 2 *\",\n \"self.B * W, 2 * self.B * H D = F.softplus(D) upper, ld\",\n \"if self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal\",\n \"self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float)) self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal\",\n \"= sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype =\",\n \"self.dim = dim self.K = K self.B = B self.layers = nn.ModuleList() self.init_param\",\n \"= f(x) = x + u h(wᵀx + b) [<NAME> Mohamed, 2015] \\\"\\\"\\\"\",\n \"// 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower - t2_transformed)\",\n \"x[:, i], ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True,\",\n \"return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018]\",\n \"z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1)\",\n \"base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim // 2, dim\",\n \"+= ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer.\",\n \"1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W,\",\n \"1) * dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower,\",\n \"// 2, (3 * K - 1) * dim // 2, hidden_dim) def\",\n \"init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D = torch.split(init_param,\",\n \"[Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim)) self.log_alpha\",\n \"self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det\",\n \"= torch.unsqueeze(x @ self.w, 1) + self.b z = x + u *\",\n \"1 / (torch.exp(self.log_alpha) + r) beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z\",\n \"* W, 2 * self.B * H D = F.softplus(D) x[:, i], ld\",\n \"def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et.\",\n \"t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed)\",\n \"import unconstrained_RQS # supported non-linearities: note that the function must be invertible functional_derivatives\",\n \"z.flip(dims=(1,)) for i in range(self.dim): if i == 0: mu, alpha = self.initial_param[0],\",\n \"self.B * W, 2 * self.B * H D = F.softplus(D) x[:, i],\",\n \"+ b) [<NAME> Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h =\",\n \"log_det def backward(self, z): x = (z - self.mu) / torch.exp(self.log_sigma) log_det =\",\n \"dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers\",\n \"2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1) W,\",\n \"// 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)\",\n \"torch.triu(self.U, diagonal = 1) z = x @ self.P @ L @ (U\",\n \"t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper -\",\n \"flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5,\",\n \"torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L = torch.tril(self.L,\",\n \"= nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b,\",\n \"+ beta * h - \\\\ beta * r / (torch.exp(self.log_alpha) + r)\",\n \"- 1) * dim // 2, hidden_dim) self.f2 = base_network(dim // 2, (3\",\n \"H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1,\",\n \"ld = unconstrained_RQS( z[:, i], W, H, D, inverse = True, tail_bound =\",\n \"upper = x[:,:self.dim // 2], x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed =\",\n \"P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L =\",\n \"(3 * K - 1) * dim // 2, hidden_dim) self.f2 = base_network(dim\",\n \"= mu + torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det\",\n \"2 * self.B * W, 2 * self.B * H D = F.softplus(D)\",\n \"= True, tail_bound = self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower,\",\n \"- 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 /\",\n \"unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1)\",\n \"= torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return\",\n \"def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.layers =\",\n \"= 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K\",\n \"= x + beta * h * (x - self.x0) log_det = (n\",\n \"returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m, n = x.shape r =\",\n \"self.B = B self.layers = nn.ModuleList() self.init_param = nn.Parameter(torch.Tensor(3 * K - 1))\",\n \"= self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not\",\n \"True, tail_bound = self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\"\",\n \"upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1,\",\n \"self.h in (F.elu, F.leaky_relu): u = self.u elif self.h == torch.tanh: scal =\",\n \"dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al.\",\n \"lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld,\",\n \"* W, 2 * self.B * H D = F.softplus(D) z[:, i], ld\",\n \"- self.w @ self.u - 1 u = self.u + scal * self.w\",\n \"\\\"\\\"\\\" m, n = x.shape r = torch.norm(x - self.x0) h = 1\",\n \"return x, log_det class ActNorm(nn.Module): \\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\"\",\n \"= self.t2(upper) s2_transformed = self.s2(upper) lower = t2_transformed + lower * torch.exp(s2_transformed) z\",\n \"alpha = out[:, 0], out[:, 1] x[:, i] = mu + torch.exp(alpha) *\",\n \"2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) *\",\n \"L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W) x = z @ self.W_inv\",\n \"self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim = 1),\",\n \"s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed) x = torch.cat([lower,\",\n \"inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim //\",\n \"algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) = = x\",\n \"+ torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class ActNorm(nn.Module):\",\n \"1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for\",\n \"def forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\"\",\n \"torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is not supported.\\\") lin = torch.unsqueeze(x @ self.w, 1)\",\n \"\\\\ torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) W = self.P @ L\",\n \"\\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim\",\n \"dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def\",\n \"= torch.triu(self.U, diagonal = 1) W = self.P @ L @ (U +\",\n \"beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta)) z = x + beta *\",\n \"self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1) W, H, D =\",\n \"-1) + torch.diag(torch.ones(self.dim)) U = torch.triu(self.U, diagonal = 1) z = x @\",\n \"@ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return z, log_det def\",\n \"base_network(dim // 2, (3 * K - 1) * dim // 2, hidden_dim)\",\n \"(z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module):\",\n \"H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False,\",\n \"@ u) + 1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow\",\n \"class Planar(nn.Module): \\\"\\\"\\\" Planar flow. z = f(x) = x + u h(wᵀx\",\n \"def __init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network\",\n \"reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self, x): z\",\n \"z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K\",\n \"as nn import torch.nn.init as init import torch.nn.functional as F from nf.utils import\",\n \"reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self,\",\n \"2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2,\",\n \"x = torch.cat([lower, upper], dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1)\",\n \"z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.x0 = nn.Parameter(torch.Tensor(dim))\",\n \"3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 /\",\n \"self.B * W, 2 * self.B * H D = F.softplus(D) lower, ld\",\n \") def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh\",\n \"(x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\\",\n \"nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim),\",\n \"== 0: init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1) W, H, D\",\n \"1e-4) return z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic\",\n \"2 * self.B * H D = F.softplus(D) lower, ld = unconstrained_RQS( lower,\",\n \"self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow,\",\n \"dim, K = 5, B = 3, hidden_dim = 8, base_network = FCNN):\",\n \"= nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u,\",\n \"dim W, _ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P =\",\n \"// 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower =\",\n \"self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i]\",\n \"torch.nn.functional as F from nf.utils import unconstrained_RQS # supported non-linearities: note that the\",\n \"self.log_alpha = nn.Parameter(torch.Tensor(1)) self.beta = nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim),\",\n \"self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\" Non-volume preserving flow. [Dinh et. al. 2017] \\\"\\\"\\\" def\",\n \"log|df/dx|. Returns ------- \\\"\\\"\\\" if self.h in (F.elu, F.leaky_relu): u = self.u elif\",\n \"= 1) z = x @ self.P @ L @ (U + torch.diag(self.S))\",\n \"torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor)\",\n \"z, log_det def backward(self, z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class\",\n \"forward(self, x): \\\"\\\"\\\" Given x, returns z and the log-determinant log|df/dx|. \\\"\\\"\\\" m,\",\n \"[Papamakarios et al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__()\",\n \"= torch.sum(torch.log(torch.abs(self.S))) return z, log_det def backward(self, z): if not self.W_inv: L =\",\n \"> 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x\",\n \"D, inverse = True, tail_bound = self.B) log_det += ld return x, log_det\",\n \"= nn.Parameter(torch.Tensor(1)) def reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim))\",\n \"torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z)\",\n \"+ self.b z = x + u * self.h(lin) phi = functional_derivatives[self.h](lin) *\",\n \"torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z,\",\n \"F.softplus(D) lower, ld = unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det +=\",\n \"z[:,:self.dim // 2], z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower\",\n \"= base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x): lower, upper\",\n \"8, base_network=FCNN): super().__init__() self.dim = dim self.layers = nn.ModuleList() self.initial_param = nn.Parameter(torch.Tensor(2)) for\",\n \"= unconstrained_RQS( lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim =\",\n \"+ β h(α, r)(z − z0) [Rezende and Mohamed 2015] \\\"\\\"\\\" def __init__(self,\",\n \"torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower,\",\n \"be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2), F.leaky_relu:\",\n \"self.t1 = base_network(dim // 2, dim // 2, hidden_dim) self.s1 = base_network(dim //\",\n \"z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i ==\",\n \"2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim))\",\n \"[base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1\",\n \"beta * h) + \\\\ torch.log(1 + beta * h - \\\\ beta\",\n \"Mohamed, 2015] \\\"\\\"\\\" def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w =\",\n \"al. 2018] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim =\",\n \":i]) W, H, D = torch.split(out, self.K, dim = 1) W, H =\",\n \"* H D = F.softplus(D) x[:, i], ld = unconstrained_RQS( z[:, i], W,\",\n \"must be invertible functional_derivatives = { torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2),\",\n \"self.reset_parameters() def reset_parameters(self): init.uniform_(self.init_param, - 1 / 2, 1 / 2) def forward(self,\",\n \"torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i == 0: mu,\",\n \"* H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W, H, D,\",\n \"// 2, dim // 2, hidden_dim) self.s1 = base_network(dim // 2, dim //\",\n \"upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0]) lower, upper\",\n \"-= alpha return z.flip(dims=(1,)), log_det def backward(self, z): x = torch.zeros_like(z) log_det =\",\n \"lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:] out =\",\n \"W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out =\",\n \"x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class NSF_AR(nn.Module):\",\n \"lower, W, H, D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out\",\n \"nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b =\",\n \"\\\"\\\"\\\" def __init__(self, dim, K = 5, B = 3, hidden_dim = 8,\",\n \"self.initial_param = nn.Parameter(torch.Tensor(2)) for i in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)]\",\n \"u * self.h(lin) phi = functional_derivatives[self.h](lin) * self.w log_det = torch.log(torch.abs(1 + phi\",\n \"def __init__(self, dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u =\",\n \"dim = 1) else: out = self.layers[i - 1](x[:, :i]) W, H, D\",\n \"hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:, :self.dim //\",\n \"\\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class MAF(nn.Module): \\\"\\\"\\\" Masked auto-regressive flow. [Papamakarios\",\n \"import math import numpy as np import scipy as sp import scipy.linalg import\",\n \"< 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x\",\n \"= self.s2(upper) lower = (lower - t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed\",\n \":self.dim // 2], x[:, self.dim // 2:] out = self.f1(lower).reshape(-1, self.dim // 2,\",\n \"return torch.cat([lower, upper], dim = 1), log_det def backward(self, z): log_det = torch.zeros(z.shape[0])\",\n \"* self.B * H D = F.softplus(D) upper, ld = unconstrained_RQS( upper, W,\",\n \"ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim =\",\n \"// 2, dim // 2, hidden_dim) self.s2 = base_network(dim // 2, dim //\",\n \"torch.inverse(W) x = z @ self.W_inv log_det = -torch.sum(torch.log(torch.abs(self.S))) return x, log_det class\",\n \"hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self, x):\",\n \"= dim self.K = K self.B = B self.f1 = base_network(dim // 2,\",\n \"1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1) W, H\",\n \"2, hidden_dim) self.f2 = base_network(dim // 2, (3 * K - 1) *\",\n \"sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float)\",\n \"dim, nonlinearity=torch.tanh): super().__init__() self.h = nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b\",\n \"out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1]\",\n \"K - 1) * dim // 2, hidden_dim) def forward(self, x): log_det =\",\n \"supported.\\\") lin = torch.unsqueeze(x @ self.w, 1) + self.b z = x +\",\n \"2, 1 / 2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0])\",\n \"H, D, inverse = True, tail_bound = self.B) log_det += ld return x,\",\n \"x[:,self.dim // 2:] t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = t1_transformed +\",\n \"- 1](x[:, :i]) mu, alpha = out[:, 0], out[:, 1] x[:, i] =\",\n \"= 1), torch.softmax(H, dim = 1) W, H = 2 * self.B *\",\n \"class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim,\",\n \"2) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in\",\n \"def backward(self, z): x = torch.zeros_like(z) log_det = torch.zeros(z.shape[0]) z = z.flip(dims=(1,)) for\",\n \"= self.layers[i - 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim =\",\n \"z[:, i], ld = unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det\",\n \"* self.B * W, 2 * self.B * H D = F.softplus(D) x[:,\",\n \"reset_parameters(dim): init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x):\",\n \"_ = sp.linalg.qr(np.random.randn(dim, dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype\",\n \"= torch.softmax(W, dim = 1), torch.softmax(H, dim = 1) W, H = 2\",\n \"D = torch.split(out, self.K, dim = 2) W, H = torch.softmax(W, dim =\",\n \"ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim\",\n \"Simple fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network\",\n \"init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for\",\n \"- mu) / torch.exp(alpha) log_det -= alpha return z.flip(dims=(1,)), log_det def backward(self, z):\",\n \"math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x,\",\n \"2, hidden_dim) self.s2 = base_network(dim // 2, dim // 2, hidden_dim) def forward(self,\",\n \"upper, ld = unconstrained_RQS( upper, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld,\",\n \"z): raise NotImplementedError(\\\"Planar flow has no algebraic inverse.\\\") class Radial(nn.Module): \\\"\\\"\\\" Radial flow.\",\n \"= torch.float), diagonal = 1)) self.W_inv = None def forward(self, x): L =\",\n \"unconstrained_RQS( x[:, i], W, H, D, inverse=False, tail_bound=self.B) log_det += ld return z,\",\n \"D, inverse=True, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) out = self.f1(lower).reshape(-1, self.dim\",\n \"dim // 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper =\",\n \"torch.sum(s2_transformed, dim=1) return z, log_det def backward(self, z): lower, upper = z[:,:self.dim //\",\n \"self.W_inv = None def forward(self, x): L = torch.tril(self.L, diagonal = -1) +\",\n \"t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper - t1_transformed) * torch.exp(-s1_transformed)\",\n \"= 2 * self.B * W, 2 * self.B * H D =\",\n \"base_network = FCNN): super().__init__() self.dim = dim self.K = K self.B = B\",\n \"flow. [Dinh et. al. 2017] \\\"\\\"\\\" def __init__(self, dim, hidden_dim = 8, base_network=FCNN):\",\n \"= B self.f1 = base_network(dim // 2, (3 * K - 1) *\",\n \"* h) + \\\\ torch.log(1 + beta * h - \\\\ beta *\",\n \"in range(1, dim): self.layers += [base_network(i, 2, hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5),\",\n \"self.u - 1 u = self.u + scal * self.w / torch.norm(self.w) else:\",\n \"torch.log(1 + torch.exp(self.beta)) z = x + beta * h * (x -\",\n \"return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et\",\n \"2, hidden_dim) self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 =\",\n \"diagonal = 1) z = x @ self.P @ L @ (U +\",\n \"numpy as np import scipy as sp import scipy.linalg import torch import torch.nn\",\n \"= 1) W, H = 2 * self.B * W, 2 * self.B\",\n \"Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim W, _ =\",\n \"__init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network =\",\n \"z = f(x) = = x + β h(α, r)(z − z0) [Rezende\",\n \"u = self.u + scal * self.w / torch.norm(self.w) else: raise NotImplementedError(\\\"Non-linearity is\",\n \"u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u)) -\",\n \"* r / (torch.exp(self.log_alpha) + r) ** 2) return z, log_det class FCNN(nn.Module):\",\n \"out[:, 0], out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha)\",\n \"// 2, hidden_dim) def forward(self, x): log_det = torch.zeros(x.shape[0]) lower, upper = x[:,\",\n \"- t2_transformed) * torch.exp(-s2_transformed) t1_transformed = self.t1(lower) s1_transformed = self.s1(lower) upper = (upper\",\n \"torch.float)) def forward(self, x): z = x * torch.exp(self.log_sigma) + self.mu log_det =\",\n \"F.leaky_relu): u = self.u elif self.h == torch.tanh: scal = torch.log(1+torch.exp(self.w @ self.u))\",\n \"class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline flow, coupling layer. [Durkan et al. 2019] \\\"\\\"\\\"\",\n \"lower, ld = unconstrained_RQS( lower, W, H, D, inverse=False, tail_bound=self.B) log_det += torch.sum(ld,\",\n \"Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim,\",\n \"= self.B) log_det += ld return x, log_det class NSF_CL(nn.Module): \\\"\\\"\\\" Neural spline\",\n \"self.K, dim = 1) W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim\",\n \"tail_bound=self.B) log_det += ld return z, log_det def backward(self, z): x = torch.zeros_like(z)\",\n \"H, D = torch.split(init_param, self.K, dim = 1) else: out = self.layers[i -\",\n \"x[:, i] = mu + torch.exp(alpha) * z[:, i] log_det += alpha return\",\n \"dim)) P, L, U = sp.linalg.lu(W) self.P = torch.tensor(P, dtype = torch.float) self.L\",\n \"layer. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K = 5, B\",\n \"nonlinearity self.w = nn.Parameter(torch.Tensor(dim)) self.u = nn.Parameter(torch.Tensor(dim)) self.b = nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self,\",\n \"+ \\\\ (x < 0).type(torch.FloatTensor) * torch.exp(x) } class Planar(nn.Module): \\\"\\\"\\\" Planar flow.\",\n \"FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def __init__(self, in_dim, out_dim, hidden_dim):\",\n \"__init__(self, in_dim, out_dim, hidden_dim): super().__init__() self.network = nn.Sequential( nn.Linear(in_dim, hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, hidden_dim),\",\n \"0], out[:, 1] z[:, i] = (x[:, i] - mu) / torch.exp(alpha) log_det\",\n \"mu + torch.exp(alpha) * z[:, i] log_det += alpha return x, log_det class\",\n \"1x1 convolution. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim =\",\n \"Neural spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def __init__(self, dim, K\",\n \"x @ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S))) return\",\n \"= x @ self.P @ L @ (U + torch.diag(self.S)) log_det = torch.sum(torch.log(torch.abs(self.S)))\",\n \"class Radial(nn.Module): \\\"\\\"\\\" Radial flow. z = f(x) = = x + β\",\n \"torch.sum(ld, dim = 1) out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K\",\n \"+ r) ** 2) return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected\",\n \"class NSF_AR(nn.Module): \\\"\\\"\\\" Neural spline flow, auto-regressive. [Durkan et al. 2019] \\\"\\\"\\\" def\",\n \"x = (z - self.mu) / torch.exp(self.log_sigma) log_det = -torch.sum(self.log_sigma) return x, log_det\",\n \"lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:] out =\",\n \"t1_transformed + upper * torch.exp(s1_transformed) t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower =\",\n \"__init__(self, dim): super().__init__() self.dim = dim self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float)) self.log_sigma\",\n \"torch.softmax(H, dim = 1) W, H = 2 * self.B * W, 2\",\n \"import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F from\",\n \"= -torch.sum(self.log_sigma) return x, log_det class OneByOneConv(nn.Module): \\\"\\\"\\\" Invertible 1x1 convolution. [Kingma and\",\n \"else: out = self.layers[i - 1](x[:, :i]) mu, alpha = out[:, 0], out[:,\",\n \"dim): self.layers += [base_network(i, 3 * K - 1, hidden_dim)] self.reset_parameters() def reset_parameters(self):\",\n \"z[:,self.dim // 2:] t2_transformed = self.t2(upper) s2_transformed = self.s2(upper) lower = (lower -\",\n \"hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim self.K = K\",\n \"z = x @ self.P @ L @ (U + torch.diag(self.S)) log_det =\",\n \"1) W = self.P @ L @ (U + torch.diag(self.S)) self.W_inv = torch.inverse(W)\",\n \"B = 3, hidden_dim = 8, base_network = FCNN): super().__init__() self.dim = dim\",\n \"= self.initial_param[0], self.initial_param[1] else: out = self.layers[i - 1](x[:, :i]) mu, alpha =\",\n \"\\\"\\\"\\\" ActNorm layer. [Kingma and Dhariwal, 2018.] \\\"\\\"\\\" def __init__(self, dim): super().__init__() self.dim\",\n \"hidden_dim), nn.Tanh(), nn.Linear(hidden_dim, out_dim), ) def forward(self, x): return self.network(x) class RealNVP(nn.Module): \\\"\\\"\\\"\",\n \"__init__(self, dim, hidden_dim = 8, base_network=FCNN): super().__init__() self.dim = dim self.t1 = base_network(dim\",\n \"dim = 1), torch.softmax(H, dim = 1) W, H = 2 * self.B\",\n \":i]) mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i]\",\n \"= base_network(dim // 2, (3 * K - 1) * dim // 2,\",\n \"hidden_dim)] self.reset_parameters() def reset_parameters(self): init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5)) def forward(self, x): z = torch.zeros_like(x)\",\n \"- 1](x[:, :i]) W, H, D = torch.split(out, self.K, dim = 1) W,\",\n \"+ beta * h * (x - self.x0) log_det = (n - 1)\",\n \"self.K = K self.B = B self.f1 = base_network(dim // 2, (3 *\",\n \"1 - torch.pow(torch.tanh(x), 2), F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x\",\n \"- 1) W, H, D = torch.split(out, self.K, dim = 2) W, H\",\n \"+ phi @ u) + 1e-4) return z, log_det def backward(self, z): raise\",\n \"x): z = torch.zeros_like(x) log_det = torch.zeros(z.shape[0]) for i in range(self.dim): if i\",\n \"return z, log_det class FCNN(nn.Module): \\\"\\\"\\\" Simple fully connected neural network. \\\"\\\"\\\" def\",\n \"z[:, :self.dim // 2], z[:, self.dim // 2:] out = self.f2(upper).reshape(-1, self.dim //\",\n \"(x > 0).type(torch.FloatTensor) + \\\\ (x < 0).type(torch.FloatTensor) * -0.01, F.elu: lambda x:\",\n \"= torch.zeros(z.shape[0]) lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:]\",\n \"math.sqrt(1/dim)) init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\" Given x,\",\n \"mu, alpha = out[:, 0], out[:, 1] z[:, i] = (x[:, i] -\",\n \"nn.Parameter(torch.Tensor(1)) self.reset_parameters(dim) def reset_parameters(self, dim): init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim),\",\n \"= torch.triu(self.U, diagonal = 1) z = x @ self.P @ L @\",\n \"log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed, dim=1) return z, log_det def backward(self,\",\n \"0).type(torch.FloatTensor) * -0.01, F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \\\\ (x <\",\n \"inverse=False, tail_bound=self.B) log_det += torch.sum(ld, dim = 1) return torch.cat([lower, upper], dim =\",\n \"dim=1) log_det = torch.sum(-s1_transformed, dim=1) + \\\\ torch.sum(-s2_transformed, dim=1) return x, log_det class\",\n \"self.s1 = base_network(dim // 2, dim // 2, hidden_dim) self.t2 = base_network(dim //\",\n \"init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim)) init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim)) def forward(self, x): \\\"\\\"\\\"\",\n \"torch.exp(s2_transformed) z = torch.cat([lower, upper], dim=1) log_det = torch.sum(s1_transformed, dim=1) + \\\\ torch.sum(s2_transformed,\"\n]","isConversational":false}}},{"rowIdx":2590,"cells":{"ngram":{"kind":"list like","value":["s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op == \"unary","# note: operands are pushed onto the stack in reverse order op2 =","return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return float(op)","else 1 if a > epsilon else 0, } exprStack = [] def","Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but","')' | '(' expr ')' factor :: atom [ expop factor ]* term","(2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term =","ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN","to # permit persons to whom the Software is furnished to do so,","^ factor ]...\" instead of \"atom [ ^ atom ]...\", we get right-to-left","} fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\":","]...\", we get right-to-left # exponents, instead of left-to-right that is, 2^3^2 =","# a copy of this software and associated documentation files (the # \"Software\"),","Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums)))","to # the following conditions: # # The above copyright notice and this","WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.","(the # \"Software\"), to deal in the Software without restriction, including # without","stats['min'] elif op == \"max\": return stats['max'] elif op == \"std\": return stats['std']","stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return","CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE","Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...]","else: break def BNF(): \"\"\" expop :: '^' multop :: '*' | '/'","\"Software\"), to deal in the Software without restriction, including # without limitation the","op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max',","{ \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn","the stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)])","of this software and associated documentation files (the # \"Software\"), to deal in","atom [ expop factor ]* term :: factor [ multop factor ]* expr","nums) + # Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) #","multop = mult | div expop = Literal(\"^\") expr = Forward() expr_list =","args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t:","import math import operator # map operator symbols to corresponding arithmetic operations epsilon","term ]* \"\"\" # use CaselessKeyword for e and pi, to avoid accidentally","def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args","+ (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf =","order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha():","term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop +","term :: factor [ multop factor ]* expr :: term [ addop term","def BNF(): \"\"\" expop :: '^' multop :: '*' | '/' addop ::","left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<=","the Software, and to # permit persons to whom the Software is furnished","\"max\": return stats['max'] elif op == \"std\": return stats['std'] elif op in fn:","# Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free of","conditions: # # The above copyright notice and this permission notice shall be","Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) # or use provided","expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action","\"E\": return math.e # 2.718281828 elif op == \"mean\": return stats['mean'] elif op","else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max', 'mean',","stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op","furnished to do so, subject to # the following conditions: # # The","Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\"E\") pi =","t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus, minus,","or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS","OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE","whom the Software is furnished to do so, subject to # the following","elif op in fn: # note: args are pushed onto the stack in","NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE","# without limitation the rights to use, copy, modify, merge, publish, # distribute,","term [ addop term ]* \"\"\" # use CaselessKeyword for e and pi,","atom ]...\", we get right-to-left # exponents, instead of left-to-right that is, 2^3^2","expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op,","any person obtaining # a copy of this software and associated documentation files","IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN","Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to str: #","factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf","args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise","KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF","DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward,","op in \"+-*/^\": # note: operands are pushed onto the stack in reverse","OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS","+ (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op,","Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free of charge,","this permission notice shall be # included in all copies or substantial portions","# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, #","\"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a:","t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi","for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" %","Permission is hereby granted, free of charge, to any person obtaining # a","module pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is hereby","stats): \"\"\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\"\"\" _","{ \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda","<<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...]","\"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop :: '^' multop ::","and/or sell copies of the Software, and to # permit persons to whom","of args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda","add parse action that replaces the function identifier with a (name, number of","rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies","lambda a: -1 if a < -epsilon else 1 if a > epsilon","Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator symbols","BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN","limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or","stats ('min', 'max', 'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val","plus, minus, mult, div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop","without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense,","+ factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return","the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF","for t in toks: if t == \"-\": exprStack.append(\"unary -\") else: break def",") ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom [ ^ factor ]...\" instead","Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the function identifier","operations epsilon = 1e-12 opn = { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul,","start with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only","| ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining","== \"max\": return stats['max'] elif op == \"std\": return stats['std'] elif op in","modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and","a < -epsilon else 1 if a > epsilon else 0, } exprStack","operator # map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn","lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call","(c) 2003-2019 <NAME> # # Permission is hereby granted, free of charge, to","PI | E | real | fn '(' expr ')' | '(' expr","stats) if op in \"+-*/^\": # note: operands are pushed onto the stack","]...\" instead of \"atom [ ^ atom ]...\", we get right-to-left # exponents,","lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom =","replaces the function identifier with a (name, number of args) tuple fn_call =","IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY","pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList,","= Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that","avoid accidentally matching # functions that start with 'e' or 'pi' (such as","functions that start with 'e' or 'pi' (such as 'exp'); Keyword # and","onto the stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s,","real | fn '(' expr ')' | '(' expr ')' factor :: atom","# \"Software\"), to deal in the Software without restriction, including # without limitation","# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE","CaselessKeyword only match whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber","push_unary_minus(toks): for t in toks: if t == \"-\": exprStack.append(\"unary -\") else: break","expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom [ ^","= Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus, minus, mult, div =","EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY,","\"_$\") plus, minus, mult, div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\")","defining exponentiation as \"atom [ ^ factor ]...\" instead of \"atom [ ^","stats['std'] elif op in fn: # note: args are pushed onto the stack","THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #","in the Software without restriction, including # without limitation the rights to use,","math.e # 2.718281828 elif op == \"mean\": return stats['mean'] elif op == \"min\":","len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi | e","- Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = (","Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator","\"atom [ ^ atom ]...\", we get right-to-left # exponents, instead of left-to-right","+ # Optional(e + Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but convert","( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import","\"+-*/^\": # note: operands are pushed onto the stack in reverse order op2","SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR","# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT","op, num_args = op if op == \"unary -\": return -evaluate_stack(s, stats) if","operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn = { \"+\":","this software and associated documentation files (the # \"Software\"), to deal in the","WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED","with a (name, number of args) tuple fn_call = (ident + lpar -","fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by","op == \"max\": return stats['max'] elif op == \"std\": return stats['std'] elif op","== \"unary -\": return -evaluate_stack(s, stats) if op in \"+-*/^\": # note: operands","pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted,","expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def","2.718281828 elif op == \"mean\": return stats['mean'] elif op == \"min\": return stats['min']","pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums)))","PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING","of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY","IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT","CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH","\"unary -\": return -evaluate_stack(s, stats) if op in \"+-*/^\": # note: operands are","instead of \"atom [ ^ atom ]...\", we get right-to-left # exponents, instead","PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS","map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn = {","# exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor","of the Software, and to # permit persons to whom the Software is","PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT","THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR","(expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term","stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return","to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident =","in all copies or substantial portions of the Software. # # THE SOFTWARE","OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION","SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing","a > epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def","fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0),","# map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn =","-\": return -evaluate_stack(s, stats) if op in \"+-*/^\": # note: operands are pushed","return stats['max'] elif op == \"std\": return stats['std'] elif op in fn: #","\"^\": operator.pow, } fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\":","OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import","+ rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] +","op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return float(op) def eval_fx(fx, stats):","| Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as","+ (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<=","stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \"PI\": return","# 2.718281828 elif op == \"mean\": return stats['mean'] elif op == \"min\": return","shall be # included in all copies or substantial portions of the Software.","term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(),","person obtaining # a copy of this software and associated documentation files (the","'max', 'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:],","LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM,","# from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword,","note: operands are pushed onto the stack in reverse order op2 = evaluate_stack(s,","are pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for","= ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\")","+ lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom","TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE #","symbols to corresponding arithmetic operations epsilon = 1e-12 opn = { \"+\": operator.add,","elif op == \"std\": return stats['std'] elif op in fn: # note: args","and to # permit persons to whom the Software is furnished to do","# by defining exponentiation as \"atom [ ^ factor ]...\" instead of \"atom","'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole words e","stats) return opn[op](op1, op2) elif op == \"PI\": return math.pi # 3.1415926535 elif","+ \"_$\") plus, minus, mult, div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress,","in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args)","(fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr","is furnished to do so, subject to # the following conditions: # #","to corresponding arithmetic operations epsilon = 1e-12 opn = { \"+\": operator.add, \"-\":","the following conditions: # # The above copyright notice and this permission notice","[ multop factor ]* expr :: term [ addop term ]* \"\"\" #","+ Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to str:","that replaces the function identifier with a (name, number of args) tuple fn_call","== \"E\": return math.e # 2.718281828 elif op == \"mean\": return stats['mean'] elif","opn[op](op1, op2) elif op == \"PI\": return math.pi # 3.1415926535 elif op ==","if a > epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0])","'(' expr ')' | '(' expr ')' factor :: atom [ expop factor",").setParseAction(push_unary_minus) # by defining exponentiation as \"atom [ ^ factor ]...\" instead of","= map(Suppress, \"()\") addop = plus | minus multop = mult | div","factor [ multop factor ]* expr :: term [ addop term ]* \"\"\"","the stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats)","raise Exception(\"invalid identifier '%s'\" % op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given","op == \"PI\": return math.pi # 3.1415926535 elif op == \"E\": return math.e","math.pi # 3.1415926535 elif op == \"E\": return math.e # 2.718281828 elif op","provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0]))","parse action that replaces the function identifier with a (name, number of args)","Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus, minus, mult, div = map(Literal,","range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return","Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math","alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map","\"atom [ ^ factor ]...\" instead of \"atom [ ^ atom ]...\", we","notice shall be # included in all copies or substantial portions of the","number of args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction(","IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR","a (name, number of args) tuple fn_call = (ident + lpar - Group(expr_list)","factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop","operator.truediv, \"^\": operator.pow, } fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan,",":: '*' | '/' addop :: '+' | '-' integer :: ['+' |","pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar)","permission notice shall be # included in all copies or substantial portions of","1 if a > epsilon else 0, } exprStack = [] def push_first(toks):","(addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args","= [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t","op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op if","and this permission notice shall be # included in all copies or substantial","copy of this software and associated documentation files (the # \"Software\"), to deal","factor ]* expr :: term [ addop term ]* \"\"\" # use CaselessKeyword",":: atom [ expop factor ]* term :: factor [ multop factor ]*","# Optional(e + Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but convert back","if isinstance(op, tuple): op, num_args = op if op == \"unary -\": return","pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for _","# module pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is","hereby granted, free of charge, to any person obtaining # a copy of","<<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s,","# distribute, sublicense, and/or sell copies of the Software, and to # permit","div expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse","the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell","elif op == \"max\": return stats['max'] elif op == \"std\": return stats['std'] elif","return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats) return val","= { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\":","identifier with a (name, number of args) tuple fn_call = (ident + lpar","use CaselessKeyword for e and pi, to avoid accidentally matching # functions that","exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop :: '^' multop :: '*'","convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\")","with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match","to avoid accidentally matching # functions that start with 'e' or 'pi' (such","granted, free of charge, to any person obtaining # a copy of this","'0'..'9'+ atom :: PI | E | real | fn '(' expr ')'",":: ['+' | '-'] '0'..'9'+ atom :: PI | E | real |","\"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT","ident = Word(alphas, alphanums + \"_$\") plus, minus, mult, div = map(Literal, \"+-*/\")","COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER","| '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E","num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op","CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator symbols to","'-'] '0'..'9'+ atom :: PI | E | real | fn '(' expr","from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress,","map(Suppress, \"()\") addop = plus | minus multop = mult | div expop","Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY","as 'exp'); Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\"E\")","== \"min\": return stats['min'] elif op == \"max\": return stats['max'] elif op ==","+ factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term +","obtaining # a copy of this software and associated documentation files (the #","to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of","')' factor :: atom [ expop factor ]* term :: factor [ multop","+ Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number,","ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation","if t == \"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop ::","= factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...]","return -evaluate_stack(s, stats) if op in \"+-*/^\": # note: operands are pushed onto","e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + #","associated documentation files (the # \"Software\"), to deal in the Software without restriction,","bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op,","fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus, minus, mult, div","op == \"unary -\": return -evaluate_stack(s, stats) if op in \"+-*/^\": # note:","( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar +","epsilon = 1e-12 opn = { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\":","'/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom","LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND","NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE","def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \"-\":","= Word(alphas, alphanums + \"_$\") plus, minus, mult, div = map(Literal, \"+-*/\") lpar,",") atom = ( addop[...] + ( (fn_call | pi | e |","# fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums","exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \"-\": exprStack.append(\"unary -\")","(t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi |","stats['mean'] elif op == \"min\": return stats['min'] elif op == \"max\": return stats['max']","Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import","+ # Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) # or","Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop","| minus multop = mult | div expop = Literal(\"^\") expr = Forward()","= s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op ==","publish, # distribute, sublicense, and/or sell copies of the Software, and to #","> epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks):","push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \"-\": exprStack.append(\"unary","arithmetic operations epsilon = 1e-12 opn = { \"+\": operator.add, \"-\": operator.sub, \"*\":","return math.pi # 3.1415926535 elif op == \"E\": return math.e # 2.718281828 elif","is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom +","OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING","integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E | real","expop factor ]* term :: factor [ multop factor ]* expr :: term","# or use provided pyparsing_common.number, but convert back to str: # fnumber =","reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier","as \"atom [ ^ factor ]...\" instead of \"atom [ ^ atom ]...\",","factor ]...\" instead of \"atom [ ^ atom ]...\", we get right-to-left #","INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A","WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN","minus multop = mult | div expop = Literal(\"^\") expr = Forward() expr_list","and stats ('min', 'max', 'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True)","distribute, sublicense, and/or sell copies of the Software, and to # permit persons","AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE",":: '^' multop :: '*' | '/' addop :: '+' | '-' integer","= 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop +","# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from","# and CaselessKeyword only match whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\")","Optional(e + Word(\"+-\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to","['+' | '-'] '0'..'9'+ atom :: PI | E | real | fn","in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else:","FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS","OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE","of \"atom [ ^ atom ]...\", we get right-to-left # exponents, instead of","| real | fn '(' expr ')' | '(' expr ')' factor ::","including # without limitation the rights to use, copy, modify, merge, publish, #","we get right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2),","EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM,","+ term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args =","= Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums,","num_args = op if op == \"unary -\": return -evaluate_stack(s, stats) if op","\"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda a: -1 if","expr ')' factor :: atom [ expop factor ]* term :: factor [","\"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn = { \"sin\": math.sin, \"cos\":","= expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if","fx and stats ('min', 'max', 'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx,","\"\"\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\"\"\" _ =","NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY #","'exp'); Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\"E\") pi","and pi, to avoid accidentally matching # functions that start with 'e' or","return stats['min'] elif op == \"max\": return stats['max'] elif op == \"std\": return","opn = { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow,","sublicense, and/or sell copies of the Software, and to # permit persons to","float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max', 'mean', 'std') return","MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL","by defining exponentiation as \"atom [ ^ factor ]...\" instead of \"atom [","THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import (","lambda a: int(a), \"round\": round, \"sgn\": lambda a: -1 if a < -epsilon","| '(' expr ')' factor :: atom [ expop factor ]* term ::","Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom","fn '(' expr ')' | '(' expr ')' factor :: atom [ expop","= reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid","= op if op == \"unary -\": return -evaluate_stack(s, stats) if op in","= plus | minus multop = mult | div expop = Literal(\"^\") expr","op == \"mean\": return stats['mean'] elif op == \"min\": return stats['min'] elif op","and associated documentation files (the # \"Software\"), to deal in the Software without","= CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\"","ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES","corresponding arithmetic operations epsilon = 1e-12 opn = { \"+\": operator.add, \"-\": operator.sub,","so, subject to # the following conditions: # # The above copyright notice","tuple): op, num_args = op if op == \"unary -\": return -evaluate_stack(s, stats)","instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward()","# functions that start with 'e' or 'pi' (such as 'exp'); Keyword #","addop = plus | minus multop = mult | div expop = Literal(\"^\")","e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus)","op in fn: # note: args are pushed onto the stack in reverse","in fn: # note: args are pushed onto the stack in reverse order","persons to whom the Software is furnished to do so, subject to #","do so, subject to # the following conditions: # # The above copyright","return opn[op](op1, op2) elif op == \"PI\": return math.pi # 3.1415926535 elif op","2003-2019 <NAME> # # Permission is hereby granted, free of charge, to any","t == \"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop :: '^'","OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word,","= (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0])))","function identifier with a (name, number of args) tuple fn_call = (ident +","reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif","('min', 'max', 'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val =","(multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr","return math.e # 2.718281828 elif op == \"mean\": return stats['mean'] elif op ==","pi, to avoid accidentally matching # functions that start with 'e' or 'pi'","< -epsilon else 1 if a > epsilon else 0, } exprStack =","the function identifier with a (name, number of args) tuple fn_call = (ident","permit persons to whom the Software is furnished to do so, subject to","CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT","return stats['mean'] elif op == \"min\": return stats['min'] elif op == \"max\": return","factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf","elif op == \"min\": return stats['min'] elif op == \"max\": return stats['max'] elif","use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the","the Software without restriction, including # without limitation the rights to use, copy,","# Optional(\".\" + Optional(Word(nums))) + # Optional(e + Word(\"+-\"+nums, nums))) # or use","BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR","term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats):","OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group,","= evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op ==","\"round\": round, \"sgn\": lambda a: -1 if a < -epsilon else 1 if","copies of the Software, and to # permit persons to whom the Software","for e and pi, to avoid accidentally matching # functions that start with","-epsilon else 1 if a > epsilon else 0, } exprStack = []","# use CaselessKeyword for e and pi, to avoid accidentally matching # functions","if a < -epsilon else 1 if a > epsilon else 0, }","AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER","<NAME> # # Permission is hereby granted, free of charge, to any person","# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF #",") import math import operator # map operator symbols to corresponding arithmetic operations","| pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr +","Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator #","CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" +","# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, #","ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,","= delimitedList(Group(expr)) # add parse action that replaces the function identifier with a","WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO","= { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, }","'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats) return","elif op == \"mean\": return stats['mean'] elif op == \"min\": return stats['min'] elif","or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole words","files (the # \"Software\"), to deal in the Software without restriction, including #","round, \"sgn\": lambda a: -1 if a < -epsilon else 1 if a","\"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\":","Software, and to # permit persons to whom the Software is furnished to","t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call |","IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF","TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.","evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \"PI\":","order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif","operands are pushed onto the stack in reverse order op2 = evaluate_stack(s, stats)","'+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI |","identifier '%s'\" % op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx and","charge, to any person obtaining # a copy of this software and associated","| E | real | fn '(' expr ')' | '(' expr ')'","[ addop term ]* \"\"\" # use CaselessKeyword for e and pi, to","int(a), \"round\": round, \"sgn\": lambda a: -1 if a < -epsilon else 1","\"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn = { \"sin\":","to deal in the Software without restriction, including # without limitation the rights","\"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda a: -1 if a <","math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda a: -1","(name, number of args) tuple fn_call = (ident + lpar - Group(expr_list) +","onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in","IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas,","that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom","= map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop = plus | minus","# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR","( addop[...] + ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first)","eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\"\"\"","+ ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar","E | real | fn '(' expr ')' | '(' expr ')' factor","atom :: PI | E | real | fn '(' expr ')' |","[ expop factor ]* term :: factor [ multop factor ]* expr ::","args are pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats)","addop term ]* \"\"\" # use CaselessKeyword for e and pi, to avoid","OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER","documentation files (the # \"Software\"), to deal in the Software without restriction, including","str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus, minus, mult,","ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums + \"_$\") plus,","]* expr :: term [ addop term ]* \"\"\" # use CaselessKeyword for","= CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) +","fn: # note: args are pushed onto the stack in reverse order args","fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs,","addop[...] + ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) |","\"sgn\": lambda a: -1 if a < -epsilon else 1 if a >","\"\"\" expop :: '^' multop :: '*' | '/' addop :: '+' |","+ rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom [ ^ factor","\"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn =","| fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) #","op == \"std\": return stats['std'] elif op in fn: # note: args are","import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, )","elif op == \"E\": return math.e # 2.718281828 elif op == \"mean\": return","of charge, to any person obtaining # a copy of this software and","== \"mean\": return stats['mean'] elif op == \"min\": return stats['min'] elif op ==","notice and this permission notice shall be # included in all copies or","ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE","return float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max', 'mean', 'std')","# # The above copyright notice and this permission notice shall be #","merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to","\"()\") addop = plus | minus multop = mult | div expop =","math import operator # map operator symbols to corresponding arithmetic operations epsilon =","minus, mult, div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop =","OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR","Software is furnished to do so, subject to # the following conditions: #","mult, div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop = plus","the Software is furnished to do so, subject to # the following conditions:","elif op == \"PI\": return math.pi # 3.1415926535 elif op == \"E\": return","mult | div expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) #","t in toks: if t == \"-\": exprStack.append(\"unary -\") else: break def BNF():","= 1e-12 opn = { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv,","are pushed onto the stack in reverse order op2 = evaluate_stack(s, stats) op1","1e-12 opn = { \"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\":",":: PI | E | real | fn '(' expr ')' | '('","== \"std\": return stats['std'] elif op in fn: # note: args are pushed","+ expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom [","-evaluate_stack(s, stats) if op in \"+-*/^\": # note: operands are pushed onto the","'^' multop :: '*' | '/' addop :: '+' | '-' integer ::","# 3.1415926535 elif op == \"E\": return math.e # 2.718281828 elif op ==","break def BNF(): \"\"\" expop :: '^' multop :: '*' | '/' addop","= ( addop[...] + ( (fn_call | pi | e | fnumber |","2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...]","# Permission is hereby granted, free of charge, to any person obtaining #","# included in all copies or substantial portions of the Software. # #","operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn = { \"sin\": math.sin, \"cos\": math.cos,","OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE,","def push_unary_minus(toks): for t in toks: if t == \"-\": exprStack.append(\"unary -\") else:","toks: if t == \"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop","exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor =","fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) + # Optional(e +","copyright notice and this permission notice shall be # included in all copies","a copy of this software and associated documentation files (the # \"Software\"), to","== \"PI\": return math.pi # 3.1415926535 elif op == \"E\": return math.e #","match whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums,","op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op","factor :: atom [ expop factor ]* term :: factor [ multop factor","USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal,","not (2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term","words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) +","factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop +","use provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t:","\"\"\" # use CaselessKeyword for e and pi, to avoid accidentally matching #","expr_list = delimitedList(Group(expr)) # add parse action that replaces the function identifier with","\"std\": return stats['std'] elif op in fn: # note: args are pushed onto","copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software,","factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor","HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN","WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT","but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber =","a: -1 if a < -epsilon else 1 if a > epsilon else","back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident","copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED","OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY,","delimitedList, ) import math import operator # map operator symbols to corresponding arithmetic","stats['max'] elif op == \"std\": return stats['std'] elif op in fn: # note:","else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t","SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES","exponentiation as \"atom [ ^ factor ]...\" instead of \"atom [ ^ atom","portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT","lpar, rpar = map(Suppress, \"()\") addop = plus | minus multop = mult","# The above copyright notice and this permission notice shall be # included","op if op == \"unary -\": return -evaluate_stack(s, stats) if op in \"+-*/^\":","fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas, alphanums +","included in all copies or substantial portions of the Software. # # THE","# # Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free","^ atom ]...\", we get right-to-left # exponents, instead of left-to-right that is,","def eval_fx(fx, stats): \"\"\"Given fx and stats ('min', 'max', 'mean', 'std') return the","isinstance(op, tuple): op, num_args = op if op == \"unary -\": return -evaluate_stack(s,","\"PI\": return math.pi # 3.1415926535 elif op == \"E\": return math.e # 2.718281828","# fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) + # Optional(e","a: int(a), \"round\": round, \"sgn\": lambda a: -1 if a < -epsilon else","op == \"min\": return stats['min'] elif op == \"max\": return stats['max'] elif op","math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a),","if op == \"unary -\": return -evaluate_stack(s, stats) if op in \"+-*/^\": #","str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\"[+-]?\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?\") ident = Word(alphas,","A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR","THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS","operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn = { \"sin\": math.sin,","3.1415926535 elif op == \"E\": return math.e # 2.718281828 elif op == \"mean\":","\"mean\": return stats['mean'] elif op == \"min\": return stats['min'] elif op == \"max\":","]* term :: factor [ multop factor ]* expr :: term [ addop","be # included in all copies or substantial portions of the Software. #","CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums) + # Optional(\".\" + Optional(Word(nums))) + #","| '/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+","Word(alphas, alphanums + \"_$\") plus, minus, mult, div = map(Literal, \"+-*/\") lpar, rpar","]* \"\"\" # use CaselessKeyword for e and pi, to avoid accidentally matching","to do so, subject to # the following conditions: # # The above","operator.pow, } fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\": math.tan, \"exp\": math.exp,","plus | minus multop = mult | div expop = Literal(\"^\") expr =","math.cos, \"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round,","atom = ( addop[...] + ( (fn_call | pi | e | fnumber","accidentally matching # functions that start with 'e' or 'pi' (such as 'exp');","substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \"AS IS\",","0 if isinstance(op, tuple): op, num_args = op if op == \"unary -\":","op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \"PI\": return math.pi","# note: args are pushed onto the stack in reverse order args =","e and pi, to avoid accidentally matching # functions that start with 'e'","math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda","only match whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber =","get right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not","restriction, including # without limitation the rights to use, copy, modify, merge, publish,","Exception(\"invalid identifier '%s'\" % op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx","expr ')' | '(' expr ')' factor :: atom [ expop factor ]*","\"min\": return stats['min'] elif op == \"max\": return stats['max'] elif op == \"std\":","LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION","stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\"","without restriction, including # without limitation the rights to use, copy, modify, merge,","expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the","multop factor ]* expr :: term [ addop term ]* \"\"\" # use","# # Permission is hereby granted, free of charge, to any person obtaining","all copies or substantial portions of the Software. # # THE SOFTWARE IS",":: term [ addop term ]* \"\"\" # use CaselessKeyword for e and","deal in the Software without restriction, including # without limitation the rights to","evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \"PI\": return math.pi # 3.1415926535","note: args are pushed onto the stack in reverse order args = reversed([evaluate_stack(s,","== \"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\" expop :: '^' multop","in toks: if t == \"-\": exprStack.append(\"unary -\") else: break def BNF(): \"\"\"","(such as 'exp'); Keyword # and CaselessKeyword only match whole words e =","alphanums + \"_$\") plus, minus, mult, div = map(Literal, \"+-*/\") lpar, rpar =","OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR","matching # functions that start with 'e' or 'pi' (such as 'exp'); Keyword","and CaselessKeyword only match whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") #","SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex,","| e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) )","'mean', 'std') return the result\"\"\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats)","that start with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword","rpar = map(Suppress, \"()\") addop = plus | minus multop = mult |","delimitedList(Group(expr)) # add parse action that replaces the function identifier with a (name,","bnf = expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0","-\") else: break def BNF(): \"\"\" expop :: '^' multop :: '*' |","OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO","= Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the function","to any person obtaining # a copy of this software and associated documentation","operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow, } fn = {","% op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats ('min',","is hereby granted, free of charge, to any person obtaining # a copy","-1 if a < -epsilon else 1 if a > epsilon else 0,","| '-'] '0'..'9'+ atom :: PI | E | real | fn '('","op2) elif op == \"PI\": return math.pi # 3.1415926535 elif op == \"E\":","factor ]* term :: factor [ multop factor ]* expr :: term [","FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE","alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator","Software without restriction, including # without limitation the rights to use, copy, modify,","\"+-*/\") lpar, rpar = map(Suppress, \"()\") addop = plus | minus multop =","above copyright notice and this permission notice shall be # included in all","Suppress, delimitedList, ) import math import operator # map operator symbols to corresponding","or use provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda","[ ^ factor ]...\" instead of \"atom [ ^ atom ]...\", we get","sell copies of the Software, and to # permit persons to whom the","map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop = plus | minus multop","= mult | div expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr))","following conditions: # # The above copyright notice and this permission notice shall","[ ^ atom ]...\", we get right-to-left # exponents, instead of left-to-right that","\"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda a:","abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\": lambda a: -1 if a","in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1,","| div expop = Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) # add","free of charge, to any person obtaining # a copy of this software","The above copyright notice and this permission notice shall be # included in","subject to # the following conditions: # # The above copyright notice and","\"/\": operator.truediv, \"^\": operator.pow, } fn = { \"sin\": math.sin, \"cos\": math.cos, \"tan\":","[] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t ==","rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + (","# the following conditions: # # The above copyright notice and this permission","expr :: term [ addop term ]* \"\"\" # use CaselessKeyword for e","'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole","return stats['std'] elif op in fn: # note: args are pushed onto the","exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if","rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \"atom [ ^ factor ]...\"","reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2)","# add parse action that replaces the function identifier with a (name, number","IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED,",":: factor [ multop factor ]* expr :: term [ addop term ]*","_ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op)","THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. #","# permit persons to whom the Software is furnished to do so, subject","DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR","Literal(\"^\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces",":: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI","\"tan\": math.tan, \"exp\": math.exp, \"abs\": abs, \"trunc\": lambda a: int(a), \"round\": round, \"sgn\":","'%s'\" % op) else: return float(op) def eval_fx(fx, stats): \"\"\"Given fx and stats","pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber","tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0,","software and associated documentation files (the # \"Software\"), to deal in the Software","(ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) )","return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple):","elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return float(op) def eval_fx(fx,","= evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \"PI\": return math.pi #","right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2.","'*' | '/' addop :: '+' | '-' integer :: ['+' | '-']","epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for","FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF","to whom the Software is furnished to do so, subject to # the","evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args =","fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return float(op) def","0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in","of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor","} exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks:","# # THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,","THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums,","action that replaces the function identifier with a (name, number of args) tuple","BNF(): \"\"\" expop :: '^' multop :: '*' | '/' addop :: '+'","OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS","in \"+-*/^\": # note: operands are pushed onto the stack in reverse order","= Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor +","multop :: '*' | '/' addop :: '+' | '-' integer :: ['+'","nums))) # or use provided pyparsing_common.number, but convert back to str: # fnumber","| fn '(' expr ')' | '(' expr ')' factor :: atom [","addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom ::","2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop","import operator # map operator symbols to corresponding arithmetic operations epsilon = 1e-12","op == \"E\": return math.e # 2.718281828 elif op == \"mean\": return stats['mean']","'-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E |","whole words e = CaselessKeyword(\"E\") pi = CaselessKeyword(\"PI\") # fnumber = Combine(Word(\"+-\"+nums, nums)","div = map(Literal, \"+-*/\") lpar, rpar = map(Suppress, \"()\") addop = plus |","pushed onto the stack in reverse order op2 = evaluate_stack(s, stats) op1 =","'(' expr ')' factor :: atom [ expop factor ]* term :: factor","CaselessKeyword for e and pi, to avoid accidentally matching # functions that start","FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE","AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR","expop :: '^' multop :: '*' | '/' addop :: '+' | '-'","if op in \"+-*/^\": # note: operands are pushed onto the stack in","atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr"],"string":"[\n \"s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op == \\\"unary\",\n \"# note: operands are pushed onto the stack in reverse order op2 =\",\n \"return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op)\",\n \"else 1 if a > epsilon else 0, } exprStack = [] def\",\n \"Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but\",\n \"')' | '(' expr ')' factor :: atom [ expop factor ]* term\",\n \"(2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term =\",\n \"ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\",\n \"to # permit persons to whom the Software is furnished to do so,\",\n \"^ factor ]...\\\" instead of \\\"atom [ ^ atom ]...\\\", we get right-to-left\",\n \"} fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\":\",\n \"]...\\\", we get right-to-left # exponents, instead of left-to-right that is, 2^3^2 =\",\n \"# a copy of this software and associated documentation files (the # \\\"Software\\\"),\",\n \"Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums)))\",\n \"to # the following conditions: # # The above copyright notice and this\",\n \"WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\",\n \"(the # \\\"Software\\\"), to deal in the Software without restriction, including # without\",\n \"stats['min'] elif op == \\\"max\\\": return stats['max'] elif op == \\\"std\\\": return stats['std']\",\n \"stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return\",\n \"CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\",\n \"Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...]\",\n \"else: break def BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*' | '/'\",\n \"\\\"Software\\\"), to deal in the Software without restriction, including # without limitation the\",\n \"op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max',\",\n \"{ \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn\",\n \"the stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)])\",\n \"of this software and associated documentation files (the # \\\"Software\\\"), to deal in\",\n \"atom [ expop factor ]* term :: factor [ multop factor ]* expr\",\n \"nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) #\",\n \"multop = mult | div expop = Literal(\\\"^\\\") expr = Forward() expr_list =\",\n \"args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t:\",\n \"import math import operator # map operator symbols to corresponding arithmetic operations epsilon\",\n \"term ]* \\\"\\\"\\\" # use CaselessKeyword for e and pi, to avoid accidentally\",\n \"def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args\",\n \"+ (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf =\",\n \"order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha():\",\n \"term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop +\",\n \"term :: factor [ multop factor ]* expr :: term [ addop term\",\n \"def BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*' | '/' addop ::\",\n \"left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<=\",\n \"the Software, and to # permit persons to whom the Software is furnished\",\n \"\\\"max\\\": return stats['max'] elif op == \\\"std\\\": return stats['std'] elif op in fn:\",\n \"# Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free of\",\n \"conditions: # # The above copyright notice and this permission notice shall be\",\n \"Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided\",\n \"expop = Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action\",\n \"\\\"E\\\": return math.e # 2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif op\",\n \"else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean',\",\n \"stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op\",\n \"furnished to do so, subject to # the following conditions: # # The\",\n \"Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\") pi =\",\n \"t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus,\",\n \"or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS\",\n \"OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE\",\n \"whom the Software is furnished to do so, subject to # the following\",\n \"elif op in fn: # note: args are pushed onto the stack in\",\n \"NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE\",\n \"# without limitation the rights to use, copy, modify, merge, publish, # distribute,\",\n \"term [ addop term ]* \\\"\\\"\\\" # use CaselessKeyword for e and pi,\",\n \"atom ]...\\\", we get right-to-left # exponents, instead of left-to-right that is, 2^3^2\",\n \"expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op,\",\n \"any person obtaining # a copy of this software and associated documentation files\",\n \"IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN\",\n \"Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to str: #\",\n \"factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf\",\n \"args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise\",\n \"KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\",\n \"DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward,\",\n \"op in \\\"+-*/^\\\": # note: operands are pushed onto the stack in reverse\",\n \"OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS\",\n \"+ (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op,\",\n \"Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free of charge,\",\n \"this permission notice shall be # included in all copies or substantial portions\",\n \"# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, #\",\n \"\\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a:\",\n \"t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi\",\n \"for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" %\",\n \"Permission is hereby granted, free of charge, to any person obtaining # a\",\n \"module pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is hereby\",\n \"stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _\",\n \"{ \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda\",\n \"<<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...]\",\n \"\\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop :: '^' multop ::\",\n \"and/or sell copies of the Software, and to # permit persons to whom\",\n \"of args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda\",\n \"add parse action that replaces the function identifier with a (name, number of\",\n \"rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies\",\n \"lambda a: -1 if a < -epsilon else 1 if a > epsilon\",\n \"Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator symbols\",\n \"BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\",\n \"limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or\",\n \"stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val\",\n \"plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop\",\n \"without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense,\",\n \"+ factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return\",\n \"the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF\",\n \"for t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def\",\n \") ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [ ^ factor ]...\\\" instead\",\n \"Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the function identifier\",\n \"operations epsilon = 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul,\",\n \"start with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only\",\n \"| ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining\",\n \"== \\\"max\\\": return stats['max'] elif op == \\\"std\\\": return stats['std'] elif op in\",\n \"modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and\",\n \"a < -epsilon else 1 if a > epsilon else 0, } exprStack\",\n \"operator # map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn\",\n \"lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call\",\n \"(c) 2003-2019 <NAME> # # Permission is hereby granted, free of charge, to\",\n \"PI | E | real | fn '(' expr ')' | '(' expr\",\n \"stats) if op in \\\"+-*/^\\\": # note: operands are pushed onto the stack\",\n \"]...\\\" instead of \\\"atom [ ^ atom ]...\\\", we get right-to-left # exponents,\",\n \"lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom =\",\n \"replaces the function identifier with a (name, number of args) tuple fn_call =\",\n \"IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\",\n \"pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList,\",\n \"= Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that\",\n \"avoid accidentally matching # functions that start with 'e' or 'pi' (such as\",\n \"functions that start with 'e' or 'pi' (such as 'exp'); Keyword # and\",\n \"onto the stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s,\",\n \"real | fn '(' expr ')' | '(' expr ')' factor :: atom\",\n \"# \\\"Software\\\"), to deal in the Software without restriction, including # without limitation\",\n \"# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\",\n \"CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber\",\n \"push_unary_minus(toks): for t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break\",\n \"expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [ ^\",\n \"= Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div =\",\n \"EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY,\",\n \"\\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\")\",\n \"defining exponentiation as \\\"atom [ ^ factor ]...\\\" instead of \\\"atom [ ^\",\n \"stats['std'] elif op in fn: # note: args are pushed onto the stack\",\n \"THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #\",\n \"in the Software without restriction, including # without limitation the rights to use,\",\n \"math.e # 2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\":\",\n \"len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi | e\",\n \"- Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = (\",\n \"Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator\",\n \"\\\"atom [ ^ atom ]...\\\", we get right-to-left # exponents, instead of left-to-right\",\n \"+ # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but convert\",\n \"( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import\",\n \"\\\"+-*/^\\\": # note: operands are pushed onto the stack in reverse order op2\",\n \"SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR\",\n \"# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT\",\n \"op, num_args = op if op == \\\"unary -\\\": return -evaluate_stack(s, stats) if\",\n \"operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn = { \\\"+\\\":\",\n \"this software and associated documentation files (the # \\\"Software\\\"), to deal in the\",\n \"WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\",\n \"with a (name, number of args) tuple fn_call = (ident + lpar -\",\n \"fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by\",\n \"op == \\\"max\\\": return stats['max'] elif op == \\\"std\\\": return stats['std'] elif op\",\n \"== \\\"unary -\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note: operands\",\n \"pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted,\",\n \"expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def\",\n \"2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return stats['min']\",\n \"pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums)))\",\n \"PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING\",\n \"of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY\",\n \"IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT\",\n \"CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH\",\n \"\\\"unary -\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note: operands are\",\n \"instead of \\\"atom [ ^ atom ]...\\\", we get right-to-left # exponents, instead\",\n \"PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\",\n \"map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn = {\",\n \"# exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor\",\n \"of the Software, and to # permit persons to whom the Software is\",\n \"PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\",\n \"THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR\",\n \"(expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term\",\n \"stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return\",\n \"to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident =\",\n \"in all copies or substantial portions of the Software. # # THE SOFTWARE\",\n \"OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\",\n \"SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing\",\n \"a > epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def\",\n \"fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0),\",\n \"# map operator symbols to corresponding arithmetic operations epsilon = 1e-12 opn =\",\n \"-\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note: operands are pushed\",\n \"return stats['max'] elif op == \\\"std\\\": return stats['std'] elif op in fn: #\",\n \"\\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\":\",\n \"OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import\",\n \"+ rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] +\",\n \"op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op) def eval_fx(fx, stats):\",\n \"| Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as\",\n \"+ (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<=\",\n \"stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \\\"PI\\\": return\",\n \"# 2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return\",\n \"shall be # included in all copies or substantial portions of the Software.\",\n \"term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(),\",\n \"person obtaining # a copy of this software and associated documentation files (the\",\n \"'max', 'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:],\",\n \"LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM,\",\n \"# from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword,\",\n \"note: operands are pushed onto the stack in reverse order op2 = evaluate_stack(s,\",\n \"are pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for\",\n \"= ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\")\",\n \"+ lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom\",\n \"TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE #\",\n \"symbols to corresponding arithmetic operations epsilon = 1e-12 opn = { \\\"+\\\": operator.add,\",\n \"elif op == \\\"std\\\": return stats['std'] elif op in fn: # note: args\",\n \"and to # permit persons to whom the Software is furnished to do\",\n \"# by defining exponentiation as \\\"atom [ ^ factor ]...\\\" instead of \\\"atom\",\n \"'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole words e\",\n \"stats) return opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi # 3.1415926535 elif\",\n \"+ \\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress,\",\n \"in reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args)\",\n \"(fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr\",\n \"is furnished to do so, subject to # the following conditions: # #\",\n \"to corresponding arithmetic operations epsilon = 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\":\",\n \"the following conditions: # # The above copyright notice and this permission notice\",\n \"[ multop factor ]* expr :: term [ addop term ]* \\\"\\\"\\\" #\",\n \"+ Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to str:\",\n \"that replaces the function identifier with a (name, number of args) tuple fn_call\",\n \"== \\\"E\\\": return math.e # 2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif\",\n \"opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi # 3.1415926535 elif op ==\",\n \"if a > epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0])\",\n \"'(' expr ')' | '(' expr ')' factor :: atom [ expop factor\",\n \").setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [ ^ factor ]...\\\" instead of\",\n \"= map(Suppress, \\\"()\\\") addop = plus | minus multop = mult | div\",\n \"factor [ multop factor ]* expr :: term [ addop term ]* \\\"\\\"\\\"\",\n \"the stack in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats)\",\n \"raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given\",\n \"op == \\\"PI\\\": return math.pi # 3.1415926535 elif op == \\\"E\\\": return math.e\",\n \"math.pi # 3.1415926535 elif op == \\\"E\\\": return math.e # 2.718281828 elif op\",\n \"provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0]))\",\n \"parse action that replaces the function identifier with a (name, number of args)\",\n \"Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div = map(Literal,\",\n \"range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return\",\n \"Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math\",\n \"alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map\",\n \"\\\"atom [ ^ factor ]...\\\" instead of \\\"atom [ ^ atom ]...\\\", we\",\n \"notice shall be # included in all copies or substantial portions of the\",\n \"number of args) tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction(\",\n \"IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR\",\n \"a (name, number of args) tuple fn_call = (ident + lpar - Group(expr_list)\",\n \"factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop\",\n \"operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan,\",\n \":: '*' | '/' addop :: '+' | '-' integer :: ['+' |\",\n \"pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar)\",\n \"permission notice shall be # included in all copies or substantial portions of\",\n \"1 if a > epsilon else 0, } exprStack = [] def push_first(toks):\",\n \"(addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args\",\n \"= [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t\",\n \"op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op if\",\n \"and this permission notice shall be # included in all copies or substantial\",\n \"copy of this software and associated documentation files (the # \\\"Software\\\"), to deal\",\n \"factor ]* expr :: term [ addop term ]* \\\"\\\"\\\" # use CaselessKeyword\",\n \":: atom [ expop factor ]* term :: factor [ multop factor ]*\",\n \"# Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but convert back\",\n \"if isinstance(op, tuple): op, num_args = op if op == \\\"unary -\\\": return\",\n \"pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for _\",\n \"# module pyparsing.py # # Copyright (c) 2003-2019 <NAME> # # Permission is\",\n \"hereby granted, free of charge, to any person obtaining # a copy of\",\n \"<<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s,\",\n \"# distribute, sublicense, and/or sell copies of the Software, and to # permit\",\n \"div expop = Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse\",\n \"the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell\",\n \"elif op == \\\"max\\\": return stats['max'] elif op == \\\"std\\\": return stats['std'] elif\",\n \"return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats) return val\",\n \"= { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\":\",\n \"identifier with a (name, number of args) tuple fn_call = (ident + lpar\",\n \"use CaselessKeyword for e and pi, to avoid accidentally matching # functions that\",\n \"exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*'\",\n \"convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\")\",\n \"with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match\",\n \"to avoid accidentally matching # functions that start with 'e' or 'pi' (such\",\n \"granted, free of charge, to any person obtaining # a copy of this\",\n \"'0'..'9'+ atom :: PI | E | real | fn '(' expr ')'\",\n \":: ['+' | '-'] '0'..'9'+ atom :: PI | E | real |\",\n \"\\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT\",\n \"ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\")\",\n \"COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\",\n \"| '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E\",\n \"num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op\",\n \"CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator symbols to\",\n \"'-'] '0'..'9'+ atom :: PI | E | real | fn '(' expr\",\n \"from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress,\",\n \"map(Suppress, \\\"()\\\") addop = plus | minus multop = mult | div expop\",\n \"Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY\",\n \"as 'exp'); Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\")\",\n \"== \\\"min\\\": return stats['min'] elif op == \\\"max\\\": return stats['max'] elif op ==\",\n \"+ factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr <<= term +\",\n \"obtaining # a copy of this software and associated documentation files (the #\",\n \"to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of\",\n \"')' factor :: atom [ expop factor ]* term :: factor [ multop\",\n \"+ Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number,\",\n \"ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation\",\n \"if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop ::\",\n \"= factor + (multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...]\",\n \"return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note: operands are pushed onto\",\n \"e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + #\",\n \"associated documentation files (the # \\\"Software\\\"), to deal in the Software without restriction,\",\n \"bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op,\",\n \"fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div\",\n \"op == \\\"unary -\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note:\",\n \"( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar +\",\n \"epsilon = 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\":\",\n \"'/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom\",\n \"LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\",\n \"NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE\",\n \"def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \\\"-\\\":\",\n \"= Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar,\",\n \") atom = ( addop[...] + ( (fn_call | pi | e |\",\n \"# fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums\",\n \"exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\")\",\n \"(t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call | pi |\",\n \"stats['mean'] elif op == \\\"min\\\": return stats['min'] elif op == \\\"max\\\": return stats['max']\",\n \"Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import\",\n \"+ # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or\",\n \"Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop\",\n \"| minus multop = mult | div expop = Literal(\\\"^\\\") expr = Forward()\",\n \"= s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op ==\",\n \"publish, # distribute, sublicense, and/or sell copies of the Software, and to #\",\n \"> epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks):\",\n \"push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary\",\n \"arithmetic operations epsilon = 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\":\",\n \"return math.pi # 3.1415926535 elif op == \\\"E\\\": return math.e # 2.718281828 elif\",\n \"is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom +\",\n \"OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING\",\n \"integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E | real\",\n \"expop factor ]* term :: factor [ multop factor ]* expr :: term\",\n \"# or use provided pyparsing_common.number, but convert back to str: # fnumber =\",\n \"reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier\",\n \"as \\\"atom [ ^ factor ]...\\\" instead of \\\"atom [ ^ atom ]...\\\",\",\n \"factor ]...\\\" instead of \\\"atom [ ^ atom ]...\\\", we get right-to-left #\",\n \"INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A\",\n \"WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN\",\n \"minus multop = mult | div expop = Literal(\\\"^\\\") expr = Forward() expr_list\",\n \"and stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True)\",\n \"distribute, sublicense, and/or sell copies of the Software, and to # permit persons\",\n \"AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE\",\n \":: '^' multop :: '*' | '/' addop :: '+' | '-' integer\",\n \"= 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop +\",\n \"# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from\",\n \"# and CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\")\",\n \"Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use provided pyparsing_common.number, but convert back to\",\n \"['+' | '-'] '0'..'9'+ atom :: PI | E | real | fn\",\n \"in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else:\",\n \"FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS\",\n \"OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE\",\n \"of \\\"atom [ ^ atom ]...\\\", we get right-to-left # exponents, instead of\",\n \"| real | fn '(' expr ')' | '(' expr ')' factor ::\",\n \"including # without limitation the rights to use, copy, modify, merge, publish, #\",\n \"we get right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2),\",\n \"EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM,\",\n \"+ term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats): op, num_args =\",\n \"= Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums,\",\n \"num_args = op if op == \\\"unary -\\\": return -evaluate_stack(s, stats) if op\",\n \"\\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if\",\n \"expr ')' factor :: atom [ expop factor ]* term :: factor [\",\n \"\\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\":\",\n \"= expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if\",\n \"fx and stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx,\",\n \"\\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _ =\",\n \"NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY #\",\n \"'exp'); Keyword # and CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\") pi\",\n \"and pi, to avoid accidentally matching # functions that start with 'e' or\",\n \"return stats['min'] elif op == \\\"max\\\": return stats['max'] elif op == \\\"std\\\": return\",\n \"opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow,\",\n \"sublicense, and/or sell copies of the Software, and to # permit persons to\",\n \"float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std') return\",\n \"MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL\",\n \"by defining exponentiation as \\\"atom [ ^ factor ]...\\\" instead of \\\"atom [\",\n \"THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import (\",\n \"lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a < -epsilon\",\n \"| '(' expr ')' factor :: atom [ expop factor ]* term ::\",\n \"Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom\",\n \"fn '(' expr ')' | '(' expr ')' factor :: atom [ expop\",\n \"= reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid\",\n \"= op if op == \\\"unary -\\\": return -evaluate_stack(s, stats) if op in\",\n \"= plus | minus multop = mult | div expop = Literal(\\\"^\\\") expr\",\n \"op == \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return stats['min'] elif op\",\n \"and associated documentation files (the # \\\"Software\\\"), to deal in the Software without\",\n \"= CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\"\",\n \"ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\",\n \"corresponding arithmetic operations epsilon = 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub,\",\n \"so, subject to # the following conditions: # # The above copyright notice\",\n \"tuple): op, num_args = op if op == \\\"unary -\\\": return -evaluate_stack(s, stats)\",\n \"instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward()\",\n \"# functions that start with 'e' or 'pi' (such as 'exp'); Keyword #\",\n \"addop = plus | minus multop = mult | div expop = Literal(\\\"^\\\")\",\n \"e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus)\",\n \"op in fn: # note: args are pushed onto the stack in reverse\",\n \"in fn: # note: args are pushed onto the stack in reverse order\",\n \"persons to whom the Software is furnished to do so, subject to #\",\n \"do so, subject to # the following conditions: # # The above copyright\",\n \"return opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi # 3.1415926535 elif op\",\n \"2003-2019 <NAME> # # Permission is hereby granted, free of charge, to any\",\n \"t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop :: '^'\",\n \"OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word,\",\n \"= (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0])))\",\n \"function identifier with a (name, number of args) tuple fn_call = (ident +\",\n \"reverse order args = reversed([evaluate_stack(s, stats) for _ in range(num_args)]) return fn[op](*args) elif\",\n \"('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val =\",\n \"(multop + factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr\",\n \"return math.e # 2.718281828 elif op == \\\"mean\\\": return stats['mean'] elif op ==\",\n \"pi, to avoid accidentally matching # functions that start with 'e' or 'pi'\",\n \"< -epsilon else 1 if a > epsilon else 0, } exprStack =\",\n \"the function identifier with a (name, number of args) tuple fn_call = (ident\",\n \"permit persons to whom the Software is furnished to do so, subject to\",\n \"CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT\",\n \"return stats['mean'] elif op == \\\"min\\\": return stats['min'] elif op == \\\"max\\\": return\",\n \"factor).setParseAction(push_first)[...] expr <<= term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf\",\n \"elif op == \\\"min\\\": return stats['min'] elif op == \\\"max\\\": return stats['max'] elif\",\n \"use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the\",\n \"the Software without restriction, including # without limitation the rights to use, copy,\",\n \"# Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e + Word(\\\"+-\\\"+nums, nums))) # or use\",\n \"BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR\",\n \"term + (addop + term).setParseAction(push_first)[...] bnf = expr return bnf def evaluate_stack(s, stats):\",\n \"OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group,\",\n \"= evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op ==\",\n \"\\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a < -epsilon else 1 if\",\n \"copies of the Software, and to # permit persons to whom the Software\",\n \"for e and pi, to avoid accidentally matching # functions that start with\",\n \"-epsilon else 1 if a > epsilon else 0, } exprStack = []\",\n \"# use CaselessKeyword for e and pi, to avoid accidentally matching # functions\",\n \"if a < -epsilon else 1 if a > epsilon else 0, }\",\n \"AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER\",\n \"<NAME> # # Permission is hereby granted, free of charge, to any person\",\n \"# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF #\",\n \") import math import operator # map operator symbols to corresponding arithmetic operations\",\n \"| pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr +\",\n \"Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator #\",\n \"CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" +\",\n \"# THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, #\",\n \"ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\",\n \"= delimitedList(Group(expr)) # add parse action that replaces the function identifier with a\",\n \"WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\",\n \"= { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, }\",\n \"'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats) return\",\n \"elif op == \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return stats['min'] elif\",\n \"or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole words\",\n \"files (the # \\\"Software\\\"), to deal in the Software without restriction, including #\",\n \"round, \\\"sgn\\\": lambda a: -1 if a < -epsilon else 1 if a\",\n \"\\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\":\",\n \"Software, and to # permit persons to whom the Software is furnished to\",\n \"t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + ( (fn_call |\",\n \"IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF\",\n \"TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\",\n \"evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \\\"PI\\\":\",\n \"order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif\",\n \"operands are pushed onto the stack in reverse order op2 = evaluate_stack(s, stats)\",\n \"'+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI |\",\n \"identifier '%s'\\\" % op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and\",\n \"charge, to any person obtaining # a copy of this software and associated\",\n \"| E | real | fn '(' expr ')' | '(' expr ')'\",\n \"[ addop term ]* \\\"\\\"\\\" # use CaselessKeyword for e and pi, to\",\n \"int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a < -epsilon else 1\",\n \"\\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\":\",\n \"to deal in the Software without restriction, including # without limitation the rights\",\n \"\\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a <\",\n \"math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1\",\n \"(name, number of args) tuple fn_call = (ident + lpar - Group(expr_list) +\",\n \"onto the stack in reverse order args = reversed([evaluate_stack(s, stats) for _ in\",\n \"IN THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas,\",\n \"that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom\",\n \"= map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop = plus | minus\",\n \"# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR\",\n \"( addop[...] + ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first)\",\n \"eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std') return the result\\\"\\\"\\\"\",\n \"+ ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) | Group(lpar\",\n \"E | real | fn '(' expr ')' | '(' expr ')' factor\",\n \"atom :: PI | E | real | fn '(' expr ')' |\",\n \"[ expop factor ]* term :: factor [ multop factor ]* expr ::\",\n \"args are pushed onto the stack in reverse order args = reversed([evaluate_stack(s, stats)\",\n \"addop term ]* \\\"\\\"\\\" # use CaselessKeyword for e and pi, to avoid\",\n \"OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER\",\n \"documentation files (the # \\\"Software\\\"), to deal in the Software without restriction, including\",\n \"str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult,\",\n \"ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums + \\\"_$\\\") plus,\",\n \"]* expr :: term [ addop term ]* \\\"\\\"\\\" # use CaselessKeyword for\",\n \"= CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) +\",\n \"fn: # note: args are pushed onto the stack in reverse order args\",\n \"fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs,\",\n \"addop[...] + ( (fn_call | pi | e | fnumber | ident).setParseAction(push_first) |\",\n \"\\\"sgn\\\": lambda a: -1 if a < -epsilon else 1 if a >\",\n \"\\\"\\\"\\\" expop :: '^' multop :: '*' | '/' addop :: '+' |\",\n \"+ rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [ ^ factor\",\n \"\\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn =\",\n \"| fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) ) ).setParseAction(push_unary_minus) #\",\n \"op == \\\"std\\\": return stats['std'] elif op in fn: # note: args are\",\n \"import ( Literal, Word, Group, Forward, alphas, alphanums, Regex, CaselessKeyword, Suppress, delimitedList, )\",\n \"elif op == \\\"E\\\": return math.e # 2.718281828 elif op == \\\"mean\\\": return\",\n \"of charge, to any person obtaining # a copy of this software and\",\n \"== \\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return stats['min'] elif op ==\",\n \"notice and this permission notice shall be # included in all copies or\",\n \"ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE\",\n \"return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std')\",\n \"# # The above copyright notice and this permission notice shall be #\",\n \"merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to\",\n \"\\\"()\\\") addop = plus | minus multop = mult | div expop =\",\n \"math import operator # map operator symbols to corresponding arithmetic operations epsilon =\",\n \"minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop =\",\n \"OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR\",\n \"Software is furnished to do so, subject to # the following conditions: #\",\n \"mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop = plus\",\n \"the Software is furnished to do so, subject to # the following conditions:\",\n \"elif op == \\\"PI\\\": return math.pi # 3.1415926535 elif op == \\\"E\\\": return\",\n \"mult | div expop = Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) #\",\n \"t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF():\",\n \"= 1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv,\",\n \"are pushed onto the stack in reverse order op2 = evaluate_stack(s, stats) op1\",\n \"1e-12 opn = { \\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\":\",\n \":: PI | E | real | fn '(' expr ')' | '('\",\n \"== \\\"std\\\": return stats['std'] elif op in fn: # note: args are pushed\",\n \"+ expr + rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [\",\n \"-evaluate_stack(s, stats) if op in \\\"+-*/^\\\": # note: operands are pushed onto the\",\n \"'^' multop :: '*' | '/' addop :: '+' | '-' integer ::\",\n \"# 3.1415926535 elif op == \\\"E\\\": return math.e # 2.718281828 elif op ==\",\n \"break def BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*' | '/' addop\",\n \"= ( addop[...] + ( (fn_call | pi | e | fnumber |\",\n \"2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...]\",\n \"# Permission is hereby granted, free of charge, to any person obtaining #\",\n \"# included in all copies or substantial portions of the Software. # #\",\n \"operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos,\",\n \"OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE,\",\n \"def push_unary_minus(toks): for t in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else:\",\n \"toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop\",\n \"exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor =\",\n \"fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e +\",\n \"copyright notice and this permission notice shall be # included in all copies\",\n \"a copy of this software and associated documentation files (the # \\\"Software\\\"), to\",\n \"== \\\"PI\\\": return math.pi # 3.1415926535 elif op == \\\"E\\\": return math.e #\",\n \"match whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums,\",\n \"op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op\",\n \"factor :: atom [ expop factor ]* term :: factor [ multop factor\",\n \"USE OR OTHER DEALINGS IN THE SOFTWARE. # from pyparsing import ( Literal,\",\n \"not (2^3)^2. factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term\",\n \"words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) +\",\n \"factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop +\",\n \"use provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t:\",\n \"\\\"\\\"\\\" # use CaselessKeyword for e and pi, to avoid accidentally matching #\",\n \"expr_list = delimitedList(Group(expr)) # add parse action that replaces the function identifier with\",\n \"\\\"std\\\": return stats['std'] elif op in fn: # note: args are pushed onto\",\n \"copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software,\",\n \"factor = Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor\",\n \"HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN\",\n \"WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT\",\n \"but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber =\",\n \"a: -1 if a < -epsilon else 1 if a > epsilon else\",\n \"back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident\",\n \"copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED\",\n \"OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY,\",\n \"delimitedList, ) import math import operator # map operator symbols to corresponding arithmetic\",\n \"stats['max'] elif op == \\\"std\\\": return stats['std'] elif op in fn: # note:\",\n \"else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t\",\n \"SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES\",\n \"exponentiation as \\\"atom [ ^ factor ]...\\\" instead of \\\"atom [ ^ atom\",\n \"portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT\",\n \"lpar, rpar = map(Suppress, \\\"()\\\") addop = plus | minus multop = mult\",\n \"# The above copyright notice and this permission notice shall be # included\",\n \"op if op == \\\"unary -\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\":\",\n \"fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas, alphanums +\",\n \"included in all copies or substantial portions of the Software. # # THE\",\n \"# # Copyright (c) 2003-2019 <NAME> # # Permission is hereby granted, free\",\n \"^ atom ]...\\\", we get right-to-left # exponents, instead of left-to-right that is,\",\n \"def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min', 'max', 'mean', 'std') return the\",\n \"isinstance(op, tuple): op, num_args = op if op == \\\"unary -\\\": return -evaluate_stack(s,\",\n \"\\\"PI\\\": return math.pi # 3.1415926535 elif op == \\\"E\\\": return math.e # 2.718281828\",\n \"# fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + # Optional(e\",\n \"a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a < -epsilon else\",\n \"op == \\\"min\\\": return stats['min'] elif op == \\\"max\\\": return stats['max'] elif op\",\n \"math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a),\",\n \"if op == \\\"unary -\\\": return -evaluate_stack(s, stats) if op in \\\"+-*/^\\\": #\",\n \"str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber = Regex(r\\\"[+-]?\\\\d+(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?\\\") ident = Word(alphas,\",\n \"A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR\",\n \"THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS\",\n \"operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin,\",\n \"3.1415926535 elif op == \\\"E\\\": return math.e # 2.718281828 elif op == \\\"mean\\\":\",\n \"\\\"mean\\\": return stats['mean'] elif op == \\\"min\\\": return stats['min'] elif op == \\\"max\\\":\",\n \"]* term :: factor [ multop factor ]* expr :: term [ addop\",\n \"be # included in all copies or substantial portions of the Software. #\",\n \"CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums) + # Optional(\\\".\\\" + Optional(Word(nums))) + #\",\n \"| '/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+\",\n \"Word(alphas, alphanums + \\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar\",\n \"]* \\\"\\\"\\\" # use CaselessKeyword for e and pi, to avoid accidentally matching\",\n \"to do so, subject to # the following conditions: # # The above\",\n \"operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp,\",\n \"plus | minus multop = mult | div expop = Literal(\\\"^\\\") expr =\",\n \"math.cos, \\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round,\",\n \"atom = ( addop[...] + ( (fn_call | pi | e | fnumber\",\n \"accidentally matching # functions that start with 'e' or 'pi' (such as 'exp');\",\n \"substantial portions of the Software. # # THE SOFTWARE IS PROVIDED \\\"AS IS\\\",\",\n \"0 if isinstance(op, tuple): op, num_args = op if op == \\\"unary -\\\":\",\n \"op1 = evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi\",\n \"# note: args are pushed onto the stack in reverse order args =\",\n \"e and pi, to avoid accidentally matching # functions that start with 'e'\",\n \"math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda\",\n \"only match whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber =\",\n \"get right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not\",\n \"restriction, including # without limitation the rights to use, copy, modify, merge, publish,\",\n \"Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx\",\n \"expr ')' | '(' expr ')' factor :: atom [ expop factor ]*\",\n \"\\\"min\\\": return stats['min'] elif op == \\\"max\\\": return stats['max'] elif op == \\\"std\\\":\",\n \"LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\",\n \"stats) for _ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\"\",\n \"without restriction, including # without limitation the rights to use, copy, modify, merge,\",\n \"expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the\",\n \"multop factor ]* expr :: term [ addop term ]* \\\"\\\"\\\" # use\",\n \"# # Permission is hereby granted, free of charge, to any person obtaining\",\n \"all copies or substantial portions of the Software. # # THE SOFTWARE IS\",\n \":: term [ addop term ]* \\\"\\\"\\\" # use CaselessKeyword for e and\",\n \"deal in the Software without restriction, including # without limitation the rights to\",\n \"evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi # 3.1415926535\",\n \"note: args are pushed onto the stack in reverse order args = reversed([evaluate_stack(s,\",\n \"== \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\" expop :: '^' multop\",\n \"in toks: if t == \\\"-\\\": exprStack.append(\\\"unary -\\\") else: break def BNF(): \\\"\\\"\\\"\",\n \"(such as 'exp'); Keyword # and CaselessKeyword only match whole words e =\",\n \"alphanums + \\\"_$\\\") plus, minus, mult, div = map(Literal, \\\"+-*/\\\") lpar, rpar =\",\n \"OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR\",\n \"matching # functions that start with 'e' or 'pi' (such as 'exp'); Keyword\",\n \"and CaselessKeyword only match whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") #\",\n \"SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums, Regex,\",\n \"| e | fnumber | ident).setParseAction(push_first) | Group(lpar + expr + rpar) )\",\n \"'mean', 'std') return the result\\\"\\\"\\\" _ = BNF().parseString(fx, parseAll=True) val = evaluate_stack(exprStack[:], stats)\",\n \"that start with 'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword\",\n \"rpar = map(Suppress, \\\"()\\\") addop = plus | minus multop = mult |\",\n \"delimitedList(Group(expr)) # add parse action that replaces the function identifier with a (name,\",\n \"bnf = expr return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0\",\n \"-\\\") else: break def BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*' |\",\n \"OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO\",\n \"= Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces the function\",\n \"to any person obtaining # a copy of this software and associated documentation\",\n \"operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = {\",\n \"% op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats ('min',\",\n \"is hereby granted, free of charge, to any person obtaining # a copy\",\n \"-1 if a < -epsilon else 1 if a > epsilon else 0,\",\n \"| '-'] '0'..'9'+ atom :: PI | E | real | fn '('\",\n \"op2) elif op == \\\"PI\\\": return math.pi # 3.1415926535 elif op == \\\"E\\\":\",\n \"factor ]* term :: factor [ multop factor ]* expr :: term [\",\n \"FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE\",\n \"alphanums, Regex, CaselessKeyword, Suppress, delimitedList, ) import math import operator # map operator\",\n \"Software without restriction, including # without limitation the rights to use, copy, modify,\",\n \"\\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop = plus | minus multop =\",\n \"above copyright notice and this permission notice shall be # included in all\",\n \"Suppress, delimitedList, ) import math import operator # map operator symbols to corresponding\",\n \"or use provided pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda\",\n \"[ ^ factor ]...\\\" instead of \\\"atom [ ^ atom ]...\\\", we get\",\n \"sell copies of the Software, and to # permit persons to whom the\",\n \"map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop = plus | minus multop\",\n \"= mult | div expop = Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr))\",\n \"following conditions: # # The above copyright notice and this permission notice shall\",\n \"[ ^ atom ]...\\\", we get right-to-left # exponents, instead of left-to-right that\",\n \"\\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a:\",\n \"abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\": lambda a: -1 if a\",\n \"in reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1,\",\n \"| div expop = Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) # add\",\n \"free of charge, to any person obtaining # a copy of this software\",\n \"The above copyright notice and this permission notice shall be # included in\",\n \"subject to # the following conditions: # # The above copyright notice and\",\n \"\\\"/\\\": operator.truediv, \\\"^\\\": operator.pow, } fn = { \\\"sin\\\": math.sin, \\\"cos\\\": math.cos, \\\"tan\\\":\",\n \"[] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if t ==\",\n \"rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) ) atom = ( addop[...] + (\",\n \"# the following conditions: # # The above copyright notice and this permission\",\n \"expr :: term [ addop term ]* \\\"\\\"\\\" # use CaselessKeyword for e\",\n \"'e' or 'pi' (such as 'exp'); Keyword # and CaselessKeyword only match whole\",\n \"return stats['std'] elif op in fn: # note: args are pushed onto the\",\n \"exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks: if\",\n \"rpar) ) ).setParseAction(push_unary_minus) # by defining exponentiation as \\\"atom [ ^ factor ]...\\\"\",\n \"reverse order op2 = evaluate_stack(s, stats) op1 = evaluate_stack(s, stats) return opn[op](op1, op2)\",\n \"# add parse action that replaces the function identifier with a (name, number\",\n \"IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED,\",\n \":: factor [ multop factor ]* expr :: term [ addop term ]*\",\n \"_ in range(num_args)]) return fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op)\",\n \"THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. #\",\n \"# permit persons to whom the Software is furnished to do so, subject\",\n \"DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR\",\n \"Literal(\\\"^\\\") expr = Forward() expr_list = delimitedList(Group(expr)) # add parse action that replaces\",\n \":: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI\",\n \"\\\"tan\\\": math.tan, \\\"exp\\\": math.exp, \\\"abs\\\": abs, \\\"trunc\\\": lambda a: int(a), \\\"round\\\": round, \\\"sgn\\\":\",\n \"'%s'\\\" % op) else: return float(op) def eval_fx(fx, stats): \\\"\\\"\\\"Given fx and stats\",\n \"pyparsing_common.number, but convert back to str: # fnumber = ppc.number().addParseAction(lambda t: str(t[0])) fnumber\",\n \"tuple fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0,\",\n \"software and associated documentation files (the # \\\"Software\\\"), to deal in the Software\",\n \"(ident + lpar - Group(expr_list) + rpar).setParseAction( lambda t: t.insert(0, (t.pop(0), len(t[0]))) )\",\n \"return bnf def evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple):\",\n \"elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op) def eval_fx(fx,\",\n \"= evaluate_stack(s, stats) return opn[op](op1, op2) elif op == \\\"PI\\\": return math.pi #\",\n \"right-to-left # exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2.\",\n \"'*' | '/' addop :: '+' | '-' integer :: ['+' | '-']\",\n \"epsilon else 0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for\",\n \"FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\",\n \"to whom the Software is furnished to do so, subject to # the\",\n \"evaluate_stack(s, stats): op, num_args = s.pop(), 0 if isinstance(op, tuple): op, num_args =\",\n \"fn[op](*args) elif op[0].isalpha(): raise Exception(\\\"invalid identifier '%s'\\\" % op) else: return float(op) def\",\n \"0, } exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in\",\n \"of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor\",\n \"} exprStack = [] def push_first(toks): exprStack.append(toks[0]) def push_unary_minus(toks): for t in toks:\",\n \"# # THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND,\",\n \"THE SOFTWARE. # from pyparsing import ( Literal, Word, Group, Forward, alphas, alphanums,\",\n \"action that replaces the function identifier with a (name, number of args) tuple\",\n \"BNF(): \\\"\\\"\\\" expop :: '^' multop :: '*' | '/' addop :: '+'\",\n \"OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS\",\n \"in \\\"+-*/^\\\": # note: operands are pushed onto the stack in reverse order\",\n \"= Forward() factor <<= atom + (expop + factor).setParseAction(push_first)[...] term = factor +\",\n \"multop :: '*' | '/' addop :: '+' | '-' integer :: ['+'\",\n \"nums))) # or use provided pyparsing_common.number, but convert back to str: # fnumber\",\n \"| fn '(' expr ')' | '(' expr ')' factor :: atom [\",\n \"addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ atom ::\",\n \"2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor <<= atom + (expop\",\n \"import operator # map operator symbols to corresponding arithmetic operations epsilon = 1e-12\",\n \"op == \\\"E\\\": return math.e # 2.718281828 elif op == \\\"mean\\\": return stats['mean']\",\n \"'-' integer :: ['+' | '-'] '0'..'9'+ atom :: PI | E |\",\n \"whole words e = CaselessKeyword(\\\"E\\\") pi = CaselessKeyword(\\\"PI\\\") # fnumber = Combine(Word(\\\"+-\\\"+nums, nums)\",\n \"div = map(Literal, \\\"+-*/\\\") lpar, rpar = map(Suppress, \\\"()\\\") addop = plus |\",\n \"pushed onto the stack in reverse order op2 = evaluate_stack(s, stats) op1 =\",\n \"'(' expr ')' factor :: atom [ expop factor ]* term :: factor\",\n \"CaselessKeyword for e and pi, to avoid accidentally matching # functions that start\",\n \"FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE\",\n \"AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR\",\n \"expop :: '^' multop :: '*' | '/' addop :: '+' | '-'\",\n \"if op in \\\"+-*/^\\\": # note: operands are pushed onto the stack in\",\n \"atom + (expop + factor).setParseAction(push_first)[...] term = factor + (multop + factor).setParseAction(push_first)[...] expr\"\n]","isConversational":false}}},{"rowIdx":2591,"cells":{"ngram":{"kind":"list like","value":["|= flags if dport in scan.ports: return # Add weight for port if","scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode =","None does scan using RST, however this could be # return packets from","target host # If A does an idle scan on C with B","socket import time import threading import optparse import entry import timerlist __author__ =","scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: #","to the scanning # host when a scanning host is doing an idle","scan on C with B as zombie, # it will appear to C","scan. # Basically # A -scanning host # B - zombie host #","TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda","if scan.type != 'Idle': line = 'Continuation of %s scan from %s to","# print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check #","possible in 60 sec - assuming # a scan occurs at most every","a host address \"\"\" value = addr h = 0 while value: #","longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan","== (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was","Skip packets in reverse direction or invalid protocol return timediff = curr -","'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie))","is idle scanning # Z dummy_scans, idle_ports = [], [] for item in","# of entries - if too many # then this is a regular","dport in scan.ports: return # Add weight for port if dport < 1024:","class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP flags to scan type","== 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP","if key in self.scans: scan = self.scans[key] if scan.src != src: # Skip","scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new","curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key)","strerror): print >> sys.stderr, \"Error opening scan log file %s => %s\" %","incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \"\"\" import sys,","b; a -= c; a ^= (c>>12) b -= c; b -= a;","# so this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST)","self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold =","so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not","== recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' #","from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan","self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ?","#!/usr/bin/env python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but","TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK:","Cleaned up code to publish to google. Apr 8 2010 - Better detection","scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup =","A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))):","continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove","sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0)","idle scan. # Basically # A -scanning host # B - zombie host","ports] if not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d)","= entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr -","B - zombie host # C - target host # If A does","import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ =","Correlation: If 'RST scan' detected from X to Y # See if there","not_scan = True # If we see scan flags 22 from A->B, make","elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP","def host_hash(self, src, dst): \"\"\" Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src,","in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else:","self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break","new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp =","%s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of","= idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry","proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self):","a regular network activity # but not a scan, so remove entry if","flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is","More than 2 entries, but SD is too large, # delete the entry","print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def run(self): # If dameon,","Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2","this could be # return packets from a zombie host to the scanning","max size is equal # to maximum such entries possible in 60 sec","False if scan.flags_or==TH_RST: # None does scan using RST, however this could be","Add weight for port if dport < 1024: scan.weight += 3 else: scan.weight","{http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1. Detects all","b -= a; b ^= (a<<16) c -= a; c -= b; c","(ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s","if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def","(ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible","%s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line =","%s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from","60 sec by default. # Since entries time out in 60 seconds, max","ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst])","TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN:","a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD","scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet }","scan, check # of entries - if too many # then this is","'Continuation of slow %s scan from %s to %s (ports:%s), average timediff %.2fs'","self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return","= [], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and","scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If","delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too","for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP","not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but","received by filter' % nrecv print '%d packets dropped by kernel' % ndrop","time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src,","too many # then this is a regular network activity # but not","doing an idle scan. # Basically # A -scanning host # B -","% (logfile, strerror) self.scanlog = None # Recent scans - this list allows","If we see scan flags 22 from A->B, make sure that # there","\"\"\" Log a message to console and/or log file \"\"\" line = '[%s]:","^= (b>>5) a -= b; a -= c; a ^= (c>>3) b -=","os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2 failed\",","= daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan","'Continuation of %s scan from %s to %s (ports: %s) using zombie host","'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP","v3.0. \"\"\" import sys, os import dpkt, pcap import struct import socket import","if not too old, else skip and remove entry if (timediff > self.timeout):","os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0)","ability to log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect","break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or","= 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH","packets from a zombie host to the scanning # host when a scanning","than 2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight if","be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False)","self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan","= curr - scan.timestamp # Update only if not too old, else skip","%s scan from %s to %s (ports: %s) using zombie host %s' else:","however this could be # return packets from a zombie host to the","logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error opening scan log","'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check # of entries","\"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if","%s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to","self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2, possible slow","recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % '","recent full-connect scan from B->A, if # so this is spurious and should","TCP full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break","self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip if not","scan.type != 'Idle': line = 'Continuation of %s scan from %s to %s","ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip,","lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" #","flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect()","a; b ^= (a<<8) c -= a; c -= b; c ^= (b>>13)","daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold","= curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key]","= curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan =","# If A does an idle scan on C with B as zombie,","= (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False","== recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\" % '","4. Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned up","= lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad","%H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad,","See if there was a SYN scan recently from host # X to","dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD,","'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'","X to Y # See if there was a SYN scan recently from","= src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP:","lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan =","at most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0)","recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst","self.log(\"Ignoring spurious TCP full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan =","average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport,","scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or = 0","Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports = [],","scans - this list allows to keep scan information # upto last 'n'","reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan","If A does an idle scan on C with B as zombie, #","- scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst","lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto","h & (8192-1) def mix(self, a, b, c): a -= b; a -=","line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush()","= self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn","SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK","|= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] =","entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr","0 while value: # print value h ^= value value = value >>","# -- coding: utf-8 #!/usr/bin/env python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired","%s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is","network activity # but not a scan, so remove entry if len(lscan.timediffs)>=10: #","Detects Idle scan and logs it correctly using correlation! 3. Detects SCTP scan.","b; a -= c; a ^= (c>>3) b -= c; b -= a;","if not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from","console \"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports])","B to A # Correlation: If 'RST scan' detected from X to Y","(get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print >>","(flags: %d) from %s to %s (ports: %s) using zombie host %s' else:","slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3","Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged","\"\"\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n')","%.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or","scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type =","# TCP flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN:","((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect","% ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a","if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets received by","threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log file","in ('', 'reply'): not_scan = True # If we see scan flags 22","lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd()","= True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan","Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17 2010","functions. Licensed under GNU GPL v3.0. \"\"\" import sys, os import dpkt, pcap","c -= a; c -= b; c ^= (b>>13) a -= b; a","scan from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg)","also. Modification History Mar 17 2010 - Cleaned up code to publish to","scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or","^= value value = value >> 9 return h & (8192-1) def mix(self,","if timediff not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l:","a -= c; a ^= (c>>13) b -= c; b -= a; b","len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key]","timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l","elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type","pc.stats() print '\\n%d packets received by filter' % nrecv print '%d packets dropped","'.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn scan, see","scanning # host when a scanning host is doing an idle scan. #","= 'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line","scan.flags_or==TH_RST: # None does scan using RST, however this could be # return","= 'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT","'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if","def hash_func(self, addr): \"\"\" Hash a host address \"\"\" value = addr h","host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log a","%s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error opening","self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del","to google. Apr 8 2010 - Better detection of TCP full-connect scan without","# If this is a syn scan, see if there was a recent","log file %s => %s\" % (logfile, strerror) self.scanlog = None # Recent","logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold","continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan:","Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon #","src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags","dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src =","not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\"","KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets received","dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport)","and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan:","'n' seconds, so as to not call duplicate scans # in the same","# Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad","A->B, make sure that # there was no recent full-connect scan from B->A,","zombie, # it will appear to C as if B is syn scanning","else: line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average","= self.scans[key] if scan.src != src: # Skip packets in reverse direction or","from %s to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup)","= True # If we see scan flags 22 from A->B, make sure","full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break #","%s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line =","-scanning host # B - zombie host # C - target host #","import struct import socket import time import threading import optparse import entry import","timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst","idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst","pcap import struct import socket import time import threading import optparse import entry","could be # return packets from a zombie host to the scanning #","(scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\" %","scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc","%d) from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg)","unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True","to save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon,","scan using RST, however this could be # return packets from a zombie","ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in","if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def","else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan","on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except","print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try:","^= (c>>12) b -= c; b -= a; b ^= (a<<16) c -=","= '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP -","self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) #","# a scan occurs at most every 5 seconds, this would be 12.","entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type =","recent in recent1: # Was not a scan, skip if not recent.is_scan: continue","but SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD","= curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto =","such entries possible in 60 sec - assuming # a scan occurs at","large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp","threshold self.threshold = threshold # Timeout for scan entries self.timeout = timeout #","and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break","less than 2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight","= dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto","= [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line =","idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src","is a regular network activity # but not a scan, so remove entry","= 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this","average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s to","scan. 4. Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned","Licensed under GNU GPL v3.0. \"\"\" import sys, os import dpkt, pcap import","srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not","dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self,","- target host # If A does an idle scan on C with","to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation","idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle'","self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self, pkt): if","= ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP:","%s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not","= addr h = 0 while value: # print value h ^= value","is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >=","x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect',","'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans =","help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192,","c ^= (b>>13) a -= b; a -= c; a ^= (c>>12) b","the same time-period. 'n' is 60 sec by default. # Since entries time","60 sec - assuming # a scan occurs at most every 5 seconds,","\"\"\" Port scan detector/logger \"\"\" # TCP flags to scan type mapping scan_types","equal # to maximum such entries possible in 60 sec - assuming #","optparse import entry import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ =","msg): \"\"\" Log a message to console and/or log file \"\"\" line =","{0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK:","host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d)","scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP","scan.type != 'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)'","most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def","weight threshold self.threshold = threshold # Timeout for scan entries self.timeout = timeout","scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or","19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD =","entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold #","recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry)","if scan.type in ('', 'reply'): not_scan = True # If we see scan","the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large","Apr 8 2010 - Better detection of TCP full-connect scan without spurious and","idle scanning # Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans):","^= (b>>15) return abs(c) def host_hash(self, src, dst): \"\"\" Hash mix two host","scan.src != src: # Skip packets in reverse direction or invalid protocol return","len(lscan.timediffs)>2: # More than 2 entries, but SD is too large, # delete","mix(self, a, b, c): a -= b; a -= c; a ^= (c>>13)","old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src !=","-= a; b ^= (a<<10) c -= a; c -= b; c ^=","B as zombie, # it will appear to C as if B is","if unsure: line = 'Possible slow %s scan (flags:%d) from %s to %s","entries, but SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since","return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in","scan detector/logger \"\"\" # TCP flags to scan type mapping scan_types = {0:","be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent","#2 failed\", e sys.exit(1) self.loop() def run(self): # If dameon, then create a","progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG","-= a; c -= b; c ^= (b>>13) a -= b; a -=","pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1)","seconds, so as to not call duplicate scans # in the same time-period.","TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries","we could get an apparent RST \"scan\" # from B to A #","is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST:","Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg):","mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\"","from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d)","scanning host is doing an idle scan. # Basically # A -scanning host","old, else skip and remove entry if (timediff > self.timeout): # Add entry","'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring","to not call duplicate scans # in the same time-period. 'n' is 60","% nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self): #","(ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s","fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print","not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash]","del self.scans[scan.hash] return True else: return False def process(self, pkt): if not hasattr(pkt,","0xffffff)) def log(self, msg): \"\"\" Log a message to console and/or log file","B->A, if # so this is spurious and should be ignored. if scan.flags_or","pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key","failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if","log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2.","syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect',","'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del","scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1]","if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src","lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than","self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan","to %s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error","'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self,","src: # Skip packets in reverse direction or invalid protocol return timediff =","by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1.","lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr","this is a regular network activity # but not a scan, so remove","pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto ==","correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar","if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep","ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop","= os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2","import dpkt, pcap import struct import socket import time import threading import optparse","else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s (ports:","=> %s\" % (logfile, strerror) self.scanlog = None # Recent scans - this","threshold # Timeout for scan entries self.timeout = timeout # Long-period scan timeouts","slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self,","line = 'Continuation of slow %s scan from %s to %s (ports:%s), average","time-period. 'n' is 60 sec by default. # Since entries time out in","('', 'reply'): not_scan = True # If we see scan flags 22 from","self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags","else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev)","else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False","too old, else skip and remove entry if (timediff > self.timeout): # Add","scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4","scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst =","# Add weight for port if dport < 1024: scan.weight += 3 else:","run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\",","self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in","# Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon","file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved","item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports)","scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode","a -= c; a ^= (c>>12) b -= c; b -= a; b","host to the scanning # host when a scanning host is doing an","if # so this is spurious and should be ignored. if scan.flags_or ==","scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP","-= c; b -= a; b ^= (a<<8) c -= a; c -=","print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2:","seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\"","'.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B elif (recent.type","for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not","full-connect scans. 2. Detects Idle scan and logs it correctly using correlation! 3.","recently from host # X to host Z. Then actually Y is idle","pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability","lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags)","scan occurs at most every 5 seconds, this would be 12. self.recent_scans =","Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1]","line = 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average","[scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation","def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize)","self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If we see","%s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s","prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan","idle scan # with this as zombie, then ignore it... elif scan.flags_or ==","print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the","2. Detects Idle scan and logs it correctly using correlation! 3. Detects SCTP","Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l))","# Since entries time out in 60 seconds, max size is equal #","if scan.src != src: # Skip packets in reverse direction or invalid protocol","GPL v3.0. \"\"\" import sys, os import dpkt, pcap import struct import socket","not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets received by filter'","If this is a syn scan, see if there was a recent idle","# there was no recent full-connect scan from B->A, if # so this","sys.exit(\"You must be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\",","seconds, max size is equal # to maximum such entries possible in 60","proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping","return True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return","to console and/or log file \"\"\" line = '[%s]: %s' % (get_timestamp(), msg)","rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if","== TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for","c -= a; c -= b; c ^= (b>>5) a -= b; a","print >> sys.stderr, 'Scan logs will be saved to %s' % logfile except","does an idle scan on C with B as zombie, # it will","dport, flags): scan.flags_or |= flags if dport in scan.ports: return # Add weight","process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp,","type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I',","60 seconds, max size is equal # to maximum such entries possible in","- prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else:","(logfile, strerror) self.scanlog = None # Recent scans - this list allows to","# SD is less than 2, possible slow scan # update port weights...","= scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports =","not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key","= os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1 failed\",","e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def run(self): # If","lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport)","of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation","Log a message to console and/or log file \"\"\" line = '[%s]: %s'","in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants","= 0 while value: # print value h ^= value value = value","is syn scanning it # and later we could get an apparent RST","scan to file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port)","TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold,","a ^= (c>>13) b -= c; b -= a; b ^= (a<<8) c","flags 22 from A->B, make sure that # there was no recent full-connect","main(): if os.geteuid() != 0: sys.exit(\"You must be super-user to run this program\")","Since entries time out in 60 seconds, max size is equal # to","# UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" #","GNU GPL v3.0. \"\"\" import sys, os import dpkt, pcap import struct import","msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |=","# Update only if not too old, else skip and remove entry if","stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs it correctly","if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown')","slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return","self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src","# then this is a regular network activity # but not a scan,","c -= b; c ^= (b>>5) a -= b; a -= c; a","slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if","self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback,","entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan'","3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17","# print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif","True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie =","(TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not","skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src ==","in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if","scan and logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects","and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan","it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def","using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History","== recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP:","Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad =","args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ == '__main__':","lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More","entry in long_scans if timediff not larger # than longscan timeout prev =","'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP","self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key] return","and full-connect scans. 2. Detects Idle scan and logs it correctly using correlation!","scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is","print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check # of","# to maximum such entries possible in 60 sec - assuming # a","time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan:","o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args()","TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK:","it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1","'%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty","src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type","recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): #","TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK:","c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \"\"\" Hash mix two","dameon, then create a new thread and wait for it if self.daemon: print","% logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error opening scan","a; b ^= (a<<16) c -= a; c -= b; c ^= (b>>5)","Then actually Y is idle scanning # Z dummy_scans, idle_ports = [], []","\"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT,","%s scan from %s to %s (ports:%s), average timediff %.2fs' msg = line","% (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print","Port scan detector/logger \"\"\" # TCP flags to scan type mapping scan_types =","so this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and","and later we could get an apparent RST \"scan\" # from B to","entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout,","pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop =","-= c; b -= a; b ^= (a<<16) c -= a; c -=","[] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and","timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s to %s","to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line","timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type","else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True #","recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset","key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst","then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1","mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST:","this as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): #","\"\"\" Hash a host address \"\"\" value = addr h = 0 while","it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also.","b; c ^= (b>>13) a -= b; a -= c; a ^= (c>>12)","'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP","Better logging functions. Licensed under GNU GPL v3.0. \"\"\" import sys, os import","b ^= (a<<8) c -= a; c -= b; c ^= (b>>13) a","= pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening",">>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def run(self): # If dameon, then","= item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True","# Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1:","be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a host","if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add","scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO'","host # X to host Z. Then actually Y is idle scanning #","scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if","# Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will","%s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon:","e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0:","sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid))","unsure: line = 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s),","host # B - zombie host # C - target host # If","as to not call duplicate scans # in the same time-period. 'n' is","del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan)","c -= a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self,","scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See","flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan","%s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from","pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter)","b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \"\"\" Hash mix","lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan","B->A for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src ==","'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line =","a ^= (c>>3) b -= c; b -= a; b ^= (a<<10) c","print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid()","to publish to google. Apr 8 2010 - Better detection of TCP full-connect","time out in 60 seconds, max size is equal # to maximum such","else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip =","scans # in the same time-period. 'n' is 60 sec by default. #","prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2,","print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2","scan (flags: %d) from %s to %s (ports: %s) using zombie host %s'","packets in reverse direction or invalid protocol return timediff = curr - scan.timestamp","not call duplicate scans # in the same time-period. 'n' is 60 sec","= \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010'","if B is syn scanning it # and later we could get an","'\\n%d packets received by filter' % nrecv print '%d packets dropped by kernel'","Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan","(timediff > self.timeout): # Add entry in long_scans if timediff not larger #","-= a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src,","','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if","rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan =","= [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = '%s","not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0","[scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP flags to","for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop,","c -= b; c ^= (b>>13) a -= b; a -= c; a","curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto","proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |=","get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x))","self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self,","print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc:","%s => %s\" % (logfile, strerror) self.scanlog = None # Recent scans -","curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan =","UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x:","else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('',","port-scans also. Modification History Mar 17 2010 - Cleaned up code to publish","= rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans:","TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent","large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else:","if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1:","value h ^= value value = value >> 9 return h & (8192-1)","%s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from","from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this","is equal # to maximum such entries possible in 60 sec - assuming","ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s","coding: utf-8 #!/usr/bin/env python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd","a new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else:","# Correlation: If 'RST scan' detected from X to Y # See if","rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d","Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan","zombie host # C - target host # If A does an idle","a; c -= b; c ^= (b>>5) a -= b; a -= c;","'[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not","self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too large,","\"\"\" Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self,","self.loop() def run(self): # If dameon, then create a new thread and wait","self.inspect_scan(lscan, True): # Not a scan, check # of entries - if too","scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return # Add","daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs","scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1. Detects","# Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight <","not_scan = True break # If this is a syn scan, see if","try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to","dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1] if","A -scanning host # B - zombie host # C - target host","else skip and remove entry if (timediff > self.timeout): # Add entry in","entry import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__","except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets","file %s => %s\" % (logfile, strerror) self.scanlog = None # Recent scans","scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation,","sys.stderr, \"Error opening scan log file %s => %s\" % (logfile, strerror) self.scanlog","b ^= (a<<16) c -= a; c -= b; c ^= (b>>5) a","# C - target host # If A does an idle scan on","sec - assuming # a scan occurs at most every 5 seconds, this","SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd","in the same time-period. 'n' is 60 sec by default. # Since entries","recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan from","2010 - Cleaned up code to publish to google. Apr 8 2010 -","wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground","2010 - Better detection of TCP full-connect scan without spurious and incorrect logging.","(ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s","line = 'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie))","return abs(c) def host_hash(self, src, dst): \"\"\" Hash mix two host addresses \"\"\"","scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If","with this as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans):","Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5","save logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile)","or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan","dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type","value = addr h = 0 while value: # print value h ^=","direction or invalid protocol return timediff = curr - scan.timestamp # Update only","flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP","= threshold # Timeout for scan entries self.timeout = timeout # Long-period scan","detection of TCP full-connect scan without spurious and incorrect logging. Better logging functions.","- this list allows to keep scan information # upto last 'n' seconds,","\"\"\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added","if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1)","if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst)","tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from %s to","msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr,","run(self): # If dameon, then create a new thread and wait for it","for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del","self.scans[scan.hash] return True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'):","to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s","= '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line =","scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if","remove entry if (timediff > self.timeout): # Add entry in long_scans if timediff","b -= c; b -= a; b ^= (a<<8) c -= a; c","host address \"\"\" value = addr h = 0 while value: # print","(scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP'","not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from %s","host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect","\"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid =","logging functions. Licensed under GNU GPL v3.0. \"\"\" import sys, os import dpkt,","be saved to %s' % logfile except (IOError, OSError), (errno, strerror): print >>","(ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan,","os.geteuid() != 0: sys.exit(\"You must be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\",","TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', #","run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except","len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1:","# Daemonize ? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a')","-= b; c ^= (b>>13) a -= b; a -= c; a ^=","ndrop, nifdrop = pc.stats() print '\\n%d packets received by filter' % nrecv print","%s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure:","# but not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src,","scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP flags to scan","= False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan =","OSError, e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second","else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s (ports:","scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST, however","if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del","# Basically # A -scanning host # B - zombie host # C","self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def","except OSError, e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def run(self):","action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args","3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan","= type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time()","inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l)","scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports #","!= 'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)' else:","and/or log file \"\"\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog:","not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else:","logs to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run()","in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto","__version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP","timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu","regular network activity # but not a scan, so remove entry if len(lscan.timediffs)>=10:","(b>>5) a -= b; a -= c; a ^= (c>>3) b -= c;","timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan =","len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or","\"\"\" Log the scan to file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan)","? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print >>","dpkt, pcap import struct import socket import time import threading import optparse import","22 from A->B, make sure that # there was no recent full-connect scan","tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line =","def run(self): # If dameon, then create a new thread and wait for","(pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon:","__author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr","in long_scans if timediff not larger # than longscan timeout prev = self.scans[key].timestamp","update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return #","scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight","continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst ==","b -= c; b -= a; b ^= (a<<16) c -= a; c","+= 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan","else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if","Y is idle scanning # Z dummy_scans, idle_ports = [], [] for item","= 'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP:","X to host Z. Then actually Y is idle scanning # Z dummy_scans,","in 60 sec - assuming # a scan occurs at most every 5","!= src: # Skip packets in reverse direction or invalid protocol return timediff","None # Recent scans - this list allows to keep scan information #","recent idle scan # with this as zombie, then ignore it... elif scan.flags_or","= open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s' %","of slow %s scan from %s to %s (ports:%s), average timediff %.2fs' msg","will be saved to %s' % logfile except (IOError, OSError), (errno, strerror): print","[], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN","line = 'Continuation of %s scan from %s to %s (ports: %s) using","host when a scanning host is doing an idle scan. # Basically #","(recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type =","scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup =","to\", default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if","by filter' % nrecv print '%d packets dropped by kernel' % ndrop def","print >> sys.stderr, \"Error opening scan log file %s => %s\" % (logfile,","Modification History Mar 17 2010 - Cleaned up code to publish to google.","if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2 failed\", e","- Cleaned up code to publish to google. Apr 8 2010 - Better","not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and","\"\"\" import sys, os import dpkt, pcap import struct import socket import time","line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff","packets received by filter' % nrecv print '%d packets dropped by kernel' %","^= (c>>3) b -= c; b -= a; b ^= (a<<10) c -=","-= b; a -= c; a ^= (c>>3) b -= c; b -=","e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork","e sys.exit(1) self.loop() def run(self): # If dameon, then create a new thread","x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan","of entries - if too many # then this is a regular network","# host when a scanning host is doing an idle scan. # Basically","TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan","# See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if","lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2, possible","# Was not a scan, skip if not recent.is_scan: continue if recent.type ==","under GNU GPL v3.0. \"\"\" import sys, os import dpkt, pcap import struct","to A # Correlation: If 'RST scan' detected from X to Y #","ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP,","UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP","-= c; b -= a; b ^= (a<<10) c -= a; c -=","recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie","= pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP,","c; a ^= (c>>13) b -= c; b -= a; b ^= (a<<8)","= pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc =","key in self.scans: scan = self.scans[key] if scan.src != src: # Skip packets","((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) #","'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP","TCP full-connect scan without spurious and incorrect logging. Better logging functions. Licensed under","2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight if not","- scan.timestamp # Update only if not too old, else skip and remove","pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP):","[pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt","full-connect scan from B->A, if # so this is spurious and should be","when a scanning host is doing an idle scan. # Basically # A","except (IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error opening scan log file","slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from %s to","If dameon, then create a new thread and wait for it if self.daemon:","True break # If this is a syn scan, see if there was","constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp",">= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan","weight for port if dport < 1024: scan.weight += 3 else: scan.weight +=","in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host","not too old, else skip and remove entry if (timediff > self.timeout): #","zombie host to the scanning # host when a scanning host is doing","\"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11","< 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan,","without spurious and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0.","and logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow","create a new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon()","detected from X to Y # See if there was a SYN scan","host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s","__maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST","apparent RST \"scan\" # from B to A # Correlation: If 'RST scan'","1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False):","options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ ==","dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation:","= lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr","port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan:","import entry import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1'","= None # Recent scans - this list allows to keep scan information","# Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You must","lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr","remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr","\"\"\" # TCP flags to scan type mapping scan_types = {0: 'TCP null',","'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: #","lscan.time_sd<2: # SD is less than 2, possible slow scan # update port","Basically # A -scanning host # B - zombie host # C -","TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda","timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port","self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout","if scan.type != 'Idle': line = '%s scan (flags:%d) from %s to %s","slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects","scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto","SD is less than 2, possible slow scan # update port weights... #","print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key","-= b; a -= c; a ^= (c>>13) b -= c; b -=","-= a; c -= b; c ^= (b>>5) a -= b; a -=","Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be","item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp -","ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0:","on C with B as zombie, # it will appear to C as","% tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport","return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: #","syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas',","to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn scan,","entry if (timediff > self.timeout): # Add entry in long_scans if timediff not","return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto","import time import threading import optparse import entry import timerlist __author__ = \"pythonhacker\"","'Scan logs will be saved to %s' % logfile except (IOError, OSError), (errno,","else: del self.scans[scan.hash] return True else: return False def process(self, pkt): if not","if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' %","ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags key =","# print value h ^= value value = value >> 9 return h","Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp","dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src","from A->B, make sure that # there was no recent full-connect scan from","scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan","host is doing an idle scan. # Basically # A -scanning host #","and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\"","scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log","try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr,","fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas',","= entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |=","TCP flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP","(slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if","with added ability to log slow port-scans. Features 1. Detects all stealth (half-open)","= \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8","If 'RST scan' detected from X to Y # See if there was","# TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP","fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a","not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle':","del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self, pkt):","maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan =","+= 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure","Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle'","the scan to file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports =","Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You must be","timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags):","self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s'","c; b -= a; b ^= (a<<16) c -= a; c -= b;","it # and later we could get an apparent RST \"scan\" # from","is 60 sec by default. # Since entries time out in 60 seconds,","a SYN scan recently from host # X to host Z. Then actually",">= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and","% (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not","line = '%s scan (flags: %d) from %s to %s (ports: %s) using","same time-period. 'n' is 60 sec by default. # Since entries time out","COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan)","entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout for scan","was a recent idle scan # with this as zombie, then ignore it...","if (timediff > self.timeout): # Add entry in long_scans if timediff not larger","o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save","a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans =","the scanning # host when a scanning host is doing an idle scan.","TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans","B is syn scanning it # and later we could get an apparent","or invalid protocol return timediff = curr - scan.timestamp # Update only if","Idle scan and logs it correctly using correlation! 3. Detects SCTP scan. 4.","curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] =","threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan","scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif","12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a host address","c ^= (b>>5) a -= b; a -= c; a ^= (c>>3) b","scan flags 22 from A->B, make sure that # there was no recent","= 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan,","60.0) def hash_func(self, addr): \"\"\" Hash a host address \"\"\" value = addr","# and later we could get an apparent RST \"scan\" # from B","and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src =","%s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow","scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d)","full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and","^= (c>>13) b -= c; b -= a; b ^= (a<<8) c -=","scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan","scan.ports: return # Add weight for port if dport < 1024: scan.weight +=","- prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than","Y # See if there was a SYN scan recently from host #","'n' is 60 sec by default. # Since entries time out in 60","# See if there was a SYN scan recently from host # X","and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP","return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan =","Add entry in long_scans if timediff not larger # than longscan timeout prev","= ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold))","lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp =","scanning it # and later we could get an apparent RST \"scan\" #","program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to","average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if","OSError), (errno, strerror): print >> sys.stderr, \"Error opening scan log file %s =>","= scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup","scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10:","__modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress...","= src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |=","key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in","# If dameon, then create a new thread and wait for it if","# Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else:","TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d","scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP","if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan,","Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e:","= True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie","# X to host Z. Then actually Y is idle scanning # Z","timediff = curr - scan.timestamp # Update only if not too old, else","scan, see if there was a recent idle scan # with this as","remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a","(b>>13) a -= b; a -= c; a ^= (c>>12) b -= c;","scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src","scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan =","C as if B is syn scanning it # and later we could","TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP","will appear to C as if B is syn scanning it # and","idle scan on C with B as zombie, # it will appear to","host Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports =","PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols","|= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key]","if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets","host_hash(self, src, dst): \"\"\" Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst,","if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if","socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port","= self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip if","scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does","0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type =","to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\",","scanning # Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan","scan from B->A, if # so this is spurious and should be ignored.","then create a new thread and wait for it if self.daemon: print 'Daemonizing...'","if dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def","(ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow","google. Apr 8 2010 - Better detection of TCP full-connect scan without spurious","scan weight threshold self.threshold = threshold # Timeout for scan entries self.timeout =","a scan occurs at most every 5 seconds, this would be 12. self.recent_scans","be # return packets from a zombie host to the scanning # host","a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print","keep scan information # upto last 'n' seconds, so as to not call","b -= a; b ^= (a<<8) c -= a; c -= b; c","A # Correlation: If 'RST scan' detected from X to Y # See","self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan","# Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key]","curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2:","scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst","# Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst","does scan using RST, however this could be # return packets from a","not a scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect'","scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type","self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst","try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork","strerror) self.scanlog = None # Recent scans - this list allows to keep","port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan,","else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from %s","'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP","self.scans: scan = self.scans[key] if scan.src != src: # Skip packets in reverse","slow scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True):","scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger","log(self, msg): \"\"\" Log a message to console and/or log file \"\"\" line","import socket import time import threading import optparse import entry import timerlist __author__","Timeout for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l =","unsure=False): \"\"\" Log the scan to file and/or console \"\"\" srcip, dstip =","file \"\"\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line +","elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently","dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto =","'%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s","if os.geteuid() != 0: sys.exit(\"You must be super-user to run this program\") o=optparse.OptionParser()","Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type","a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst):","in reverse direction or invalid protocol return timediff = curr - scan.timestamp #","from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A","timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize","full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'}","-= a; b ^= (a<<16) c -= a; c -= b; c ^=","entries possible in 60 sec - assuming # a scan occurs at most","this is a syn scan, see if there was a recent idle scan","value = value >> 9 return h & (8192-1) def mix(self, a, b,","publish to google. Apr 8 2010 - Better detection of TCP full-connect scan","there was no recent full-connect scan from B->A, if # so this is","RST, however this could be # return packets from a zombie host to","timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp =","1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie:","print '%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from","for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst)","= self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src","c): a -= b; a -= c; a ^= (c>>13) b -= c;","nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets received by filter' % nrecv","in self.scans: scan = self.scans[key] if scan.src != src: # Skip packets in","(flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags:","entries - if too many # then this is a regular network activity","self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan =","self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight","ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP flags to scan type mapping","break # If this is a syn scan, see if there was a","from host # X to host Z. Then actually Y is idle scanning","every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self,","# return packets from a zombie host to the scanning # host when","decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s:","was a SYN scan recently from host # X to host Z. Then","a -= b; a -= c; a ^= (c>>3) b -= c; b","# than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in","dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\")","but not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs,","kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork()","scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove","Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned up code","of %s scan from %s to %s (ports: %s) using zombie host %s'","SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I',","= 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff","in recent1: # Was not a scan, skip if not recent.is_scan: continue if","from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print","scan information # upto last 'n' seconds, so as to not call duplicate","line = '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line","entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period","# Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else:","for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp","return # Add weight for port if dport < 1024: scan.weight += 3","} [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts,","pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv,","and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in","not self.inspect_scan(lscan, True): # Not a scan, check # of entries - if","== TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old","self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid() != 0:","self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry","' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B","TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp =","foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You must be super-user to","lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or","= lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\"","25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN #","len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged","'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not","threading import optparse import entry import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\"","proto = type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst)","self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if","get an apparent RST \"scan\" # from B to A # Correlation: If","default. # Since entries time out in 60 seconds, max size is equal","code to publish to google. Apr 8 2010 - Better detection of TCP","b; c ^= (b>>5) a -= b; a -= c; a ^= (c>>3)","entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src != src:",">> 9 return h & (8192-1) def mix(self, a, b, c): a -=","= scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet","scans. 2. Detects Idle scan and logs it correctly using correlation! 3. Detects","%s to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg)","break # Reply from B->A for full-connect scan from A->B elif (recent.type ==","'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect'","time import threading import optparse import entry import timerlist __author__ = \"pythonhacker\" __maintainer__","Log the scan to file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports","== recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan","self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports:","two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log","(scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True",">>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid","only if not too old, else skip and remove entry if (timediff >","maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight","pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap()","8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD","using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s","(TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto =","-= b; c ^= (b>>5) a -= b; a -= c; a ^=","self.scanlog = None # Recent scans - this list allows to keep scan","' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn","# for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan:","appear to C as if B is syn scanning it # and later","sure that # there was no recent full-connect scan from B->A, if #","2 entries, but SD is too large, # delete the entry # print",">> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan","# Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError,","else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle':","(flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type,","help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options,","log file \"\"\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line","= self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If we","TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def","maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None","lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object):","scan' detected from X to Y # See if there was a SYN","Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload","= line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags","Not a scan, check # of entries - if too many # then","a syn scan, see if there was a recent idle scan # with","h = 0 while value: # print value h ^= value value =","pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1","as zombie, # it will appear to C as if B is syn","c; b -= a; b ^= (a<<10) c -= a; c -= b;","Better detection of TCP full-connect scan without spurious and incorrect logging. Better logging","and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a","Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return","so as to not call duplicate scans # in the same time-period. 'n'","= lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class","A does an idle scan on C with B as zombie, # it","% ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from","# None does scan using RST, however this could be # return packets","'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt))","%.2fs' else: line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s),","- zombie host # C - target host # If A does an",": time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda","scan without spurious and incorrect logging. Better logging functions. Licensed under GNU GPL","non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data","and remove entry if (timediff > self.timeout): # Add entry in long_scans if","WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN","# in the same time-period. 'n' is 60 sec by default. # Since","else: # Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You","True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip","there was a SYN scan recently from host # X to host Z.","not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp","port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle","%s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line","scan.flags_or |= flags if dport in scan.ports: return # Add weight for port","utf-8 #!/usr/bin/env python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd}","= entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout for","self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try: self.scanlog =","tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line","# Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged:","c; a ^= (c>>3) b -= c; b -= a; b ^= (a<<10)","with B as zombie, # it will appear to C as if B","that # there was no recent full-connect scan from B->A, if # so","to C as if B is syn scanning it # and later we","default=\"/var/log/scanlog\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__","scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from","src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags =","if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST,","of TCP full-connect scan without spurious and incorrect logging. Better logging functions. Licensed","if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)):","^= (a<<16) c -= a; c -= b; c ^= (b>>5) a -=","c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \"\"\"","# Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError,","timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a host address \"\"\" value =","1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan and","SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17 2010 -","else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if","hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data)","entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False","to file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for","full-connect scan without spurious and incorrect logging. Better logging functions. Licensed under GNU","if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst =","#1 failed\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork()","= self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try: self.scanlog","= dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type","sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to","def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to file and/or","it will appear to C as if B is syn scanning it #","scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan)))","-= a; b ^= (a<<8) c -= a; c -= b; c ^=","3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon =","def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >=","(c>>12) b -= c; b -= a; b ^= (a<<16) c -= a;","scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan","return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log a message to console","from %s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip,","return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip","log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to file and/or console","# Recent scans - this list allows to keep scan information # upto","def log(self, msg): \"\"\" Log a message to console and/or log file \"\"\"","in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan)","5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr):","filter' % nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self):","if not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan","not_scan = False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan","TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())","scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize)","# A -scanning host # B - zombie host # C - target","for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from","key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan,","slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan from %s","tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in","syn scan, see if there was a recent idle scan # with this","information # upto last 'n' seconds, so as to not call duplicate scans","= True break # If this is a syn scan, see if there","was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans:","'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize,","then this is a regular network activity # but not a scan, so","del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport,","from B->A, if # so this is spurious and should be ignored. if","console and/or log file \"\"\" line = '[%s]: %s' % (get_timestamp(), msg) if","else: # Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst =","- assuming # a scan occurs at most every 5 seconds, this would","IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25","and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged =","host # If A does an idle scan on C with B as","= False if scan.flags_or==TH_RST: # None does scan using RST, however this could","TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST:","%s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to","= '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if","if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item)","flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src = src","^= (a<<8) c -= a; c -= b; c ^= (b>>13) a -=","flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp","and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and","= entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True","while value: # print value h ^= value value = value >> 9","%s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan","in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30):","from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if","!= 'Idle': line = 'Continuation of %s scan from %s to %s (ports:%s)'","from B->A for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src","= pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if","spurious TCP full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan))) not_scan = True","all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout,","-= b; a -= c; a ^= (c>>12) b -= c; b -=","occurs at most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12,","C with B as zombie, # it will appear to C as if","Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans","# with this as zombie, then ignore it... elif scan.flags_or == TH_SYN and","by kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid =","-= c; a ^= (c>>3) b -= c; b -= a; b ^=","a ^= (c>>12) b -= c; b -= a; b ^= (a<<16) c","scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif","last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and","hash_func(self, addr): \"\"\" Hash a host address \"\"\" value = addr h =","'\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False,","self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan","is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1","- if too many # then this is a regular network activity #","return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry","(errno, strerror): print >> sys.stderr, \"Error opening scan log file %s => %s\"","o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs","elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or =","# it will appear to C as if B is syn scanning it","'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT =","to keep scan information # upto last 'n' seconds, so as to not","idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src","(IOError, OSError), (errno, strerror): print >> sys.stderr, \"Error opening scan log file %s","self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try:","True): # Not a scan, check # of entries - if too many","\"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\",","del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key]","slow %s scan from %s to %s (ports:%s), average timediff %.2fs' msg =","lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: #","by default. # Since entries time out in 60 seconds, max size is","for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop()","to maximum such entries possible in 60 sec - assuming # a scan","- Better detection of TCP full-connect scan without spurious and incorrect logging. Better","full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True,","pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for","(a<<16) c -= a; c -= b; c ^= (b>>5) a -= b;","'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP","ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack',","if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type =","self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src =","= curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if","+ '\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan,","scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else:","an idle scan. # Basically # A -scanning host # B - zombie","TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S',","2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\"","sec by default. # Since entries time out in 60 seconds, max size","to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s","an idle scan on C with B as zombie, # it will appear","new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: #","= 'Continuation of %s scan from %s to %s (ports: %s) using zombie","idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST, however this","elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 =","from a zombie host to the scanning # host when a scanning host","# Add entry in long_scans if timediff not larger # than longscan timeout","TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN:","self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False,","= o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ == '__main__': main()","a message to console and/or log file \"\"\" line = '[%s]: %s' %","a, b, c): a -= b; a -= c; a ^= (c>>13) b","assuming # a scan occurs at most every 5 seconds, this would be","Mar 17 2010 - Cleaned up code to publish to google. Apr 8","recent1: # Was not a scan, skip if not recent.is_scan: continue if recent.type","from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line","as if B is syn scanning it # and later we could get","a; b ^= (a<<10) c -= a; c -= b; c ^= (b>>15)","to Y # See if there was a SYN scan recently from host","in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp =","os import dpkt, pcap import struct import socket import time import threading import","not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan from","b -= c; b -= a; b ^= (a<<10) c -= a; c","and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan:","%s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from","False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False","flags) if lscan.time_sd<2: # SD is less than 2, possible slow scan #","line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to file","type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn',","c; a ^= (c>>12) b -= c; b -= a; b ^= (a<<16)","sys, os import dpkt, pcap import struct import socket import time import threading","later we could get an apparent RST \"scan\" # from B to A","== 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious","and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to","scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring","if lscan.time_sd<2: # SD is less than 2, possible slow scan # update","we see scan flags 22 from A->B, make sure that # there was","size is equal # to maximum such entries possible in 60 sec -","def mix(self, a, b, c): a -= b; a -= c; a ^=","h ^= value value = value >> 9 return h & (8192-1) def","zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan","python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with","%s\" % (logfile, strerror) self.scanlog = None # Recent scans - this list","int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags key","((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst","full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\"Ignoring spurious","if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags =","tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s (ports: %s)","scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry","to host Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports","scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s","slow_scan=False, unsure=False): \"\"\" Log the scan to file and/or console \"\"\" srcip, dstip","1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and","dst): \"\"\" Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def","%s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports]","= timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a host address \"\"\" value","self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else:","if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del","flags): scan.flags_or |= flags if dport in scan.ports: return # Add weight for","'RST scan' detected from X to Y # See if there was a","continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to file and/or console \"\"\" srcip,","scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 #","if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False):","self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags)","print value h ^= value value = value >> 9 return h &","loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try:","'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not","super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\",","scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key]","x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags',","%.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type !=","pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print","def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore","|= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD","opening scan log file %s => %s\" % (logfile, strerror) self.scanlog = None","duplicate scans # in the same time-period. 'n' is 60 sec by default.","^= (b>>13) a -= b; a -= c; a ^= (c>>12) b -=","Update only if not too old, else skip and remove entry if (timediff","recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted","to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN:","scan # with this as zombie, then ignore it... elif scan.flags_or == TH_SYN","would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a","recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s'","\"pythonhacker\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' #","too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key]","actually Y is idle scanning # Z dummy_scans, idle_ports = [], [] for","list allows to keep scan information # upto last 'n' seconds, so as","# Not a scan, check # of entries - if too many #","= 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon","'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else:","-= c; a ^= (c>>12) b -= c; b -= a; b ^=","'SCTP COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan, not","larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not","RST \"scan\" # from B to A # Correlation: If 'RST scan' detected","out in 60 seconds, max size is equal # to maximum such entries","Recent scans - this list allows to keep scan information # upto last","to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def","entries time out in 60 seconds, max size is equal # to maximum","else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to %s","this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash","long_scans if timediff not larger # than longscan timeout prev = self.scans[key].timestamp if","pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name,","TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN:","zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from","curr - scan.timestamp # Update only if not too old, else skip and","if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type !=","# Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\" % ' to '.join(scan_ip2quad(scan)))","not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan,","a -= b; a -= c; a ^= (c>>13) b -= c; b","for recent in recent1: # Was not a scan, skip if not recent.is_scan:","using RST, however this could be # return packets from a zombie host","file and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port","entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev)","# print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove","SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key]","activity # but not a scan, so remove entry if len(lscan.timediffs)>=10: # print","b -= a; b ^= (a<<10) c -= a; c -= b; c","srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation of","inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features","lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less","rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src","^= (a<<10) c -= a; c -= b; c ^= (b>>15) return abs(c)","flags if dport in scan.ports: return # Add weight for port if dport","'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP","scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log","thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run","del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type","tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr,","idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if","self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr","self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You must be super-user to run","'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK:","recent.src)): # Spurious self.log(\"Ignoring spurious TCP full-connect scan from %s\" % ' to","open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s' % logfile","'0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP - in","!= 0: sys.exit(\"You must be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\",","= int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags","scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line =","protocol return timediff = curr - scan.timestamp # Update only if not too","was no recent full-connect scan from B->A, if # so this is spurious","False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True","c; b -= a; b ^= (a<<8) c -= a; c -= b;","scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log the scan to file and/or console \"\"\"","scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 #","scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry =","if not self.inspect_scan(lscan, True): # Not a scan, check # of entries -","from B to A # Correlation: If 'RST scan' detected from X to","'reply'): not_scan = True # If we see scan flags 22 from A->B,","((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif","9 return h & (8192-1) def mix(self, a, b, c): a -= b;","print '\\n%d packets received by filter' % nrecv print '%d packets dropped by","TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda :","= self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan,","detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow","saved to %s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr,","tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to %s (ports:%s),","from X to Y # See if there was a SYN scan recently","host # C - target host # If A does an idle scan","last 'n' seconds, so as to not call duplicate scans # in the","dst, 0xffffff)) def log(self, msg): \"\"\" Log a message to console and/or log","-= c; a ^= (c>>13) b -= c; b -= a; b ^=","(not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and","scan.type in ('', 'reply'): not_scan = True # If we see scan flags","address \"\"\" value = addr h = 0 while value: # print value","slow port-scans also. Modification History Mar 17 2010 - Cleaned up code to","try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in","self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP:","value value = value >> 9 return h & (8192-1) def mix(self, a,","failed\", e sys.exit(1) self.loop() def run(self): # If dameon, then create a new","d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash]","& (8192-1) def mix(self, a, b, c): a -= b; a -= c;","and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible","Hash a host address \"\"\" value = addr h = 0 while value:","sys.stderr, 'Scan logs will be saved to %s' % logfile except (IOError, OSError),","-- coding: utf-8 #!/usr/bin/env python \"\"\" pyscanlogger: Port scan detector/logger tool, inspired by","scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): #","detector/logger \"\"\" # TCP flags to scan type mapping scan_types = {0: 'TCP","zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last","- in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag","# delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: #","nifdrop = pc.stats() print '\\n%d packets received by filter' % nrecv print '%d","'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP","= timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold","'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid() !=","Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if","self.scans[key] if scan.src != src: # Skip packets in reverse direction or invalid",">> sys.stderr, 'Scan logs will be saved to %s' % logfile except (IOError,","dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del","null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack',","= 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'","this list allows to keep scan information # upto last 'n' seconds, so","b ^= (a<<10) c -= a; c -= b; c ^= (b>>15) return","Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs","is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del","scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports","self.threshold = threshold # Timeout for scan entries self.timeout = timeout # Long-period","this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans):","to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B elif","see scan flags 22 from A->B, make sure that # there was no","not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd","__init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) #","for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600","OSError, e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def run(self): #","message to console and/or log file \"\"\" line = '[%s]: %s' % (get_timestamp(),","pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print","(a<<10) c -= a; c -= b; c ^= (b>>15) return abs(c) def","SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if","a scan, check # of entries - if too many # then this","spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 =","packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty try:","abs(c) def host_hash(self, src, dst): \"\"\" Hash mix two host addresses \"\"\" return","scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight","def run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0)","an apparent RST \"scan\" # from B to A # Correlation: If 'RST","sys.exit(1) self.loop() def run(self): # If dameon, then create a new thread and","spurious and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \"\"\"","scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports","# Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan","self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log a message to console and/or","must be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\",","os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except","return timediff = curr - scan.timestamp # Update only if not too old,","17 2010 - Cleaned up code to publish to google. Apr 8 2010","tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s (ports: %s)","= self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans:","if there was a SYN scan recently from host # X to host","than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans:","src, dst): \"\"\" Hash mix two host addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff))","# Reply from B->A for full-connect scan from A->B elif (recent.type == 'reply'","Reply from B->A for full-connect scan from A->B elif (recent.type == 'reply' and","non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload =","8 2010 - Better detection of TCP full-connect scan without spurious and incorrect","= 'Continuation of slow %s scan from %s to %s (ports:%s), average timediff","self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src != src: #","(half-open) and full-connect scans. 2. Detects Idle scan and logs it correctly using","in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats()","C - target host # If A does an idle scan on C","a -= b; a -= c; a ^= (c>>12) b -= c; b","flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type =","= pc.stats() print '\\n%d packets received by filter' % nrecv print '%d packets","self.timeout): # Add entry in long_scans if timediff not larger # than longscan","update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a","maximum such entries possible in 60 sec - assuming # a scan occurs","added ability to log slow port-scans. Features 1. Detects all stealth (half-open) and","except OSError, e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid() os.umask(0) #","# B - zombie host # C - target host # If A","correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification","0: sys.exit(\"You must be super-user to run this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\",","in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\"You must be super-user","should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for","all stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs it","self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr,","(ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan:","a recent idle scan # with this as zombie, then ignore it... elif","open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop()","recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type","rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans: #","del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too","a zombie host to the scanning # host when a scanning host is","# self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash]","len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan,","# Port scan weight threshold self.threshold = threshold # Timeout for scan entries","allows to keep scan information # upto last 'n' seconds, so as to","scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in","= { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s'","b; a -= c; a ^= (c>>13) b -= c; b -= a;","elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too large, #","# Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l =","syn scanning it # and later we could get an apparent RST \"scan\"","%s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt:","import optparse import entry import timerlist __author__ = \"pythonhacker\" __maintainer__ = \"pythonhacker\" __version__","(a<<8) c -= a; c -= b; c ^= (b>>13) a -= b;","x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\"","many # then this is a regular network activity # but not a","(flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow","(b>>15) return abs(c) def host_hash(self, src, dst): \"\"\" Hash mix two host addresses","= 'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else:","def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()]","%s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan","logging. Better logging functions. Licensed under GNU GPL v3.0. \"\"\" import sys, os","to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if","and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \"\"\" import","is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del","dport, flags) if lscan.time_sd<2: # SD is less than 2, possible slow scan","# Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if","import threading import optparse import entry import timerlist __author__ = \"pythonhacker\" __maintainer__ =","pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on","{ pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' %","\"Error opening scan log file %s => %s\" % (logfile, strerror) self.scanlog =","call duplicate scans # in the same time-period. 'n' is 60 sec by","\"fork #2 failed\", e sys.exit(1) self.loop() def run(self): # If dameon, then create","scan.timestamp # Update only if not too old, else skip and remove entry","# More than 2 entries, but SD is too large, # delete the","there was a recent idle scan # with this as zombie, then ignore","syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin',","in scan.ports: return # Add weight for port if dport < 1024: scan.weight","if scan.flags_or==TH_RST: # None does scan using RST, however this could be #","# update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not","this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in","and/or console \"\"\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in","# If we see scan flags 22 from A->B, make sure that #","if dport in scan.ports: return # Add weight for port if dport <","'%s scan (flags: %d) from %s to %s (ports: %s) using zombie host","make sure that # there was no recent full-connect scan from B->A, if","flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin',","[scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = '%s scan","and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1]","> self.timeout): # Add entry in long_scans if timediff not larger # than","= value >> 9 return h & (8192-1) def mix(self, a, b, c):","del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'):","Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return","= 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type","for port if dport < 1024: scan.weight += 3 else: scan.weight += 1","see if there was a recent idle scan # with this as zombie,","in 60 seconds, max size is equal # to maximum such entries possible","-= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \"\"\" Hash","self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d packets received by filter' %","from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst ==","return packets from a zombie host to the scanning # host when a","is less than 2, possible slow scan # update port weights... # print","if self.scanlog: self.scanlog.write(line + '\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line","\"\"\" value = addr h = 0 while value: # print value h","Daemonize ? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print","# Timeout for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l","# upto last 'n' seconds, so as to not call duplicate scans #","pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1 failed\", e sys.exit(1) os.setsid()","= {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn',","map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \"\"\" Port scan detector/logger \"\"\" # TCP flags","nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect","self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \"\"\" Hash a host address \"\"\"","scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan","to log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans.","using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan","possible slow scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan,","slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else:","def main(): if os.geteuid() != 0: sys.exit(\"You must be super-user to run this","is a syn scan, see if there was a recent idle scan #","< self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True if","b, c): a -= b; a -= c; a ^= (c>>13) b -=","to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d)","a scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and","UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload)","addr): \"\"\" Hash a host address \"\"\" value = addr h = 0","lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: #",">> sys.stderr, \"Error opening scan log file %s => %s\" % (logfile, strerror)","reverse direction or invalid protocol return timediff = curr - scan.timestamp # Update","addresses \"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log a message","scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since","continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line","= rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for","%s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to","but with added ability to log slow port-scans. Features 1. Detects all stealth","proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback,","'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was","if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' #","not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan:","scan = self.scans[key] if scan.src != src: # Skip packets in reverse direction","lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr -","addr h = 0 while value: # print value h ^= value value","is doing an idle scan. # Basically # A -scanning host # B","from %s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s","prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key)","= entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold","than 2 entries, but SD is too large, # delete the entry #","type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() #","(c>>13) b -= c; b -= a; b ^= (a<<8) c -= a;","if there was a recent idle scan # with this as zombie, then","struct import socket import time import threading import optparse import entry import timerlist","pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \"fork","self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport,","upto last 'n' seconds, so as to not call duplicate scans # in","(c>>3) b -= c; b -= a; b ^= (a<<10) c -= a;","= 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN","if too many # then this is a regular network activity # but","SYN scan recently from host # X to host Z. Then actually Y","= proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or","mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break #","(8192-1) def mix(self, a, b, c): a -= b; a -= c; a","tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans.","# from B to A # Correlation: If 'RST scan' detected from X","check # of entries - if too many # then this is a","os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #1 failed\", e","\"scan\" # from B to A # Correlation: If 'RST scan' detected from","See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry","Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and","pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp","= '%s scan (flags: %d) from %s to %s (ports: %s) using zombie","lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad =","% ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork() if","up code to publish to google. Apr 8 2010 - Better detection of","\"\"\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \"\"\" Log a message to","History Mar 17 2010 - Cleaned up code to publish to google. Apr","a; c -= b; c ^= (b>>13) a -= b; a -= c;","syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply","not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if","%s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self,","scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): #","dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid","this program\") o=optparse.OptionParser() o.add_option(\"-d\", \"--daemonize\", dest=\"daemon\", help=\"Daemonize\", action=\"store_true\", default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File","# Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return","%s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if","default=False) o.add_option(\"-f\", \"--logfile\", dest=\"logfile\", help=\"File to save logs to\", default=\"/var/log/scanlog\") options, args =","invalid protocol return timediff = curr - scan.timestamp # Update only if not","and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in","could get an apparent RST \"scan\" # from B to A # Correlation:","Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to","Port scan weight threshold self.threshold = threshold # Timeout for scan entries self.timeout","sys.exit(0) except OSError, e: print >>sys.stderr, \"fork #2 failed\", e sys.exit(1) self.loop() def","Was not a scan, skip if not recent.is_scan: continue if recent.type == 'TCP","= proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = {","packets if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags","logs will be saved to %s' % logfile except (IOError, OSError), (errno, strerror):","as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try","if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not","value >> 9 return h & (8192-1) def mix(self, a, b, c): a","weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check","True # If we see scan flags 22 from A->B, make sure that","False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip #","if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main():","# Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'):","value: # print value h ^= value value = value >> 9 return","ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags","= ','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports]","%s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup","Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e:","'Idle': line = 'Continuation of %s scan from %s to %s (ports:%s)' else:","or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: #","scan log file %s => %s\" % (logfile, strerror) self.scanlog = None #","import sys, os import dpkt, pcap import struct import socket import time import","# Skip packets in reverse direction or invalid protocol return timediff = curr","return h & (8192-1) def mix(self, a, b, c): a -= b; a","recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip","self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\n%d","slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not","not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not","timediff not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if","True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using","line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if","logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans","skip and remove entry if (timediff > self.timeout): # Add entry in long_scans","entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if","ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans","zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for","scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags","entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags","logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False","# Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan =","no recent full-connect scan from B->A, if # so this is spurious and","a -= c; a ^= (c>>3) b -= c; b -= a; b","def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return","'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP","= 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for","and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type =","continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash]","a scanning host is doing an idle scan. # Basically # A -scanning","scan recently from host # X to host Z. Then actually Y is","port if dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport)","Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type","UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init'","5 WEIGHT_THRESHOLD = 25 PIDFILE=\"/var/run/pyscanlogger.pid\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST","full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan","'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries,","scan from %s to %s (ports:%s), average timediff %.2fs' msg = line %","self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \"\"\" Log","%s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of"],"string":"[\n \"|= flags if dport in scan.ports: return # Add weight for port if\",\n \"scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode =\",\n \"None does scan using RST, however this could be # return packets from\",\n \"target host # If A does an idle scan on C with B\",\n \"socket import time import threading import optparse import entry import timerlist __author__ =\",\n \"scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: #\",\n \"to the scanning # host when a scanning host is doing an idle\",\n \"scan on C with B as zombie, # it will appear to C\",\n \"scan. # Basically # A -scanning host # B - zombie host #\",\n \"TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda\",\n \"if scan.type != 'Idle': line = 'Continuation of %s scan from %s to\",\n \"# print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check #\",\n \"possible in 60 sec - assuming # a scan occurs at most every\",\n \"a host address \\\"\\\"\\\" value = addr h = 0 while value: #\",\n \"longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan\",\n \"== (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was\",\n \"Skip packets in reverse direction or invalid protocol return timediff = curr -\",\n \"'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie))\",\n \"is idle scanning # Z dummy_scans, idle_ports = [], [] for item in\",\n \"# of entries - if too many # then this is a regular\",\n \"dport in scan.ports: return # Add weight for port if dport < 1024:\",\n \"class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags to scan type\",\n \"== 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP\",\n \"if key in self.scans: scan = self.scans[key] if scan.src != src: # Skip\",\n \"scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new\",\n \"curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key)\",\n \"strerror): print >> sys.stderr, \\\"Error opening scan log file %s => %s\\\" %\",\n \"incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys,\",\n \"b; a -= c; a ^= (c>>12) b -= c; b -= a;\",\n \"# so this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST)\",\n \"self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold =\",\n \"so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not\",\n \"== recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' #\",\n \"from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan\",\n \"self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ?\",\n \"#!/usr/bin/env python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but\",\n \"TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK:\",\n \"Cleaned up code to publish to google. Apr 8 2010 - Better detection\",\n \"scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup =\",\n \"A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))):\",\n \"continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove\",\n \"sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0)\",\n \"idle scan. # Basically # A -scanning host # B - zombie host\",\n \"ports] if not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d)\",\n \"= entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr -\",\n \"B - zombie host # C - target host # If A does\",\n \"import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ =\",\n \"Correlation: If 'RST scan' detected from X to Y # See if there\",\n \"not_scan = True # If we see scan flags 22 from A->B, make\",\n \"elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP\",\n \"def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src,\",\n \"in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else:\",\n \"self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break\",\n \"new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp =\",\n \"%s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of\",\n \"= idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry\",\n \"proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self):\",\n \"a regular network activity # but not a scan, so remove entry if\",\n \"flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is\",\n \"More than 2 entries, but SD is too large, # delete the entry\",\n \"print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self): # If dameon,\",\n \"Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2\",\n \"this could be # return packets from a zombie host to the scanning\",\n \"max size is equal # to maximum such entries possible in 60 sec\",\n \"False if scan.flags_or==TH_RST: # None does scan using RST, however this could be\",\n \"Add weight for port if dport < 1024: scan.weight += 3 else: scan.weight\",\n \"{http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1. Detects all\",\n \"b -= a; b ^= (a<<16) c -= a; c -= b; c\",\n \"(ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s\",\n \"if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def\",\n \"(ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible\",\n \"%s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line =\",\n \"%s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from\",\n \"60 sec by default. # Since entries time out in 60 seconds, max\",\n \"ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst])\",\n \"TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN:\",\n \"a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD\",\n \"scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet }\",\n \"scan, check # of entries - if too many # then this is\",\n \"'Continuation of slow %s scan from %s to %s (ports:%s), average timediff %.2fs'\",\n \"self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return\",\n \"= [], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and\",\n \"scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If\",\n \"delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too\",\n \"for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP\",\n \"not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but\",\n \"received by filter' % nrecv print '%d packets dropped by kernel' % ndrop\",\n \"time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src,\",\n \"too many # then this is a regular network activity # but not\",\n \"doing an idle scan. # Basically # A -scanning host # B -\",\n \"% (logfile, strerror) self.scanlog = None # Recent scans - this list allows\",\n \"If we see scan flags 22 from A->B, make sure that # there\",\n \"\\\"\\\"\\\" Log a message to console and/or log file \\\"\\\"\\\" line = '[%s]:\",\n \"^= (b>>5) a -= b; a -= c; a ^= (c>>3) b -=\",\n \"os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\",\",\n \"= daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan\",\n \"'Continuation of %s scan from %s to %s (ports: %s) using zombie host\",\n \"'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP\",\n \"v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap import struct import socket import\",\n \"if not too old, else skip and remove entry if (timediff > self.timeout):\",\n \"os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0)\",\n \"ability to log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect\",\n \"break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or\",\n \"= 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH\",\n \"packets from a zombie host to the scanning # host when a scanning\",\n \"than 2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight if\",\n \"be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False)\",\n \"self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan\",\n \"= curr - scan.timestamp # Update only if not too old, else skip\",\n \"%s scan from %s to %s (ports: %s) using zombie host %s' else:\",\n \"however this could be # return packets from a zombie host to the\",\n \"logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening scan log\",\n \"'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check # of entries\",\n \"\\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if\",\n \"%s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to\",\n \"self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2, possible slow\",\n \"recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % '\",\n \"recent full-connect scan from B->A, if # so this is spurious and should\",\n \"TCP full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break\",\n \"self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip if not\",\n \"scan.type != 'Idle': line = 'Continuation of %s scan from %s to %s\",\n \"ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip,\",\n \"lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" #\",\n \"flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect()\",\n \"a; b ^= (a<<8) c -= a; c -= b; c ^= (b>>13)\",\n \"daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold\",\n \"= curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key]\",\n \"= curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan =\",\n \"# If A does an idle scan on C with B as zombie,\",\n \"= (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False\",\n \"== recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" % '\",\n \"4. Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned up\",\n \"= lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad\",\n \"%H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad,\",\n \"See if there was a SYN scan recently from host # X to\",\n \"dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD,\",\n \"'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'\",\n \"X to Y # See if there was a SYN scan recently from\",\n \"= src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP:\",\n \"lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan =\",\n \"at most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0)\",\n \"recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst\",\n \"self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan =\",\n \"average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport,\",\n \"scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or = 0\",\n \"Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports = [],\",\n \"scans - this list allows to keep scan information # upto last 'n'\",\n \"reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan\",\n \"If A does an idle scan on C with B as zombie, #\",\n \"- scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst\",\n \"lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto\",\n \"h & (8192-1) def mix(self, a, b, c): a -= b; a -=\",\n \"line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush()\",\n \"= self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn\",\n \"SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK\",\n \"|= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] =\",\n \"entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr\",\n \"0 while value: # print value h ^= value value = value >>\",\n \"# -- coding: utf-8 #!/usr/bin/env python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired\",\n \"%s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is\",\n \"network activity # but not a scan, so remove entry if len(lscan.timediffs)>=10: #\",\n \"Detects Idle scan and logs it correctly using correlation! 3. Detects SCTP scan.\",\n \"b; a -= c; a ^= (c>>3) b -= c; b -= a;\",\n \"if not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from\",\n \"console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports])\",\n \"B to A # Correlation: If 'RST scan' detected from X to Y\",\n \"(get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print >>\",\n \"(flags: %d) from %s to %s (ports: %s) using zombie host %s' else:\",\n \"slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3\",\n \"Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged\",\n \"\\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n')\",\n \"%.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or\",\n \"scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type =\",\n \"# TCP flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN:\",\n \"((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect\",\n \"% ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a\",\n \"if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets received by\",\n \"threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log file\",\n \"in ('', 'reply'): not_scan = True # If we see scan flags 22\",\n \"lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd()\",\n \"= True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan\",\n \"Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17 2010\",\n \"functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap\",\n \"c -= a; c -= b; c ^= (b>>13) a -= b; a\",\n \"scan from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg)\",\n \"also. Modification History Mar 17 2010 - Cleaned up code to publish to\",\n \"scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or\",\n \"^= value value = value >> 9 return h & (8192-1) def mix(self,\",\n \"if timediff not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l:\",\n \"a -= c; a ^= (c>>13) b -= c; b -= a; b\",\n \"len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key]\",\n \"timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l\",\n \"elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type\",\n \"pc.stats() print '\\\\n%d packets received by filter' % nrecv print '%d packets dropped\",\n \"'.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn scan, see\",\n \"scanning # host when a scanning host is doing an idle scan. #\",\n \"= 'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line\",\n \"scan.flags_or==TH_RST: # None does scan using RST, however this could be # return\",\n \"= 'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT\",\n \"'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if\",\n \"def hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value = addr h\",\n \"host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a\",\n \"%s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening\",\n \"self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del\",\n \"to google. Apr 8 2010 - Better detection of TCP full-connect scan without\",\n \"# If this is a syn scan, see if there was a recent\",\n \"log file %s => %s\\\" % (logfile, strerror) self.scanlog = None # Recent\",\n \"logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold\",\n \"continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan:\",\n \"Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon #\",\n \"src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags\",\n \"dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src =\",\n \"not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\"\",\n \"KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets received\",\n \"dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport)\",\n \"and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan:\",\n \"'n' seconds, so as to not call duplicate scans # in the same\",\n \"# Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad\",\n \"A->B, make sure that # there was no recent full-connect scan from B->A,\",\n \"zombie, # it will appear to C as if B is syn scanning\",\n \"else: line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average\",\n \"= self.scans[key] if scan.src != src: # Skip packets in reverse direction or\",\n \"from %s to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup)\",\n \"= True # If we see scan flags 22 from A->B, make sure\",\n \"full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break #\",\n \"%s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line =\",\n \"-scanning host # B - zombie host # C - target host #\",\n \"import struct import socket import time import threading import optparse import entry import\",\n \"timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst\",\n \"idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst\",\n \"pcap import struct import socket import time import threading import optparse import entry\",\n \"could be # return packets from a zombie host to the scanning #\",\n \"(scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" %\",\n \"scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc\",\n \"%d) from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg)\",\n \"unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True\",\n \"to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon,\",\n \"scan using RST, however this could be # return packets from a zombie\",\n \"ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in\",\n \"if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def\",\n \"else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan\",\n \"on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except\",\n \"print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try:\",\n \"^= (c>>12) b -= c; b -= a; b ^= (a<<16) c -=\",\n \"= '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP -\",\n \"self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) #\",\n \"# a scan occurs at most every 5 seconds, this would be 12.\",\n \"entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type =\",\n \"recent in recent1: # Was not a scan, skip if not recent.is_scan: continue\",\n \"but SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD\",\n \"= curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto =\",\n \"such entries possible in 60 sec - assuming # a scan occurs at\",\n \"large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp\",\n \"threshold self.threshold = threshold # Timeout for scan entries self.timeout = timeout #\",\n \"and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break\",\n \"less than 2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight\",\n \"= dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto\",\n \"= [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line =\",\n \"idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src\",\n \"is a regular network activity # but not a scan, so remove entry\",\n \"= 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this\",\n \"average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s to\",\n \"scan. 4. Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned\",\n \"Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap import\",\n \"srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not\",\n \"dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self,\",\n \"- target host # If A does an idle scan on C with\",\n \"to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation\",\n \"idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle'\",\n \"self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self, pkt): if\",\n \"= ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP:\",\n \"%s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not\",\n \"= addr h = 0 while value: # print value h ^= value\",\n \"is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >=\",\n \"x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect',\",\n \"'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans =\",\n \"help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192,\",\n \"c ^= (b>>13) a -= b; a -= c; a ^= (c>>12) b\",\n \"the same time-period. 'n' is 60 sec by default. # Since entries time\",\n \"60 sec - assuming # a scan occurs at most every 5 seconds,\",\n \"\\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags to scan type mapping scan_types\",\n \"equal # to maximum such entries possible in 60 sec - assuming #\",\n \"optparse import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ =\",\n \"msg): \\\"\\\"\\\" Log a message to console and/or log file \\\"\\\"\\\" line =\",\n \"{0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK:\",\n \"host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d)\",\n \"scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP\",\n \"scan.type != 'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)'\",\n \"most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def\",\n \"weight threshold self.threshold = threshold # Timeout for scan entries self.timeout = timeout\",\n \"scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or\",\n \"19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD =\",\n \"entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold #\",\n \"recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry)\",\n \"if scan.type in ('', 'reply'): not_scan = True # If we see scan\",\n \"the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large\",\n \"Apr 8 2010 - Better detection of TCP full-connect scan without spurious and\",\n \"idle scanning # Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans):\",\n \"^= (b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host\",\n \"scan.src != src: # Skip packets in reverse direction or invalid protocol return\",\n \"len(lscan.timediffs)>2: # More than 2 entries, but SD is too large, # delete\",\n \"mix(self, a, b, c): a -= b; a -= c; a ^= (c>>13)\",\n \"old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src !=\",\n \"-= a; b ^= (a<<10) c -= a; c -= b; c ^=\",\n \"B as zombie, # it will appear to C as if B is\",\n \"if unsure: line = 'Possible slow %s scan (flags:%d) from %s to %s\",\n \"entries, but SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since\",\n \"return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in\",\n \"scan detector/logger \\\"\\\"\\\" # TCP flags to scan type mapping scan_types = {0:\",\n \"be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent\",\n \"#2 failed\\\", e sys.exit(1) self.loop() def run(self): # If dameon, then create a\",\n \"progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG\",\n \"-= a; c -= b; c ^= (b>>13) a -= b; a -=\",\n \"pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1)\",\n \"seconds, so as to not call duplicate scans # in the same time-period.\",\n \"TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries\",\n \"we could get an apparent RST \\\"scan\\\" # from B to A #\",\n \"is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST:\",\n \"Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg):\",\n \"mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\"\",\n \"from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d)\",\n \"scanning host is doing an idle scan. # Basically # A -scanning host\",\n \"old, else skip and remove entry if (timediff > self.timeout): # Add entry\",\n \"'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring\",\n \"to not call duplicate scans # in the same time-period. 'n' is 60\",\n \"% nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self): #\",\n \"(ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s\",\n \"fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print\",\n \"not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash]\",\n \"del self.scans[scan.hash] return True else: return False def process(self, pkt): if not hasattr(pkt,\",\n \"0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message to console and/or log file\",\n \"B->A, if # so this is spurious and should be ignored. if scan.flags_or\",\n \"pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key\",\n \"failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if\",\n \"log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2.\",\n \"syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect',\",\n \"'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del\",\n \"scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1]\",\n \"if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src\",\n \"lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than\",\n \"self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan\",\n \"to %s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error\",\n \"'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self,\",\n \"src: # Skip packets in reverse direction or invalid protocol return timediff =\",\n \"by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1.\",\n \"lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr\",\n \"this is a regular network activity # but not a scan, so remove\",\n \"pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto ==\",\n \"correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar\",\n \"if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep\",\n \"ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop\",\n \"= os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2\",\n \"import dpkt, pcap import struct import socket import time import threading import optparse\",\n \"else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s (ports:\",\n \"=> %s\\\" % (logfile, strerror) self.scanlog = None # Recent scans - this\",\n \"threshold # Timeout for scan entries self.timeout = timeout # Long-period scan timeouts\",\n \"slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self,\",\n \"line = 'Continuation of slow %s scan from %s to %s (ports:%s), average\",\n \"time-period. 'n' is 60 sec by default. # Since entries time out in\",\n \"('', 'reply'): not_scan = True # If we see scan flags 22 from\",\n \"self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags\",\n \"else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev)\",\n \"else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False\",\n \"too old, else skip and remove entry if (timediff > self.timeout): # Add\",\n \"scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4\",\n \"scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst =\",\n \"# Add weight for port if dport < 1024: scan.weight += 3 else:\",\n \"run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\",\",\n \"self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in\",\n \"# Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon\",\n \"file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved\",\n \"item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports)\",\n \"scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode\",\n \"a -= c; a ^= (c>>12) b -= c; b -= a; b\",\n \"host to the scanning # host when a scanning host is doing an\",\n \"if # so this is spurious and should be ignored. if scan.flags_or ==\",\n \"scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP\",\n \"-= c; b -= a; b ^= (a<<8) c -= a; c -=\",\n \"print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2:\",\n \"seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\"\",\n \"'.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B elif (recent.type\",\n \"for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not\",\n \"full-connect scans. 2. Detects Idle scan and logs it correctly using correlation! 3.\",\n \"recently from host # X to host Z. Then actually Y is idle\",\n \"pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability\",\n \"lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags)\",\n \"scan occurs at most every 5 seconds, this would be 12. self.recent_scans =\",\n \"Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1]\",\n \"line = 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average\",\n \"[scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation\",\n \"def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize)\",\n \"self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If we see\",\n \"%s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s\",\n \"prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan\",\n \"idle scan # with this as zombie, then ignore it... elif scan.flags_or ==\",\n \"print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the\",\n \"2. Detects Idle scan and logs it correctly using correlation! 3. Detects SCTP\",\n \"Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l))\",\n \"# Since entries time out in 60 seconds, max size is equal #\",\n \"if scan.src != src: # Skip packets in reverse direction or invalid protocol\",\n \"GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap import struct import socket\",\n \"not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets received by filter'\",\n \"If this is a syn scan, see if there was a recent idle\",\n \"# there was no recent full-connect scan from B->A, if # so this\",\n \"sys.exit(\\\"You must be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\",\",\n \"seconds, max size is equal # to maximum such entries possible in 60\",\n \"proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping\",\n \"return True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return\",\n \"to console and/or log file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg)\",\n \"rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if\",\n \"== TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for\",\n \"c -= a; c -= b; c ^= (b>>5) a -= b; a\",\n \"print >> sys.stderr, 'Scan logs will be saved to %s' % logfile except\",\n \"does an idle scan on C with B as zombie, # it will\",\n \"dport, flags): scan.flags_or |= flags if dport in scan.ports: return # Add weight\",\n \"process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp,\",\n \"type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I',\",\n \"60 seconds, max size is equal # to maximum such entries possible in\",\n \"- prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else:\",\n \"(logfile, strerror) self.scanlog = None # Recent scans - this list allows to\",\n \"# SD is less than 2, possible slow scan # update port weights...\",\n \"= scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports =\",\n \"not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key\",\n \"= os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\",\",\n \"e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self): # If\",\n \"lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport)\",\n \"of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation\",\n \"Log a message to console and/or log file \\\"\\\"\\\" line = '[%s]: %s'\",\n \"in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants\",\n \"= 0 while value: # print value h ^= value value = value\",\n \"is syn scanning it # and later we could get an apparent RST\",\n \"scan to file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port)\",\n \"TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold,\",\n \"a ^= (c>>13) b -= c; b -= a; b ^= (a<<8) c\",\n \"flags 22 from A->B, make sure that # there was no recent full-connect\",\n \"main(): if os.geteuid() != 0: sys.exit(\\\"You must be super-user to run this program\\\")\",\n \"Since entries time out in 60 seconds, max size is equal # to\",\n \"# UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" #\",\n \"GNU GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap import struct import\",\n \"msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |=\",\n \"# Update only if not too old, else skip and remove entry if\",\n \"stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs it correctly\",\n \"if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown')\",\n \"slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return\",\n \"self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src\",\n \"# then this is a regular network activity # but not a scan,\",\n \"c -= b; c ^= (b>>5) a -= b; a -= c; a\",\n \"slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if\",\n \"self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback,\",\n \"entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan'\",\n \"3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17\",\n \"# print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif\",\n \"True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie =\",\n \"(TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not\",\n \"skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src ==\",\n \"in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if\",\n \"scan and logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects\",\n \"and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan\",\n \"it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def\",\n \"using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification History\",\n \"== recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP:\",\n \"Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad =\",\n \"args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ == '__main__':\",\n \"lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More\",\n \"entry in long_scans if timediff not larger # than longscan timeout prev =\",\n \"'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP\",\n \"self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key] return\",\n \"and full-connect scans. 2. Detects Idle scan and logs it correctly using correlation!\",\n \"scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is\",\n \"print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check # of\",\n \"# to maximum such entries possible in 60 sec - assuming # a\",\n \"time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan:\",\n \"o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args()\",\n \"TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK:\",\n \"it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1\",\n \"'%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty\",\n \"src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type\",\n \"recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): #\",\n \"TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK:\",\n \"c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two\",\n \"dameon, then create a new thread and wait for it if self.daemon: print\",\n \"% logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening scan\",\n \"a; b ^= (a<<16) c -= a; c -= b; c ^= (b>>5)\",\n \"Then actually Y is idle scanning # Z dummy_scans, idle_ports = [], []\",\n \"\\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT,\",\n \"%s scan from %s to %s (ports:%s), average timediff %.2fs' msg = line\",\n \"% (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print\",\n \"Port scan detector/logger \\\"\\\"\\\" # TCP flags to scan type mapping scan_types =\",\n \"so this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and\",\n \"and later we could get an apparent RST \\\"scan\\\" # from B to\",\n \"entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout,\",\n \"pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop =\",\n \"-= c; b -= a; b ^= (a<<16) c -= a; c -=\",\n \"[] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and\",\n \"timediff %.2fs' else: line = 'Slow %s scan (flags:%d) from %s to %s\",\n \"to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line\",\n \"timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type\",\n \"else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True #\",\n \"recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset\",\n \"key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst\",\n \"then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1\",\n \"mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST:\",\n \"this as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): #\",\n \"\\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value = addr h = 0 while\",\n \"it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also.\",\n \"b; c ^= (b>>13) a -= b; a -= c; a ^= (c>>12)\",\n \"'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP\",\n \"Better logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys, os import\",\n \"b ^= (a<<8) c -= a; c -= b; c ^= (b>>13) a\",\n \"= pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening\",\n \">>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self): # If dameon, then\",\n \"= item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True\",\n \"# Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1:\",\n \"be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host\",\n \"if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add\",\n \"scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO'\",\n \"host # X to host Z. Then actually Y is idle scanning #\",\n \"scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags if\",\n \"# Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will\",\n \"%s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon:\",\n \"e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0:\",\n \"sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid))\",\n \"unsure: line = 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s),\",\n \"host # B - zombie host # C - target host # If\",\n \"as to not call duplicate scans # in the same time-period. 'n' is\",\n \"del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan)\",\n \"c -= a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self,\",\n \"scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See\",\n \"flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan\",\n \"%s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from\",\n \"pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter)\",\n \"b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix\",\n \"lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan\",\n \"B->A for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src ==\",\n \"'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line =\",\n \"a ^= (c>>3) b -= c; b -= a; b ^= (a<<10) c\",\n \"print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid()\",\n \"to publish to google. Apr 8 2010 - Better detection of TCP full-connect\",\n \"time out in 60 seconds, max size is equal # to maximum such\",\n \"else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip =\",\n \"scans # in the same time-period. 'n' is 60 sec by default. #\",\n \"prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2,\",\n \"print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2\",\n \"scan (flags: %d) from %s to %s (ports: %s) using zombie host %s'\",\n \"packets in reverse direction or invalid protocol return timediff = curr - scan.timestamp\",\n \"not call duplicate scans # in the same time-period. 'n' is 60 sec\",\n \"= \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010'\",\n \"if B is syn scanning it # and later we could get an\",\n \"'\\\\n%d packets received by filter' % nrecv print '%d packets dropped by kernel'\",\n \"Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan\",\n \"(timediff > self.timeout): # Add entry in long_scans if timediff not larger #\",\n \"-= a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src,\",\n \"','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if\",\n \"rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan =\",\n \"= [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = '%s\",\n \"not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0\",\n \"[scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags to\",\n \"for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop,\",\n \"c -= b; c ^= (b>>13) a -= b; a -= c; a\",\n \"curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto\",\n \"proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |=\",\n \"get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x))\",\n \"self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self,\",\n \"print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc:\",\n \"%s => %s\\\" % (logfile, strerror) self.scanlog = None # Recent scans -\",\n \"curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan =\",\n \"UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x:\",\n \"else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('',\",\n \"port-scans also. Modification History Mar 17 2010 - Cleaned up code to publish\",\n \"= rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans:\",\n \"TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent\",\n \"large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else:\",\n \"if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1:\",\n \"value h ^= value value = value >> 9 return h & (8192-1)\",\n \"%s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from\",\n \"from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True break # If this\",\n \"is equal # to maximum such entries possible in 60 sec - assuming\",\n \"ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s\",\n \"coding: utf-8 #!/usr/bin/env python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd\",\n \"a new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else:\",\n \"# Correlation: If 'RST scan' detected from X to Y # See if\",\n \"rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d\",\n \"Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan\",\n \"zombie host # C - target host # If A does an idle\",\n \"a; c -= b; c ^= (b>>5) a -= b; a -= c;\",\n \"'[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not\",\n \"self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too large,\",\n \"\\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self,\",\n \"self.loop() def run(self): # If dameon, then create a new thread and wait\",\n \"self.inspect_scan(lscan, True): # Not a scan, check # of entries - if too\",\n \"scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return # Add\",\n \"daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs\",\n \"scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features 1. Detects\",\n \"# Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight <\",\n \"not_scan = True break # If this is a syn scan, see if\",\n \"try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to\",\n \"dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan = item[1] if\",\n \"A -scanning host # B - zombie host # C - target host\",\n \"else skip and remove entry if (timediff > self.timeout): # Add entry in\",\n \"entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__\",\n \"except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets\",\n \"file %s => %s\\\" % (logfile, strerror) self.scanlog = None # Recent scans\",\n \"scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation,\",\n \"sys.stderr, \\\"Error opening scan log file %s => %s\\\" % (logfile, strerror) self.scanlog\",\n \"b ^= (a<<16) c -= a; c -= b; c ^= (b>>5) a\",\n \"# C - target host # If A does an idle scan on\",\n \"sec - assuming # a scan occurs at most every 5 seconds, this\",\n \"SD is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd\",\n \"in the same time-period. 'n' is 60 sec by default. # Since entries\",\n \"recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from\",\n \"2010 - Cleaned up code to publish to google. Apr 8 2010 -\",\n \"wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground\",\n \"2010 - Better detection of TCP full-connect scan without spurious and incorrect logging.\",\n \"(ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s\",\n \"line = 'Continuation of %s scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie))\",\n \"return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\"\",\n \"scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If\",\n \"with this as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans):\",\n \"Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5\",\n \"save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile)\",\n \"or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan\",\n \"dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type\",\n \"value = addr h = 0 while value: # print value h ^=\",\n \"direction or invalid protocol return timediff = curr - scan.timestamp # Update only\",\n \"flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP\",\n \"= threshold # Timeout for scan entries self.timeout = timeout # Long-period scan\",\n \"detection of TCP full-connect scan without spurious and incorrect logging. Better logging functions.\",\n \"- this list allows to keep scan information # upto last 'n' seconds,\",\n \"\\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added\",\n \"if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1)\",\n \"if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst)\",\n \"tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from %s to\",\n \"msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr,\",\n \"run(self): # If dameon, then create a new thread and wait for it\",\n \"for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del\",\n \"self.scans[scan.hash] return True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'):\",\n \"to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s\",\n \"= '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line =\",\n \"scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if\",\n \"remove entry if (timediff > self.timeout): # Add entry in long_scans if timediff\",\n \"b -= c; b -= a; b ^= (a<<8) c -= a; c\",\n \"host address \\\"\\\"\\\" value = addr h = 0 while value: # print\",\n \"(scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP'\",\n \"not slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from %s\",\n \"host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect\",\n \"\\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid =\",\n \"logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt,\",\n \"be saved to %s' % logfile except (IOError, OSError), (errno, strerror): print >>\",\n \"(ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan,\",\n \"os.geteuid() != 0: sys.exit(\\\"You must be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\",\",\n \"TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', #\",\n \"run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except\",\n \"len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1:\",\n \"# Daemonize ? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a')\",\n \"-= b; c ^= (b>>13) a -= b; a -= c; a ^=\",\n \"ndrop, nifdrop = pc.stats() print '\\\\n%d packets received by filter' % nrecv print\",\n \"%s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if unsure:\",\n \"# but not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src,\",\n \"scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags to scan\",\n \"= False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan =\",\n \"OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second\",\n \"else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s (ports:\",\n \"scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST, however\",\n \"if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del\",\n \"# Basically # A -scanning host # B - zombie host # C\",\n \"self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def\",\n \"except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self):\",\n \"action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args\",\n \"3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan\",\n \"= type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time()\",\n \"inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l)\",\n \"scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports #\",\n \"!= 'Idle': line = '%s scan (flags:%d) from %s to %s (ports:%s)' else:\",\n \"and/or log file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog:\",\n \"not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else:\",\n \"logs to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run()\",\n \"in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto\",\n \"__version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP\",\n \"timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu\",\n \"regular network activity # but not a scan, so remove entry if len(lscan.timediffs)>=10:\",\n \"(b>>5) a -= b; a -= c; a ^= (c>>3) b -= c;\",\n \"timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan =\",\n \"len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or\",\n \"\\\"\\\"\\\" Log the scan to file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan)\",\n \"? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print >>\",\n \"dpkt, pcap import struct import socket import time import threading import optparse import\",\n \"22 from A->B, make sure that # there was no recent full-connect scan\",\n \"tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line =\",\n \"def run(self): # If dameon, then create a new thread and wait for\",\n \"(pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon:\",\n \"__author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr\",\n \"in long_scans if timediff not larger # than longscan timeout prev = self.scans[key].timestamp\",\n \"update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return #\",\n \"scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight\",\n \"continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst ==\",\n \"b -= c; b -= a; b ^= (a<<16) c -= a; c\",\n \"+= 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan\",\n \"else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if\",\n \"Y is idle scanning # Z dummy_scans, idle_ports = [], [] for item\",\n \"= 'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP:\",\n \"X to host Z. Then actually Y is idle scanning # Z dummy_scans,\",\n \"in 60 sec - assuming # a scan occurs at most every 5\",\n \"!= src: # Skip packets in reverse direction or invalid protocol return timediff\",\n \"None # Recent scans - this list allows to keep scan information #\",\n \"recent idle scan # with this as zombie, then ignore it... elif scan.flags_or\",\n \"= open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s' %\",\n \"of slow %s scan from %s to %s (ports:%s), average timediff %.2fs' msg\",\n \"will be saved to %s' % logfile except (IOError, OSError), (errno, strerror): print\",\n \"[], [] for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN\",\n \"line = 'Continuation of %s scan from %s to %s (ports: %s) using\",\n \"host when a scanning host is doing an idle scan. # Basically #\",\n \"(recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type =\",\n \"scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup =\",\n \"to\\\", default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if\",\n \"by filter' % nrecv print '%d packets dropped by kernel' % ndrop def\",\n \"print >> sys.stderr, \\\"Error opening scan log file %s => %s\\\" % (logfile,\",\n \"Modification History Mar 17 2010 - Cleaned up code to publish to google.\",\n \"if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e\",\n \"- Cleaned up code to publish to google. Apr 8 2010 - Better\",\n \"not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and\",\n \"\\\"\\\"\\\" import sys, os import dpkt, pcap import struct import socket import time\",\n \"line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff\",\n \"packets received by filter' % nrecv print '%d packets dropped by kernel' %\",\n \"^= (c>>3) b -= c; b -= a; b ^= (a<<10) c -=\",\n \"-= b; a -= c; a ^= (c>>3) b -= c; b -=\",\n \"e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork\",\n \"e sys.exit(1) self.loop() def run(self): # If dameon, then create a new thread\",\n \"x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan\",\n \"of entries - if too many # then this is a regular network\",\n \"# host when a scanning host is doing an idle scan. # Basically\",\n \"TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan\",\n \"# See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if\",\n \"lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than 2, possible\",\n \"# Was not a scan, skip if not recent.is_scan: continue if recent.type ==\",\n \"under GNU GPL v3.0. \\\"\\\"\\\" import sys, os import dpkt, pcap import struct\",\n \"to A # Correlation: If 'RST scan' detected from X to Y #\",\n \"ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP,\",\n \"UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP\",\n \"-= c; b -= a; b ^= (a<<10) c -= a; c -=\",\n \"recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie\",\n \"= pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP,\",\n \"c; a ^= (c>>13) b -= c; b -= a; b ^= (a<<8)\",\n \"= pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc =\",\n \"key in self.scans: scan = self.scans[key] if scan.src != src: # Skip packets\",\n \"((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) #\",\n \"'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP\",\n \"TCP full-connect scan without spurious and incorrect logging. Better logging functions. Licensed under\",\n \"2, possible slow scan # update port weights... # print 'Weight=>',lscan.weight if not\",\n \"- scan.timestamp # Update only if not too old, else skip and remove\",\n \"pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP):\",\n \"[pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt\",\n \"full-connect scan from B->A, if # so this is spurious and should be\",\n \"when a scanning host is doing an idle scan. # Basically # A\",\n \"except (IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening scan log file\",\n \"slow_scan: if scan.type != 'Idle': line = '%s scan (flags:%d) from %s to\",\n \"If dameon, then create a new thread and wait for it if self.daemon:\",\n \"True break # If this is a syn scan, see if there was\",\n \"constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp\",\n \">= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan\",\n \"weight for port if dport < 1024: scan.weight += 3 else: scan.weight +=\",\n \"in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host\",\n \"not too old, else skip and remove entry if (timediff > self.timeout): #\",\n \"zombie host to the scanning # host when a scanning host is doing\",\n \"\\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11\",\n \"< 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan,\",\n \"without spurious and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0.\",\n \"and logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow\",\n \"create a new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon()\",\n \"detected from X to Y # See if there was a SYN scan\",\n \"host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s\",\n \"__maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST\",\n \"apparent RST \\\"scan\\\" # from B to A # Correlation: If 'RST scan'\",\n \"1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False):\",\n \"options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ ==\",\n \"dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation:\",\n \"= lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr\",\n \"port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan:\",\n \"import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1'\",\n \"= None # Recent scans - this list allows to keep scan information\",\n \"# Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You must\",\n \"lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr\",\n \"remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr\",\n \"\\\"\\\"\\\" # TCP flags to scan type mapping scan_types = {0: 'TCP null',\",\n \"'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: #\",\n \"lscan.time_sd<2: # SD is less than 2, possible slow scan # update port\",\n \"Basically # A -scanning host # B - zombie host # C -\",\n \"TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda\",\n \"timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port\",\n \"self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout\",\n \"if scan.type != 'Idle': line = '%s scan (flags:%d) from %s to %s\",\n \"slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects\",\n \"scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto\",\n \"SD is less than 2, possible slow scan # update port weights... #\",\n \"print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key\",\n \"-= b; a -= c; a ^= (c>>13) b -= c; b -=\",\n \"-= a; c -= b; c ^= (b>>5) a -= b; a -=\",\n \"Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be\",\n \"item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp -\",\n \"ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0:\",\n \"on C with B as zombie, # it will appear to C as\",\n \"% tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport\",\n \"return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: #\",\n \"syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas',\",\n \"to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn scan,\",\n \"entry if (timediff > self.timeout): # Add entry in long_scans if timediff not\",\n \"return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto\",\n \"import time import threading import optparse import entry import timerlist __author__ = \\\"pythonhacker\\\"\",\n \"'Scan logs will be saved to %s' % logfile except (IOError, OSError), (errno,\",\n \"else: del self.scans[scan.hash] return True else: return False def process(self, pkt): if not\",\n \"if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' %\",\n \"ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags key =\",\n \"# print value h ^= value value = value >> 9 return h\",\n \"Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp\",\n \"dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src\",\n \"from A->B, make sure that # there was no recent full-connect scan from\",\n \"scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry scan\",\n \"host is doing an idle scan. # Basically # A -scanning host #\",\n \"and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\"\",\n \"scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log\",\n \"try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr,\",\n \"fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas',\",\n \"= entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |=\",\n \"TCP flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP\",\n \"(slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if\",\n \"with added ability to log slow port-scans. Features 1. Detects all stealth (half-open)\",\n \"= \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8\",\n \"If 'RST scan' detected from X to Y # See if there was\",\n \"# TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP\",\n \"fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a\",\n \"not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle':\",\n \"del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False def process(self, pkt):\",\n \"maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan =\",\n \"+= 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure\",\n \"Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle'\",\n \"the scan to file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports =\",\n \"Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You must be\",\n \"timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags):\",\n \"self.scanlog = open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s'\",\n \"c; b -= a; b ^= (a<<16) c -= a; c -= b;\",\n \"it # and later we could get an apparent RST \\\"scan\\\" # from\",\n \"is 60 sec by default. # Since entries time out in 60 seconds,\",\n \"a SYN scan recently from host # X to host Z. Then actually\",\n \">= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and\",\n \"% (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not\",\n \"line = '%s scan (flags: %d) from %s to %s (ports: %s) using\",\n \"same time-period. 'n' is 60 sec by default. # Since entries time out\",\n \"COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan)\",\n \"entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout for scan\",\n \"was a recent idle scan # with this as zombie, then ignore it...\",\n \"if (timediff > self.timeout): # Add entry in long_scans if timediff not larger\",\n \"o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save\",\n \"a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans =\",\n \"the scanning # host when a scanning host is doing an idle scan.\",\n \"TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans\",\n \"B is syn scanning it # and later we could get an apparent\",\n \"or invalid protocol return timediff = curr - scan.timestamp # Update only if\",\n \"Idle scan and logs it correctly using correlation! 3. Detects SCTP scan. 4.\",\n \"curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] =\",\n \"threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan\",\n \"scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif\",\n \"12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host address\",\n \"c ^= (b>>5) a -= b; a -= c; a ^= (c>>3) b\",\n \"scan flags 22 from A->B, make sure that # there was no recent\",\n \"= 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan,\",\n \"60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value = addr\",\n \"# and later we could get an apparent RST \\\"scan\\\" # from B\",\n \"and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src =\",\n \"%s) using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow\",\n \"scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d)\",\n \"full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and\",\n \"^= (c>>13) b -= c; b -= a; b ^= (a<<8) c -=\",\n \"scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan\",\n \"scan.ports: return # Add weight for port if dport < 1024: scan.weight +=\",\n \"- prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less than\",\n \"Y # See if there was a SYN scan recently from host #\",\n \"'n' is 60 sec by default. # Since entries time out in 60\",\n \"# See if there was a SYN scan recently from host # X\",\n \"and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP\",\n \"return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan =\",\n \"Add entry in long_scans if timediff not larger # than longscan timeout prev\",\n \"= ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold))\",\n \"lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp =\",\n \"scanning it # and later we could get an apparent RST \\\"scan\\\" #\",\n \"program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to\",\n \"average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if\",\n \"OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening scan log file %s =>\",\n \"= scan_ip2quad(scan) ports = ','.join([str(port) for port in scan.ports]) if not continuation: tup\",\n \"scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10:\",\n \"__modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress...\",\n \"= src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |=\",\n \"key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in\",\n \"# If dameon, then create a new thread and wait for it if\",\n \"# Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else:\",\n \"TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d\",\n \"scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP\",\n \"if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan,\",\n \"Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e:\",\n \"= True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie\",\n \"# X to host Z. Then actually Y is idle scanning # Z\",\n \"timediff = curr - scan.timestamp # Update only if not too old, else\",\n \"scan, see if there was a recent idle scan # with this as\",\n \"remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a\",\n \"(b>>13) a -= b; a -= c; a ^= (c>>12) b -= c;\",\n \"scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and ((scan.src\",\n \"scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan =\",\n \"C as if B is syn scanning it # and later we could\",\n \"TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP\",\n \"will appear to C as if B is syn scanning it # and\",\n \"idle scan on C with B as zombie, # it will appear to\",\n \"host Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports =\",\n \"PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols\",\n \"|= flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key]\",\n \"if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets\",\n \"host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst,\",\n \"if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if\",\n \"socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port\",\n \"= self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip if\",\n \"scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does\",\n \"0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type =\",\n \"to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\",\",\n \"scanning # Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan\",\n \"scan from B->A, if # so this is spurious and should be ignored.\",\n \"then create a new thread and wait for it if self.daemon: print 'Daemonizing...'\",\n \"if dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def\",\n \"(ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow\",\n \"google. Apr 8 2010 - Better detection of TCP full-connect scan without spurious\",\n \"scan weight threshold self.threshold = threshold # Timeout for scan entries self.timeout =\",\n \"a scan occurs at most every 5 seconds, this would be 12. self.recent_scans\",\n \"be # return packets from a zombie host to the scanning # host\",\n \"a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print\",\n \"keep scan information # upto last 'n' seconds, so as to not call\",\n \"b -= a; b ^= (a<<8) c -= a; c -= b; c\",\n \"A # Correlation: If 'RST scan' detected from X to Y # See\",\n \"self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan\",\n \"# Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key]\",\n \"curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2:\",\n \"scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst\",\n \"# Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst = dst\",\n \"does scan using RST, however this could be # return packets from a\",\n \"not a scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect'\",\n \"scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type\",\n \"self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst\",\n \"try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork\",\n \"strerror) self.scanlog = None # Recent scans - this list allows to keep\",\n \"port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan,\",\n \"else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan (flags:%d) from %s\",\n \"'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP\",\n \"self.scans: scan = self.scans[key] if scan.src != src: # Skip packets in reverse\",\n \"slow scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True):\",\n \"scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger\",\n \"log(self, msg): \\\"\\\"\\\" Log a message to console and/or log file \\\"\\\"\\\" line\",\n \"import socket import time import threading import optparse import entry import timerlist __author__\",\n \"Timeout for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l =\",\n \"unsure=False): \\\"\\\"\\\" Log the scan to file and/or console \\\"\\\"\\\" srcip, dstip =\",\n \"file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line +\",\n \"elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently\",\n \"dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or |= flags lscan.ports.append(dport) lscan.proto =\",\n \"'%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s\",\n \"if os.geteuid() != 0: sys.exit(\\\"You must be super-user to run this program\\\") o=optparse.OptionParser()\",\n \"Reset flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type\",\n \"a; c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst):\",\n \"in reverse direction or invalid protocol return timediff = curr - scan.timestamp #\",\n \"from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A\",\n \"timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize\",\n \"full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'}\",\n \"-= a; b ^= (a<<16) c -= a; c -= b; c ^=\",\n \"entries possible in 60 sec - assuming # a scan occurs at most\",\n \"this is a syn scan, see if there was a recent idle scan\",\n \"value = value >> 9 return h & (8192-1) def mix(self, a, b,\",\n \"publish to google. Apr 8 2010 - Better detection of TCP full-connect scan\",\n \"there was no recent full-connect scan from B->A, if # so this is\",\n \"RST, however this could be # return packets from a zombie host to\",\n \"timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp =\",\n \"1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie:\",\n \"print '%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from\",\n \"for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst)\",\n \"= self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src\",\n \"c): a -= b; a -= c; a ^= (c>>13) b -= c;\",\n \"nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets received by filter' % nrecv\",\n \"in self.scans: scan = self.scans[key] if scan.src != src: # Skip packets in\",\n \"(flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags:\",\n \"entries - if too many # then this is a regular network activity\",\n \"self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan =\",\n \"self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight\",\n \"ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags to scan type mapping\",\n \"break # If this is a syn scan, see if there was a\",\n \"from host # X to host Z. Then actually Y is idle scanning\",\n \"every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self,\",\n \"# return packets from a zombie host to the scanning # host when\",\n \"decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s:\",\n \"was a SYN scan recently from host # X to host Z. Then\",\n \"a -= b; a -= c; a ^= (c>>3) b -= c; b\",\n \"# than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in\",\n \"dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\")\",\n \"but not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs,\",\n \"kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork()\",\n \"scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove\",\n \"Detects slow port-scans also. Modification History Mar 17 2010 - Cleaned up code\",\n \"of %s scan from %s to %s (ports: %s) using zombie host %s'\",\n \"SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I',\",\n \"= 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff\",\n \"in recent1: # Was not a scan, skip if not recent.is_scan: continue if\",\n \"from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print\",\n \"scan information # upto last 'n' seconds, so as to not call duplicate\",\n \"line = '%s scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line\",\n \"entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period\",\n \"# Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else:\",\n \"for item in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp\",\n \"return # Add weight for port if dport < 1024: scan.weight += 3\",\n \"} [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts,\",\n \"pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv,\",\n \"and len(self.recent_scans): # Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in\",\n \"not self.inspect_scan(lscan, True): # Not a scan, check # of entries - if\",\n \"== TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old\",\n \"self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid() != 0:\",\n \"self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry\",\n \"' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B\",\n \"TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp =\",\n \"foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You must be super-user to\",\n \"lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev) lscan.flags_or\",\n \"= lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\"\",\n \"25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN #\",\n \"len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged\",\n \"'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not\",\n \"threading import optparse import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\"\",\n \"proto = type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst)\",\n \"self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if\",\n \"get an apparent RST \\\"scan\\\" # from B to A # Correlation: If\",\n \"default. # Since entries time out in 60 seconds, max size is equal\",\n \"code to publish to google. Apr 8 2010 - Better detection of TCP\",\n \"b; c ^= (b>>5) a -= b; a -= c; a ^= (c>>3)\",\n \"entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src != src:\",\n \">> 9 return h & (8192-1) def mix(self, a, b, c): a -=\",\n \"= scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet\",\n \"scans. 2. Detects Idle scan and logs it correctly using correlation! 3. Detects\",\n \"%s to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg)\",\n \"break # Reply from B->A for full-connect scan from A->B elif (recent.type ==\",\n \"'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect'\",\n \"time import threading import optparse import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__\",\n \"Log the scan to file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports\",\n \"== recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan\",\n \"self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports:\",\n \"two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log\",\n \"(scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True\",\n \">>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid\",\n \"only if not too old, else skip and remove entry if (timediff >\",\n \"maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight\",\n \"pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap()\",\n \"8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD\",\n \"using zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s\",\n \"(TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto =\",\n \"-= b; c ^= (b>>5) a -= b; a -= c; a ^=\",\n \"self.scanlog = None # Recent scans - this list allows to keep scan\",\n \"' to '.join(scan_ip2quad(scan))) not_scan = True break # If this is a syn\",\n \"# for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan:\",\n \"appear to C as if B is syn scanning it # and later\",\n \"sure that # there was no recent full-connect scan from B->A, if #\",\n \"2 entries, but SD is too large, # delete the entry # print\",\n \">> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan\",\n \"# Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError,\",\n \"else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle':\",\n \"(flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type,\",\n \"help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options,\",\n \"log file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line\",\n \"= self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True # If we\",\n \"TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def\",\n \"maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None\",\n \"lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object):\",\n \"scan' detected from X to Y # See if there was a SYN\",\n \"Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload\",\n \"= line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags\",\n \"Not a scan, check # of entries - if too many # then\",\n \"a syn scan, see if there was a recent idle scan # with\",\n \"h = 0 while value: # print value h ^= value value =\",\n \"pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1\",\n \"as zombie, # it will appear to C as if B is syn\",\n \"c; b -= a; b ^= (a<<10) c -= a; c -= b;\",\n \"Better detection of TCP full-connect scan without spurious and incorrect logging. Better logging\",\n \"and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a\",\n \"Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return\",\n \"so as to not call duplicate scans # in the same time-period. 'n'\",\n \"= lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class\",\n \"A does an idle scan on C with B as zombie, # it\",\n \"% ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from\",\n \"# None does scan using RST, however this could be # return packets\",\n \"'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt))\",\n \"%.2fs' else: line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s),\",\n \"- zombie host # C - target host # If A does an\",\n \": time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda\",\n \"scan without spurious and incorrect logging. Better logging functions. Licensed under GNU GPL\",\n \"non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data\",\n \"and remove entry if (timediff > self.timeout): # Add entry in long_scans if\",\n \"WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN\",\n \"# in the same time-period. 'n' is 60 sec by default. # Since\",\n \"else: # Run in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You\",\n \"True else: return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip\",\n \"there was a SYN scan recently from host # X to host Z.\",\n \"not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp\",\n \"port-scans. Features 1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle\",\n \"%s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line\",\n \"scan.flags_or |= flags if dport in scan.ports: return # Add weight for port\",\n \"utf-8 #!/usr/bin/env python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd}\",\n \"= entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold # Timeout for\",\n \"self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try: self.scanlog =\",\n \"tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line\",\n \"# Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged:\",\n \"c; a ^= (c>>3) b -= c; b -= a; b ^= (a<<10)\",\n \"with B as zombie, # it will appear to C as if B\",\n \"that # there was no recent full-connect scan from B->A, if # so\",\n \"to C as if B is syn scanning it # and later we\",\n \"default=\\\"/var/log/scanlog\\\") options, args = o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__\",\n \"scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from\",\n \"src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags =\",\n \"if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST,\",\n \"of TCP full-connect scan without spurious and incorrect logging. Better logging functions. Licensed\",\n \"if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)):\",\n \"^= (a<<16) c -= a; c -= b; c ^= (b>>5) a -=\",\n \"c -= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\"\",\n \"# Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError,\",\n \"timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value =\",\n \"1. Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan and\",\n \"SCTP scan. 4. Detects slow port-scans also. Modification History Mar 17 2010 -\",\n \"else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if\",\n \"hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data)\",\n \"entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return True else: return False\",\n \"to file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for\",\n \"full-connect scan without spurious and incorrect logging. Better logging functions. Licensed under GNU\",\n \"if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst =\",\n \"#1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) # Second fork try: pid = os.fork()\",\n \"= self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try: self.scanlog\",\n \"= dst scan.timestamp = curr scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type\",\n \"sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to\",\n \"def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file and/or\",\n \"it will appear to C as if B is syn scanning it #\",\n \"scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan)))\",\n \"-= a; b ^= (a<<8) c -= a; c -= b; c ^=\",\n \"3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon =\",\n \"def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >=\",\n \"(c>>12) b -= c; b -= a; b ^= (a<<16) c -= a;\",\n \"scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible scan\",\n \"return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message to console\",\n \"from %s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip,\",\n \"return False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip\",\n \"log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file and/or console\",\n \"# Recent scans - this list allows to keep scan information # upto\",\n \"def log(self, msg): \\\"\\\"\\\" Log a message to console and/or log file \\\"\\\"\\\"\",\n \"in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan)\",\n \"5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr):\",\n \"filter' % nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self):\",\n \"if not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan\",\n \"not_scan = False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan\",\n \"TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())\",\n \"scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize)\",\n \"# A -scanning host # B - zombie host # C - target\",\n \"for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from\",\n \"key del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan,\",\n \"slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan from %s\",\n \"tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in\",\n \"syn scan, see if there was a recent idle scan # with this\",\n \"information # upto last 'n' seconds, so as to not call duplicate scans\",\n \"= True break # If this is a syn scan, see if there\",\n \"was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans:\",\n \"'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize,\",\n \"then this is a regular network activity # but not a scan, so\",\n \"del self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport,\",\n \"from B->A, if # so this is spurious and should be ignored. if\",\n \"console and/or log file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(), msg) if\",\n \"else: # Add new entry scan = entry.ScanEntry(key) scan.src = src scan.dst =\",\n \"- assuming # a scan occurs at most every 5 seconds, this would\",\n \"IST 2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25\",\n \"and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged =\",\n \"host # If A does an idle scan on C with B as\",\n \"= False if scan.flags_or==TH_RST: # None does scan using RST, however this could\",\n \"TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST:\",\n \"%s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to\",\n \"= '[%s]: %s' % (get_timestamp(), msg) if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if\",\n \"if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item)\",\n \"flags) self.inspect_scan(scan) else: # Add new entry scan = entry.ScanEntry(key) scan.src = src\",\n \"^= (a<<8) c -= a; c -= b; c ^= (b>>13) a -=\",\n \"flags lscan.ports.append(dport) lscan.proto = proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp\",\n \"and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and len(scan.ports)>=3 and\",\n \"= entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True\",\n \"while value: # print value h ^= value value = value >> 9\",\n \"%s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan\",\n \"in reversed(self.recent_scans): rscan = item[1] if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30):\",\n \"from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) if\",\n \"!= 'Idle': line = 'Continuation of %s scan from %s to %s (ports:%s)'\",\n \"from B->A for full-connect scan from A->B elif (recent.type == 'reply' and ((scan.src\",\n \"= pload.flags key = self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if\",\n \"spurious TCP full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan))) not_scan = True\",\n \"all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout,\",\n \"-= b; a -= c; a ^= (c>>12) b -= c; b -=\",\n \"occurs at most every 5 seconds, this would be 12. self.recent_scans = timerlist.TimerList(12,\",\n \"C with B as zombie, # it will appear to C as if\",\n \"Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans\",\n \"# with this as zombie, then ignore it... elif scan.flags_or == TH_SYN and\",\n \"by kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid =\",\n \"-= c; a ^= (c>>3) b -= c; b -= a; b ^=\",\n \"a ^= (c>>12) b -= c; b -= a; b ^= (a<<16) c\",\n \"scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or = 0 elif\",\n \"last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and\",\n \"hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value = addr h =\",\n \"'\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False,\",\n \"self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan\",\n \"is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1\",\n \"- if too many # then this is a regular network activity #\",\n \"return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else: # Add new entry\",\n \"(errno, strerror): print >> sys.stderr, \\\"Error opening scan log file %s => %s\\\"\",\n \"o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs\",\n \"elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan scan.flags_or =\",\n \"# it will appear to C as if B is syn scanning it\",\n \"'Thu Apr 8 19:21:11 IST 2010' # UDP - in progress... SCAN_TIMEOUT =\",\n \"to keep scan information # upto last 'n' seconds, so as to not\",\n \"idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src\",\n \"(IOError, OSError), (errno, strerror): print >> sys.stderr, \\\"Error opening scan log file %s\",\n \"self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log file try:\",\n \"True): # Not a scan, check # of entries - if too many\",\n \"\\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\",\",\n \"del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del self.scans[key]\",\n \"slow %s scan from %s to %s (ports:%s), average timediff %.2fs' msg =\",\n \"lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: #\",\n \"by default. # Since entries time out in 60 seconds, max size is\",\n \"for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop()\",\n \"to maximum such entries possible in 60 sec - assuming # a scan\",\n \"- Better detection of TCP full-connect scan without spurious and incorrect logging. Better\",\n \"full-connect', # Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True,\",\n \"pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name, pc.filter) for\",\n \"(a<<16) c -= a; c -= b; c ^= (b>>5) a -= b;\",\n \"'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP\",\n \"ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack',\",\n \"if idle_scan: scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type =\",\n \"self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key) lscan.src =\",\n \"= curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if\",\n \"+ '\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan,\",\n \"scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else:\",\n \"an idle scan. # Basically # A -scanning host # B - zombie\",\n \"TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S',\",\n \"2010' # UDP - in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\"\",\n \"sec by default. # Since entries time out in 60 seconds, max size\",\n \"to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s\",\n \"an idle scan on C with B as zombie, # it will appear\",\n \"new thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: #\",\n \"= 'Continuation of %s scan from %s to %s (ports: %s) using zombie\",\n \"idle_scan = False if scan.flags_or==TH_RST: # None does scan using RST, however this\",\n \"elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans recent1 =\",\n \"from a zombie host to the scanning # host when a scanning host\",\n \"# Add entry in long_scans if timediff not larger # than longscan timeout\",\n \"TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN:\",\n \"self.scanlog.flush() if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False,\",\n \"= o.parse_args() s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile) s.run() if __name__ == '__main__': main()\",\n \"a message to console and/or log file \\\"\\\"\\\" line = '[%s]: %s' %\",\n \"a, b, c): a -= b; a -= c; a ^= (c>>13) b\",\n \"assuming # a scan occurs at most every 5 seconds, this would be\",\n \"Mar 17 2010 - Cleaned up code to publish to google. Apr 8\",\n \"recent1: # Was not a scan, skip if not recent.is_scan: continue if recent.type\",\n \"from %s to %s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line\",\n \"as if B is syn scanning it # and later we could get\",\n \"a; b ^= (a<<10) c -= a; c -= b; c ^= (b>>15)\",\n \"to Y # See if there was a SYN scan recently from host\",\n \"in self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp =\",\n \"os import dpkt, pcap import struct import socket import time import threading import\",\n \"not slow_scan: if scan.type != 'Idle': line = 'Continuation of %s scan from\",\n \"b -= c; b -= a; b ^= (a<<10) c -= a; c\",\n \"and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l)) not_scan = False if is_scan or maybe_scan:\",\n \"%s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from\",\n \"False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False\",\n \"flags) if lscan.time_sd<2: # SD is less than 2, possible slow scan #\",\n \"line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file\",\n \"type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn',\",\n \"c; a ^= (c>>12) b -= c; b -= a; b ^= (a<<16)\",\n \"sys, os import dpkt, pcap import struct import socket import time import threading\",\n \"later we could get an apparent RST \\\"scan\\\" # from B to A\",\n \"== 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious\",\n \"and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to\",\n \"scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring\",\n \"if lscan.time_sd<2: # SD is less than 2, possible slow scan # update\",\n \"we see scan flags 22 from A->B, make sure that # there was\",\n \"size is equal # to maximum such entries possible in 60 sec -\",\n \"def mix(self, a, b, c): a -= b; a -= c; a ^=\",\n \"h ^= value value = value >> 9 return h & (8192-1) def\",\n \"zombie host %s' else: tup.append(scan.time_avg) if unsure: line = 'Possible slow %s scan\",\n \"python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with\",\n \"%s\\\" % (logfile, strerror) self.scanlog = None # Recent scans - this list\",\n \"int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags key\",\n \"((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src = scan.dst\",\n \"full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)): # Spurious self.log(\\\"Ignoring spurious\",\n \"if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags =\",\n \"tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to %s (ports: %s)\",\n \"scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry\",\n \"to host Z. Then actually Y is idle scanning # Z dummy_scans, idle_ports\",\n \"scan from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s\",\n \"slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file and/or console \\\"\\\"\\\" srcip, dstip\",\n \"1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): # Sure scan is_scan = ((slow_scan and\",\n \"dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def\",\n \"%s to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports]\",\n \"= timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value\",\n \"self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags) self.inspect_scan(scan) else:\",\n \"if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del\",\n \"flags): scan.flags_or |= flags if dport in scan.ports: return # Add weight for\",\n \"'RST scan' detected from X to Y # See if there was a\",\n \"continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file and/or console \\\"\\\"\\\" srcip,\",\n \"scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600 #\",\n \"if not self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False):\",\n \"self.long_scans[key] del self.scans[key] return if scan.logged: return scan.timestamp = curr self.update_ports(scan, dport, flags)\",\n \"print value h ^= value value = value >> 9 return h &\",\n \"loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try:\",\n \"'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if type(ip.data) not\",\n \"super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\",\",\n \"scan.flags_or |= flags if proto==SCTP: scan.chunk_type = pload.chunks[0].type scan.ports.append(dport) scan.proto = proto self.scans[key]\",\n \"x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags',\",\n \"%.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan: if scan.type !=\",\n \"pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print\",\n \"def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore\",\n \"|= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD\",\n \"opening scan log file %s => %s\\\" % (logfile, strerror) self.scanlog = None\",\n \"duplicate scans # in the same time-period. 'n' is 60 sec by default.\",\n \"^= (b>>13) a -= b; a -= c; a ^= (c>>12) b -=\",\n \"Update only if not too old, else skip and remove entry if (timediff\",\n \"recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted\",\n \"to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN:\",\n \"scan # with this as zombie, then ignore it... elif scan.flags_or == TH_SYN\",\n \"would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a\",\n \"recent1: if recent.type=='Idle' and scan.src==recent.zombie: self.log('Ignoring mis-interpreted syn scan from zombie host %s'\",\n \"\\\"pythonhacker\\\" __version__ = '0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' #\",\n \"too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key]\",\n \"actually Y is idle scanning # Z dummy_scans, idle_ports = [], [] for\",\n \"list allows to keep scan information # upto last 'n' seconds, so as\",\n \"# Not a scan, check # of entries - if too many #\",\n \"= 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon\",\n \"'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else:\",\n \"-= c; a ^= (c>>12) b -= c; b -= a; b ^=\",\n \"'SCTP COOKIE_ECHO' # See if this was logged recently scanentry = entry.RecentScanEntry(scan, not\",\n \"larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not\",\n \"RST \\\"scan\\\" # from B to A # Correlation: If 'RST scan' detected\",\n \"out in 60 seconds, max size is equal # to maximum such entries\",\n \"Recent scans - this list allows to keep scan information # upto last\",\n \"to %s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def\",\n \"entries time out in 60 seconds, max size is equal # to maximum\",\n \"else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to %s\",\n \"this would be 12. self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash\",\n \"long_scans if timediff not larger # than longscan timeout prev = self.scans[key].timestamp if\",\n \"pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' % (pc.name,\",\n \"TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN:\",\n \"zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan from\",\n \"curr - scan.timestamp # Update only if not too old, else skip and\",\n \"if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type !=\",\n \"# Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" % ' to '.join(scan_ip2quad(scan)))\",\n \"not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan: self.log_scan(scan, continuation=continuation, slow_scan=slow_scan,\",\n \"a -= b; a -= c; a ^= (c>>13) b -= c; b\",\n \"for recent in recent1: # Was not a scan, skip if not recent.is_scan:\",\n \"using RST, however this could be # return packets from a zombie host\",\n \"file and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port\",\n \"entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr lscan.timediffs.append(curr - prev)\",\n \"# print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove\",\n \"SD is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key]\",\n \"activity # but not a scan, so remove entry if len(lscan.timediffs)>=10: # print\",\n \"b -= a; b ^= (a<<10) c -= a; c -= b; c\",\n \"srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = 'Continuation of\",\n \"inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans. Features\",\n \"lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: # SD is less\",\n \"rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30): idle_scan = True idle_ports.append(rscan.ports) dummy_scans.append(item) if idle_scan: scan.src\",\n \"^= (a<<10) c -= a; c -= b; c ^= (b>>15) return abs(c)\",\n \"flags if dport in scan.ports: return # Add weight for port if dport\",\n \"'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP\",\n \"scan threshold self.threshold_l = self.threshold/2 # Daemonize ? self.daemon = daemon # Log\",\n \"thread and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run\",\n \"del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type\",\n \"tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr,\",\n \"idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if\",\n \"self.long_scans: lscan = entry.ScanEntry(key) lscan.src = src lscan.dst = dst lscan.timestamp = curr\",\n \"self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You must be super-user to run\",\n \"'TCP full-connect', TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags', TH_SYN|TH_ACK|TH_RST: 'TCP full-connect', # Not a scan TH_RST|TH_ACK:\",\n \"recent.src)): # Spurious self.log(\\\"Ignoring spurious TCP full-connect scan from %s\\\" % ' to\",\n \"open(logfile,'a') print >> sys.stderr, 'Scan logs will be saved to %s' % logfile\",\n \"'0.5.1' __modified__ = 'Thu Apr 8 19:21:11 IST 2010' # UDP - in\",\n \"!= 0: sys.exit(\\\"You must be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\",\",\n \"= int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags = pload.flags\",\n \"scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line =\",\n \"protocol return timediff = curr - scan.timestamp # Update only if not too\",\n \"was no recent full-connect scan from B->A, if # so this is spurious\",\n \"False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'): not_scan = True\",\n \"c; b -= a; b ^= (a<<8) c -= a; c -= b;\",\n \"scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log the scan to file and/or console \\\"\\\"\\\"\",\n \"scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l = self.threshold/2 #\",\n \"scan.type = 'SCTP COOKIE_ECHO' # See if this was logged recently scanentry =\",\n \"if not self.inspect_scan(lscan, True): # Not a scan, check # of entries -\",\n \"from B to A # Correlation: If 'RST scan' detected from X to\",\n \"'reply'): not_scan = True # If we see scan flags 22 from A->B,\",\n \"((scan.src == recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif\",\n \"9 return h & (8192-1) def mix(self, a, b, c): a -= b;\",\n \"print '\\\\n%d packets received by filter' % nrecv print '%d packets dropped by\",\n \"TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN # Protocols TCP=dpkt.tcp.TCP UDP=dpkt.udp.UDP SCTP=dpkt.sctp.SCTP get_timestamp = lambda :\",\n \"= self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan,\",\n \"detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow\",\n \"saved to %s' % logfile except (IOError, OSError), (errno, strerror): print >> sys.stderr,\",\n \"tup.append(scan.time_avg) line = 'Continuation of slow %s scan from %s to %s (ports:%s),\",\n \"from X to Y # See if there was a SYN scan recently\",\n \"host # C - target host # If A does an idle scan\",\n \"last 'n' seconds, so as to not call duplicate scans # in the\",\n \"dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message to console and/or log\",\n \"-= c; a ^= (c>>13) b -= c; b -= a; b ^=\",\n \"(not slow_scan and scan.weight >= self.threshold)) # Possible scan maybe_scan = (slow_scan and\",\n \"scan.type in ('', 'reply'): not_scan = True # If we see scan flags\",\n \"address \\\"\\\"\\\" value = addr h = 0 while value: # print value\",\n \"slow port-scans also. Modification History Mar 17 2010 - Cleaned up code to\",\n \"try: print 'listening on %s: %s' % (pc.name, pc.filter) for ts, pkt in\",\n \"self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True if scan.proto==TCP:\",\n \"value value = value >> 9 return h & (8192-1) def mix(self, a,\",\n \"failed\\\", e sys.exit(1) self.loop() def run(self): # If dameon, then create a new\",\n \"d in dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash]\",\n \"& (8192-1) def mix(self, a, b, c): a -= b; a -= c;\",\n \"and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold)) # Possible\",\n \"Hash a host address \\\"\\\"\\\" value = addr h = 0 while value:\",\n \"sys.stderr, 'Scan logs will be saved to %s' % logfile except (IOError, OSError),\",\n \"-- coding: utf-8 #!/usr/bin/env python \\\"\\\"\\\" pyscanlogger: Port scan detector/logger tool, inspired by\",\n \"scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport) def inspect_scan(self, scan, slow_scan=False): #\",\n \"detector/logger \\\"\\\"\\\" # TCP flags to scan type mapping scan_types = {0: 'TCP\",\n \"zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last\",\n \"- in progress... SCAN_TIMEOUT = 5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag\",\n \"# delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del self.long_scans[key] else: #\",\n \"nifdrop = pc.stats() print '\\\\n%d packets received by filter' % nrecv print '%d\",\n \"'TCP syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP\",\n \"= timeout # Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold\",\n \"'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main(): if os.geteuid() !=\",\n \"Keep dropping old entries self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if\",\n \"self.scans[key] if scan.src != src: # Skip packets in reverse direction or invalid\",\n \">> sys.stderr, 'Scan logs will be saved to %s' % logfile except (IOError,\",\n \"dummy_scans: # self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del\",\n \"null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn', TH_ACK: 'TCP ack',\",\n \"= 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'\",\n \"this list allows to keep scan information # upto last 'n' seconds, so\",\n \"b ^= (a<<10) c -= a; c -= b; c ^= (b>>15) return\",\n \"Detects all stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs\",\n \"is too large, # delete the entry # print 'Removing',key,lscan.src,'since SD is',lscan.time_sd del\",\n \"scan.src = scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports\",\n \"self.threshold = threshold # Timeout for scan entries self.timeout = timeout # Long-period\",\n \"this is spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans):\",\n \"to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan from A->B elif\",\n \"see scan flags 22 from A->B, make sure that # there was no\",\n \"not a scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd\",\n \"__init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'): self.scans = entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) #\",\n \"for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l = 3600\",\n \"OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self): #\",\n \"message to console and/or log file \\\"\\\"\\\" line = '[%s]: %s' % (get_timestamp(),\",\n \"pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print\",\n \"(a<<10) c -= a; c -= b; c ^= (b>>15) return abs(c) def\",\n \"SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if\",\n \"a scan, check # of entries - if too many # then this\",\n \"spurious and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 =\",\n \"packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty try:\",\n \"abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return\",\n \"scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight\",\n \"def run_daemon(self): # Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0)\",\n \"an apparent RST \\\"scan\\\" # from B to A # Correlation: If 'RST\",\n \"sys.exit(1) self.loop() def run(self): # If dameon, then create a new thread and\",\n \"spurious and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\"\",\n \"scan.dst scan.dst = rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports\",\n \"# Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan\",\n \"self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message to console and/or\",\n \"must be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\",\",\n \"os.umask(0) # Second fork try: pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except\",\n \"return timediff = curr - scan.timestamp # Update only if not too old,\",\n \"17 2010 - Cleaned up code to publish to google. Apr 8 2010\",\n \"tup.append(ip2quad(scan.zombie)) line = '%s scan (flags: %d) from %s to %s (ports: %s)\",\n \"= self.host_hash(src,dst) curr=time.time() # Keep dropping old entries self.recent_scans.collect() if key in self.scans:\",\n \"if there was a SYN scan recently from host # X to host\",\n \"than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans:\",\n \"src, dst): \\\"\\\"\\\" Hash mix two host addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff))\",\n \"# Reply from B->A for full-connect scan from A->B elif (recent.type == 'reply'\",\n \"Reply from B->A for full-connect scan from A->B elif (recent.type == 'reply' and\",\n \"non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return pload =\",\n \"8 2010 - Better detection of TCP full-connect scan without spurious and incorrect\",\n \"= 'Continuation of slow %s scan from %s to %s (ports:%s), average timediff\",\n \"self.recent_scans.collect() if key in self.scans: scan = self.scans[key] if scan.src != src: #\",\n \"(half-open) and full-connect scans. 2. Detects Idle scan and logs it correctly using\",\n \"in pc: self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats()\",\n \"C - target host # If A does an idle scan on C\",\n \"a -= b; a -= c; a ^= (c>>12) b -= c; b\",\n \"flags for UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type =\",\n \"= pc.stats() print '\\\\n%d packets received by filter' % nrecv print '%d packets\",\n \"self.timeout): # Add entry in long_scans if timediff not larger # than longscan\",\n \"update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a\",\n \"maximum such entries possible in 60 sec - assuming # a scan occurs\",\n \"added ability to log slow port-scans. Features 1. Detects all stealth (half-open) and\",\n \"except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid() os.umask(0) #\",\n \"# B - zombie host # C - target host # If A\",\n \"correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans also. Modification\",\n \"0: sys.exit(\\\"You must be super-user to run this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\",\",\n \"in foreground self.loop() def main(): if os.geteuid() != 0: sys.exit(\\\"You must be super-user\",\n \"should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for\",\n \"all stealth (half-open) and full-connect scans. 2. Detects Idle scan and logs it\",\n \"self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print >> sys.stderr,\",\n \"(ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if not slow_scan:\",\n \"a recent idle scan # with this as zombie, then ignore it... elif\",\n \"open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop()\",\n \"recent.dst) and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type\",\n \"rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in dummy_scans: #\",\n \"del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too\",\n \"a zombie host to the scanning # host when a scanning host is\",\n \"# self.recent_scans.remove(d) else: # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash]\",\n \"len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan,\",\n \"# Port scan weight threshold self.threshold = threshold # Timeout for scan entries\",\n \"allows to keep scan information # upto last 'n' seconds, so as to\",\n \"scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for d in\",\n \"= { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s'\",\n \"b; a -= c; a ^= (c>>13) b -= c; b -= a;\",\n \"elif len(lscan.timediffs)>2: # More than 2 entries, but SD is too large, #\",\n \"# Long-period scan timeouts self.timeout_l = 3600 # Long-period scan threshold self.threshold_l =\",\n \"syn scanning it # and later we could get an apparent RST \\\"scan\\\"\",\n \"%s: %s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt:\",\n \"import optparse import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ = \\\"pythonhacker\\\" __version__\",\n \"(a<<8) c -= a; c -= b; c ^= (b>>13) a -= b;\",\n \"x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\"\",\n \"many # then this is a regular network activity # but not a\",\n \"(flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow\",\n \"(b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash mix two host addresses\",\n \"= 'Idle' scan.ports = idle_ports # for d in dummy_scans: # self.recent_scans.remove(d) else:\",\n \"def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()]\",\n \"%s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for full-connect scan\",\n \"logging. Better logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import sys, os\",\n \"to %s (ports:%s), average timediff %.2fs' else: tup = [scan.type, srcip,dstip, ports] if\",\n \"and incorrect logging. Better logging functions. Licensed under GNU GPL v3.0. \\\"\\\"\\\" import\",\n \"is',lscan.time_sd del self.long_scans[key] else: # Too large timeout, remove key del self.long_scans[key] del\",\n \"dport, flags) if lscan.time_sd<2: # SD is less than 2, possible slow scan\",\n \"# Try last 1 scans recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: if\",\n \"import threading import optparse import entry import timerlist __author__ = \\\"pythonhacker\\\" __maintainer__ =\",\n \"pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on\",\n \"{ pcap.DLT_LOOP:dpkt.loopback.Loopback, pcap.DLT_NULL:dpkt.loopback.Loopback, pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()] try: print 'listening on %s: %s' %\",\n \"\\\"Error opening scan log file %s => %s\\\" % (logfile, strerror) self.scanlog =\",\n \"call duplicate scans # in the same time-period. 'n' is 60 sec by\",\n \"\\\"fork #2 failed\\\", e sys.exit(1) self.loop() def run(self): # If dameon, then create\",\n \"scan.timestamp # Update only if not too old, else skip and remove entry\",\n \"# More than 2 entries, but SD is too large, # delete the\",\n \"there was a recent idle scan # with this as zombie, then ignore\",\n \"syn', TH_ACK: 'TCP ack', TH_URG|TH_PSH|TH_FIN: 'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin',\",\n \"in scan.ports: return # Add weight for port if dport < 1024: scan.weight\",\n \"if scan.flags_or==TH_RST: # None does scan using RST, however this could be #\",\n \"# update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not\",\n \"this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in\",\n \"and/or console \\\"\\\"\\\" srcip, dstip = scan_ip2quad(scan) ports = ','.join([str(port) for port in\",\n \"# If we see scan flags 22 from A->B, make sure that #\",\n \"if dport in scan.ports: return # Add weight for port if dport <\",\n \"'%s scan (flags: %d) from %s to %s (ports: %s) using zombie host\",\n \"make sure that # there was no recent full-connect scan from B->A, if\",\n \"flags to scan type mapping scan_types = {0: 'TCP null', TH_FIN: 'TCP fin',\",\n \"[scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line = '%s scan\",\n \"and should be ignored. if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans): recent1 = self.recent_scans[-1:-2:-1]\",\n \"> self.timeout): # Add entry in long_scans if timediff not larger # than\",\n \"= value >> 9 return h & (8192-1) def mix(self, a, b, c):\",\n \"del self.scans[scan.hash] return False else: scan.type = self.scan_types.get(scan.flags_or,'unknown') if scan.type in ('', 'reply'):\",\n \"Too large timeout, remove key del self.long_scans[key] del self.scans[key] return if scan.logged: return\",\n \"= 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type\",\n \"for port if dport < 1024: scan.weight += 3 else: scan.weight += 1\",\n \"see if there was a recent idle scan # with this as zombie,\",\n \"in 60 seconds, max size is equal # to maximum such entries possible\",\n \"-= b; c ^= (b>>15) return abs(c) def host_hash(self, src, dst): \\\"\\\"\\\" Hash\",\n \"self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d packets received by filter' %\",\n \"from A->B elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst ==\",\n \"return packets from a zombie host to the scanning # host when a\",\n \"is less than 2, possible slow scan # update port weights... # print\",\n \"if self.scanlog: self.scanlog.write(line + '\\\\n') self.scanlog.flush() if not self.daemon: print >> sys.stderr, line\",\n \"\\\"\\\"\\\" value = addr h = 0 while value: # print value h\",\n \"Daemonize ? self.daemon = daemon # Log file try: self.scanlog = open(logfile,'a') print\",\n \"# Timeout for scan entries self.timeout = timeout # Long-period scan timeouts self.timeout_l\",\n \"# upto last 'n' seconds, so as to not call duplicate scans #\",\n \"pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e sys.exit(1) os.setsid()\",\n \"= {0: 'TCP null', TH_FIN: 'TCP fin', TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn',\",\n \"map(ip2quad, [scan.src, scan.dst]) class ScanLogger(object): \\\"\\\"\\\" Port scan detector/logger \\\"\\\"\\\" # TCP flags\",\n \"nrecv print '%d packets dropped by kernel' % ndrop def run_daemon(self): # Disconnect\",\n \"self.recent_scans = timerlist.TimerList(12, 60.0) def hash_func(self, addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\"\",\n \"scan (flags:%d) from %s to %s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = '%s scan\",\n \"to log slow port-scans. Features 1. Detects all stealth (half-open) and full-connect scans.\",\n \"using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of slow %s scan\",\n \"possible slow scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan,\",\n \"slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else:\",\n \"def main(): if os.geteuid() != 0: sys.exit(\\\"You must be super-user to run this\",\n \"is a syn scan, see if there was a recent idle scan #\",\n \"< self.threshold_l)) not_scan = False if is_scan or maybe_scan: scan.logged = True if\",\n \"b, c): a -= b; a -= c; a ^= (c>>13) b -=\",\n \"to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s scan (flags:%d)\",\n \"a scan, skip if not recent.is_scan: continue if recent.type == 'TCP full-connect' and\",\n \"UDP, SCTP): return pload = ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload)\",\n \"addr): \\\"\\\"\\\" Hash a host address \\\"\\\"\\\" value = addr h = 0\",\n \"lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport, flags) if lscan.time_sd<2: #\",\n \">> sys.stderr, \\\"Error opening scan log file %s => %s\\\" % (logfile, strerror)\",\n \"reverse direction or invalid protocol return timediff = curr - scan.timestamp # Update\",\n \"addresses \\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message\",\n \"scan, so remove entry if len(lscan.timediffs)>=10: # print lscan.src, lscan.timediffs, lscan.time_sd print 'Removing',key,lscan.src,'since\",\n \"continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports] if not slow_scan: if scan.type != 'Idle': line\",\n \"= rscan.dst scan.zombie = rscan.src scan.type = 'Idle' scan.ports = idle_ports # for\",\n \"%s (ports:%s)' else: tup.append(ip2quad(scan.zombie)) line = 'Continuation of %s scan from %s to\",\n \"but with added ability to log slow port-scans. Features 1. Detects all stealth\",\n \"proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback,\",\n \"'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' # See if this was\",\n \"if scan.chunk_type==1: scan.type = 'SCTP Init' elif scan.chunk_type==10: scan.type = 'SCTP COOKIE_ECHO' #\",\n \"not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not not_scan:\",\n \"scan = self.scans[key] if scan.src != src: # Skip packets in reverse direction\",\n \"lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr -\",\n \"addr h = 0 while value: # print value h ^= value value\",\n \"is doing an idle scan. # Basically # A -scanning host # B\",\n \"from %s to %s (ports:%s), average timediff %.2fs' else: line = 'Slow %s\",\n \"prev = self.scans[key].timestamp if timediff<=self.timeout_l: if key not in self.long_scans: lscan = entry.ScanEntry(key)\",\n \"= entry.EntryLog(maxsize) self.long_scans = entry.EntryLog(maxsize) # Port scan weight threshold self.threshold = threshold\",\n \"than 2 entries, but SD is too large, # delete the entry #\",\n \"type(pload) if proto == TCP: flags = pload.flags key = self.host_hash(src,dst) curr=time.time() #\",\n \"(c>>13) b -= c; b -= a; b ^= (a<<8) c -= a;\",\n \"if there was a recent idle scan # with this as zombie, then\",\n \"struct import socket import time import threading import optparse import entry import timerlist\",\n \"pid = os.fork() if pid>0: open(PIDFILE,'w').write(str(pid)) sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork\",\n \"self.long_scans[key] lscan.timestamp = curr lscan.flags_or |= flags lscan.timediffs.append(curr - prev) lscan.update_time_sd() self.update_ports(lscan, dport,\",\n \"upto last 'n' seconds, so as to not call duplicate scans # in\",\n \"(c>>3) b -= c; b -= a; b ^= (a<<10) c -= a;\",\n \"= 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST TH_SYN=dpkt.tcp.TH_SYN TH_FIN=dpkt.tcp.TH_FIN\",\n \"if too many # then this is a regular network activity # but\",\n \"SYN scan recently from host # X to host Z. Then actually Y\",\n \"= proto self.long_scans[key] = lscan else: lscan = self.long_scans[key] lscan.timestamp = curr lscan.flags_or\",\n \"mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break #\",\n \"(8192-1) def mix(self, a, b, c): a -= b; a -= c; a\",\n \"tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to log slow port-scans.\",\n \"# from B to A # Correlation: If 'RST scan' detected from X\",\n \"check # of entries - if too many # then this is a\",\n \"os.fork() if pid>0: sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #1 failed\\\", e\",\n \"\\\"scan\\\" # from B to A # Correlation: If 'RST scan' detected from\",\n \"See if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry\",\n \"Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and\",\n \"pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp\",\n \"= '%s scan (flags: %d) from %s to %s (ports: %s) using zombie\",\n \"lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x)) scan_ip2quad =\",\n \"% ndrop def run_daemon(self): # Disconnect from tty try: pid = os.fork() if\",\n \"up code to publish to google. Apr 8 2010 - Better detection of\",\n \"\\\"\\\"\\\" return self.hash_func(self.mix(src, dst, 0xffffff)) def log(self, msg): \\\"\\\"\\\" Log a message to\",\n \"History Mar 17 2010 - Cleaned up code to publish to google. Apr\",\n \"a; c -= b; c ^= (b>>13) a -= b; a -= c;\",\n \"syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply\",\n \"not hasattr(pkt, 'ip'): return ip = pkt.ip # Ignore non-tcp, non-udp packets if\",\n \"%s (ports:%s), average timediff %.2fs' msg = line % tuple(tup) self.log(msg) def update_ports(self,\",\n \"scan # update port weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): #\",\n \"dropped by kernel' % ndrop def run_daemon(self): # Disconnect from tty try: pid\",\n \"this program\\\") o=optparse.OptionParser() o.add_option(\\\"-d\\\", \\\"--daemonize\\\", dest=\\\"daemon\\\", help=\\\"Daemonize\\\", action=\\\"store_true\\\", default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File\",\n \"# Ignore non-tcp, non-udp packets if type(ip.data) not in (TCP, UDP, SCTP): return\",\n \"%s' % (pc.name, pc.filter) for ts, pkt in pc: self.process(decode(pkt)) except KeyboardInterrupt: if\",\n \"default=False) o.add_option(\\\"-f\\\", \\\"--logfile\\\", dest=\\\"logfile\\\", help=\\\"File to save logs to\\\", default=\\\"/var/log/scanlog\\\") options, args =\",\n \"invalid protocol return timediff = curr - scan.timestamp # Update only if not\",\n \"and wait for it if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in\",\n \"could get an apparent RST \\\"scan\\\" # from B to A # Correlation:\",\n \"Port scan detector/logger tool, inspired by scanlogd {http://www.openwall.com/scanlogd} but with added ability to\",\n \"Port scan weight threshold self.threshold = threshold # Timeout for scan entries self.timeout\",\n \"sys.exit(0) except OSError, e: print >>sys.stderr, \\\"fork #2 failed\\\", e sys.exit(1) self.loop() def\",\n \"Was not a scan, skip if not recent.is_scan: continue if recent.type == 'TCP\",\n \"= proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode = {\",\n \"packets if type(ip.data) not in (TCP, UDP, SCTP): return pload = ip.data src,dst,dport,flags\",\n \"logs will be saved to %s' % logfile except (IOError, OSError), (errno, strerror):\",\n \"as zombie, then ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try\",\n \"if this was logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not\",\n \"value >> 9 return h & (8192-1) def mix(self, a, b, c): a\",\n \"weights... # print 'Weight=>',lscan.weight if not self.inspect_scan(lscan, True): # Not a scan, check\",\n \"True # If we see scan flags 22 from A->B, make sure that\",\n \"False def process(self, pkt): if not hasattr(pkt, 'ip'): return ip = pkt.ip #\",\n \"if self.daemon: print 'Daemonizing...' self.run_daemon() else: # Run in foreground self.loop() def main():\",\n \"# Not a scan TH_RST|TH_ACK: 'reply'} def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'):\",\n \"value: # print value h ^= value value = value >> 9 return\",\n \"ip.data src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0 proto = type(pload) if proto == TCP: flags\",\n \"= ','.join([str(port) for port in scan.ports]) if not continuation: tup = [scan.type,scan.flags_or,srcip,dstip, ports]\",\n \"%s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs' else: tup\",\n \"Disconnect from tty try: pid = os.fork() if pid>0: sys.exit(0) except OSError, e:\",\n \"'Idle': line = 'Continuation of %s scan from %s to %s (ports:%s)' else:\",\n \"or maybe_scan: scan.logged = True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: #\",\n \"scan log file %s => %s\\\" % (logfile, strerror) self.scanlog = None #\",\n \"import sys, os import dpkt, pcap import struct import socket import time import\",\n \"# Skip packets in reverse direction or invalid protocol return timediff = curr\",\n \"return h & (8192-1) def mix(self, a, b, c): a -= b; a\",\n \"recent1 = self.recent_scans[-1:-2:-1] for recent in recent1: # Was not a scan, skip\",\n \"self.process(decode(pkt)) except KeyboardInterrupt: if not self.daemon: nrecv, ndrop, nifdrop = pc.stats() print '\\\\n%d\",\n \"slow_scan=False): # Sure scan is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not\",\n \"not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if not\",\n \"timediff not larger # than longscan timeout prev = self.scans[key].timestamp if timediff<=self.timeout_l: if\",\n \"True if scan.proto==TCP: idle_scan = False if scan.flags_or==TH_RST: # None does scan using\",\n \"line % tuple(tup) self.log(msg) def update_ports(self, scan, dport, flags): scan.flags_or |= flags if\",\n \"logs it correctly using correlation! 3. Detects SCTP scan. 4. Detects slow port-scans\",\n \"skip and remove entry if (timediff > self.timeout): # Add entry in long_scans\",\n \"entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False self.recent_scans.append(scanentry) else: continuation=True if\",\n \"ignore it... elif scan.flags_or == TH_SYN and len(self.recent_scans): # Try last 1 scans\",\n \"zombie host %s' % ' to '.join(scan_ip2quad(scan))) break # Reply from B->A for\",\n \"scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags\",\n \"entry.ScanEntry(key) scan.src = src scan.dst = dst scan.timestamp = curr scan.flags_or |= flags\",\n \"logged recently scanentry = entry.RecentScanEntry(scan, not not_scan) if scanentry not in self.recent_scans: continuation=False\",\n \"# Z dummy_scans, idle_ports = [], [] for item in reversed(self.recent_scans): rscan =\",\n \"no recent full-connect scan from B->A, if # so this is spurious and\",\n \"a -= c; a ^= (c>>3) b -= c; b -= a; b\",\n \"def update_ports(self, scan, dport, flags): scan.flags_or |= flags if dport in scan.ports: return\",\n \"'TCP x-mas', TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas', TH_SYN|TH_FIN: 'TCP syn/fin', TH_FIN|TH_ACK: 'TCP fin/ack', TH_SYN|TH_ACK: 'TCP\",\n \"= 'TCP full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for\",\n \"and (scan.dst == recent.src))): scan.type = 'TCP full-connect' break elif scan.proto==UDP: scan.type =\",\n \"continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan) # Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash]\",\n \"a scanning host is doing an idle scan. # Basically # A -scanning\",\n \"scan recently from host # X to host Z. Then actually Y is\",\n \"port if dport < 1024: scan.weight += 3 else: scan.weight += 1 scan.ports.append(dport)\",\n \"Remove entry if slow_scan: del self.long_scans[scan.hash] else: del self.scans[scan.hash] return False else: scan.type\",\n \"UDP scan scan.flags_or = 0 elif scan.proto==SCTP: if scan.chunk_type==1: scan.type = 'SCTP Init'\",\n \"5 WEIGHT_THRESHOLD = 25 PIDFILE=\\\"/var/run/pyscanlogger.pid\\\" # TCP flag constants TH_URG=dpkt.tcp.TH_URG TH_ACK=dpkt.tcp.TH_ACK TH_PSH=dpkt.tcp.TH_PUSH TH_RST=dpkt.tcp.TH_RST\",\n \"full-connect' break elif scan.proto==UDP: scan.type = 'UDP' # Reset flags for UDP scan\",\n \"'Removing',key,lscan.src,'since not a scan' del self.long_scans[key] elif len(lscan.timediffs)>2: # More than 2 entries,\",\n \"scan from %s to %s (ports:%s), average timediff %.2fs' msg = line %\",\n \"self.daemon: print >> sys.stderr, line def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False): \\\"\\\"\\\" Log\",\n \"%s (ports: %s) using zombie host %s' else: tup.append(scan.time_avg) line = 'Continuation of\"\n]","isConversational":false}}},{"rowIdx":2592,"cells":{"ngram":{"kind":"list like","value":["from itertools import combinations import copy def get_reverse(n): if n == 1: return","elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains,","visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0, len(e)):","\"\"\" e = copy.copy(edges) visited = [False for _ in e] miggroup =","for j in range(0, len(e)): if j != i and not visited[j]: e[j]","b): for i in a: for j in b: if i + 1","1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j)","if tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor,","closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1,","t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and","return True return False def compare(a, b): return (a > b) - (a","l1, l2): for i in range(len(l1)): if d1 == l1[i] and d2 ==","t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1] and","\"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains:","= True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param","def flip(i): if i == 0: i = 1 elif i == 1:","\"\"\" :param domain1: :param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target,",":param domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif","for i in tpl: if edge in i: return True return False def","< mindist: mint = t return mint def check_continuity(a, b): for i in","domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2)","() for i in domains: dist = get_absdist(i, target) if dist < closet:","return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = []","tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num","== startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param","e = copy.copy(edges) visited = [False for _ in e] miggroup = []",":return: \"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] ==","abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return: \"\"\"","target) if dist < closet: closet = dist closetd = i return closetd","sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt +=","return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return:","val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if","1 elif i == 1: i = 0 return i def get_free_domains(limits, blocks,","interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold =","in range(0, oldlen)] combnew = [] if oldlen != newlen: for i in","get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges,","for i in domains: dist = get_absdist(i, target) if dist < closet: closet","list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0]","combinations import copy def get_reverse(n): if n == 1: return 0 else: return","interval = limits[1] - limits[0] for i in blocks: if limits[1] > i","!= newlen: for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i,","get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] ==","mindist = 10000 mint = None for t in targets: dist = min(get_absdist(t,","= min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint =","def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return True","limits[1] > i > limits[0]: tmp = abs(bound - i) if tmp <","= i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param","for i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return","range(0, oldlen)] combnew = [] if oldlen != newlen: for i in range(0,","target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 =","visited = [False for _ in e] miggroup = [] cnt = -1","compare(a, b): return (a > b) - (a < b) def flip(i): if","[False for _ in e] miggroup = [] cnt = -1 for i","mint = None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains)","== j: return i, j return None def check_bond_existence(d1, d2, l1, l2): for","== j or i - 1 == j: return i, j return None","< closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2,","in range(2)] t = 0 for x in e: v[t], n[t] = x","\"\"\" :param target1: :param target2: :param domains1: :param domains2: :return: \"\"\" if target1[0]","return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1","d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param edges: :return:",":param edges: :return: \"\"\" e = copy.copy(edges) visited = [False for _ in","def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge","tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1])","copy.copy(edges) visited = [False for _ in e] miggroup = [] cnt =","== domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2,","list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]:","return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl:","= t return mint def check_continuity(a, b): for i in a: for j","key=lambda tup: tup[1]) mindist = 10000 mint = None for t in targets:",":return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param","== domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\"","i) if tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen,","range(2)] t = 0 for x in e: v[t], n[t] = x t","def get_reverse(n): if n == 1: return 0 else: return 1 def get_edge_info(e):","interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen],","or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p],","visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1:","= () for i in domains: dist = get_absdist(i, target) if dist <","\"\"\" :param edges: :return: \"\"\" e = copy.copy(edges) visited = [False for _","j in range(0, len(e)): if j != i and not visited[j]: e[j] =","closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param target2: :param domains1:","mindist: mint = t return mint def check_continuity(a, b): for i in a:","j in b: if i + 1 == j or i - 1","d2, l1, l2): for i in range(len(l1)): if d1 == l1[i] and d2","i = 1 elif i == 1: i = 0 return i def","e: v[t], n[t] = x t += 1 return v, n def sort_e_by_domain(val):","def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\" return abs(domain1[1] -","+ 1 == j or i - 1 == j: return i, j","+ combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices:","\"\"\" :param target: :param domains: :return: \"\"\" closet = 10000 closetd = ()","= list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)] combnew","in i: return True return False def compare(a, b): return (a > b)","i > limits[0]: tmp = abs(bound - i) if tmp < interval: interval","t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return","target2, domains1, domains2): \"\"\" :param target1: :param target2: :param domains1: :param domains2: :return:","1 for j in range(0, len(e)): if j != i and not visited[j]:","p=0): \"\"\" :param edges: :return: \"\"\" e = copy.copy(edges) visited = [False for","= list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not","1])) if dist < mindist: mint = t return mint def check_continuity(a, b):","range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 =","get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1])","import combinations import copy def get_reverse(n): if n == 1: return 0 else:","check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return True return","== 0: i = 1 elif i == 1: i = 0 return","v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl):","= copy.copy(edges) visited = [False for _ in e] miggroup = [] cnt","get_reverse(n): if n == 1: return 0 else: return 1 def get_edge_info(e): v","def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e = copy.copy(edges) visited =","in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold +","return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\" return","j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def","t return mint def check_continuity(a, b): for i in a: for j in","len(e)): if j != i and not visited[j]: e[j] = list(e[j]) e[j][p] =","or i - 1 == j: return i, j return None def check_bond_existence(d1,","i in range(0, oldlen)] combnew = [] if oldlen != newlen: for i","domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0]","- limits[0] for i in blocks: if limits[1] > i > limits[0]: tmp","combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)]","t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def","i in tpl: if edge in i: return True return False def compare(a,","< interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold","i, j return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)):","get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return: \"\"\" closet = 10000 closetd","n[t] = x t += 1 return v, n def sort_e_by_domain(val): return val[0][1]","[] if oldlen != newlen: for i in range(0, oldlen): for j in","return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] -","return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i:","indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort()","domains[len(domains) - 1])) if dist < mindist: mint = t return mint def","+ 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1]","> b) - (a < b) def flip(i): if i == 0: i","e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0])","domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2)","0: i = 1 elif i == 1: i = 0 return i","if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda","(a < b) def flip(i): if i == 0: i = 1 elif","newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i","and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda","tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 ==","i: return True return False def compare(a, b): return (a > b) -","d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e =","domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains,","domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]:","get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains =","i in domains: dist = get_absdist(i, target) if dist < closet: closet =","return False def compare(a, b): return (a > b) - (a < b)","and t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1]","key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1","in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges,","len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1]","i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else:","range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices,","[(i, i) for i in range(0, oldlen)] combnew = [] if oldlen !=","- 1 == t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1]","or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j]","get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi, ni =","e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if","sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue","[0 for i in range(2)] t = 0 for x in e: v[t],","domains: :return: \"\"\" closet = 10000 closetd = () for i in domains:","tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j","get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for",":param domains: :return: \"\"\" closet = 10000 closetd = () for i in","targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000","return mint def check_continuity(a, b): for i in a: for j in b:","target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 =","= list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0]","e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if","(t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\ or","indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0,","i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True","def check_continuity(a, b): for i in a: for j in b: if i","return 0 else: return 1 def get_edge_info(e): v = [0 for i in","2)) combself = [(i, i) for i in range(0, oldlen)] combnew = []","combself = [(i, i) for i in range(0, oldlen)] combnew = [] if","key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1","1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1] -","= [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] ==","- i) if tmp < interval: interval = tmp return interval def get_combinations(oldlen,","edges: :return: \"\"\" e = copy.copy(edges) visited = [False for _ in e]","dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param","if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2","- (a < b) def flip(i): if i == 0: i = 1","oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew +","i == 1: i = 0 return i def get_free_domains(limits, blocks, bound): limits","cnt = -1 for i in range(0, len(e)): if visited[i]: continue e[i] =","if limits[1] > i > limits[0]: tmp = abs(bound - i) if tmp","= sorted(limits) interval = limits[1] - limits[0] for i in blocks: if limits[1]","if oldlen != newlen: for i in range(0, oldlen): for j in range(oldlen,","i in a: for j in b: if i + 1 == j","t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i])","1: i = 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits)","None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1]))","= [0 for i in range(2)] t = 0 for x in e:","in e: v[t], n[t] = x t += 1 return v, n def","= get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains","key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for","== 1: i = 0 return i def get_free_domains(limits, blocks, bound): limits =","def get_edge_info(e): v = [0 for i in range(2)] n = [0 for","0 for x in e: v[t], n[t] = x t += 1 return","startstrand): d = [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if","i in blocks: if limits[1] > i > limits[0]: tmp = abs(bound -","+= 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0]","10000 mint = None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t,","= get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def","= [] if oldlen != newlen: for i in range(0, oldlen): for j","edge in i: return True return False def compare(a, b): return (a >","t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 =",":param domain1: :param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains):","sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in","i - 1 == j: return i, j return None def check_bond_existence(d1, d2,","sorted(limits) interval = limits[1] - limits[0] for i in blocks: if limits[1] >","list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)] combnew =","for i in range(2)] n = [0 for i in range(2)] t =","closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup:","1: return 0 else: return 1 def get_edge_info(e): v = [0 for i","None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 ==","t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0])","closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1:","domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for","< b) def flip(i): if i == 0: i = 1 elif i","domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t return","x t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val):","combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d","sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for t in","= 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval =","visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup:","return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge,","= [0 for i in range(2)] n = [0 for i in range(2)]","= 10000 closetd = () for i in domains: dist = get_absdist(i, target)","domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return:","= sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for t","oldlen != newlen: for i in range(0, oldlen): for j in range(oldlen, newlen):","if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1","targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist:","= limits[1] - limits[0] for i in blocks: if limits[1] > i >","= sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt","= -1 for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i])","- 1 == j: return i, j return None def check_bond_existence(d1, d2, l1,","return i, j return None def check_bond_existence(d1, d2, l1, l2): for i in","for x in e: v[t], n[t] = x t += 1 return v,",":param target: :param domains: :return: \"\"\" closet = 10000 closetd = () for","l2): for i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]:","dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint","get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for i","visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0])","and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup","= sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]):","get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param target2: :param domains1: :param domains2:","= sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1]","range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True return False","_ in e] miggroup = [] cnt = -1 for i in range(0,","interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i,","domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target:","range(2)] n = [0 for i in range(2)] t = 0 for x","+= 1 for j in range(0, len(e)): if j != i and not","== domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1,","get_absdist(i, target) if dist < closet: closet = dist closetd = i return","-1 for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p]","j: return i, j return None def check_bond_existence(d1, d2, l1, l2): for i","mint def check_continuity(a, b): for i in a: for j in b: if","= get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1,","i) for i in range(0, oldlen)] combnew = [] if oldlen != newlen:","for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself","elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2","n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for","if dist < closet: closet = dist closetd = i return closetd def","n == 1: return 0 else: return 1 def get_edge_info(e): v = [0","def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 == l1[i]","- 1])) if dist < mindist: mint = t return mint def check_continuity(a,","[0 for i in range(2)] n = [0 for i in range(2)] t","limits = sorted(limits) interval = limits[1] - limits[0] for i in blocks: if","not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup:","startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param edges:","blocks: if limits[1] > i > limits[0]: tmp = abs(bound - i) if","combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi,","for j in b: if i + 1 == j or i -","= get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0]","domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1)","blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for i in","if i + 1 == j or i - 1 == j: return","== 1: return 0 else: return 1 def get_edge_info(e): v = [0 for","def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param target2: :param domains1: :param","= get_absdist(i, target) if dist < closet: closet = dist closetd = i",":return: \"\"\" closet = 10000 closetd = () for i in domains: dist","t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return","domains: dist = get_absdist(i, target) if dist < closet: closet = dist closetd","t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 ==","domains2): \"\"\" :param target1: :param target2: :param domains1: :param domains2: :return: \"\"\" if","domain1: :param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\"","tmp = abs(bound - i) if tmp < interval: interval = tmp return","return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself =","if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\","for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] =","b: if i + 1 == j or i - 1 == j:","get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist =","1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def","== t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1","in domains: dist = get_absdist(i, target) if dist < closet: closet = dist","i + 1 == j or i - 1 == j: return i,","closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1])","e] miggroup = [] cnt = -1 for i in range(0, len(e)): if","cnt += 1 for j in range(0, len(e)): if j != i and","\"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]:","elif i == 1: i = 0 return i def get_free_domains(limits, blocks, bound):","closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if","= tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2))","\"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint","= [] cnt = -1 for i in range(0, len(e)): if visited[i]: continue","miggroup = [] cnt = -1 for i in range(0, len(e)): if visited[i]:","t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] !=","!= t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup:","len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p],","for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return","+ combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i","target2: :param domains1: :param domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1 =",":param domains1: :param domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1,","b) def flip(i): if i == 0: i = 1 elif i ==","domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return: \"\"\" closet =","tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in","return 1 def get_edge_info(e): v = [0 for i in range(2)] n =","in e] miggroup = [] cnt = -1 for i in range(0, len(e)):","False def compare(a, b): return (a > b) - (a < b) def","True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j !=","range(0, len(e)): if j != i and not visited[j]: e[j] = list(e[j]) e[j][p]","(t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])):","get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2","target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 =","True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2:","i in range(2)] n = [0 for i in range(2)] t = 0","combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in","for i in range(2)] t = 0 for x in e: v[t], n[t]","if j != i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p])","def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi, ni","if dist < mindist: mint = t return mint def check_continuity(a, b): for","= get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0]","import copy def get_reverse(n): if n == 1: return 0 else: return 1","d = [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0]","n = [0 for i in range(2)] t = 0 for x in","check_continuity(a, b): for i in a: for j in b: if i +","bound): limits = sorted(limits) interval = limits[1] - limits[0] for i in blocks:","\"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None","closet = 10000 closetd = () for i in domains: dist = get_absdist(i,","limits[1] - limits[0] for i in blocks: if limits[1] > i > limits[0]:","if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0,","def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i","range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 ==","i = 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval","!= t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1","if i == 0: i = 1 elif i == 1: i =","def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i)",":return: \"\"\" e = copy.copy(edges) visited = [False for _ in e] miggroup","i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold","get_edge_info(e): v = [0 for i in range(2)] n = [0 for i","domains): \"\"\" :param target: :param domains: :return: \"\"\" closet = 10000 closetd =","j return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if","limits[0]: tmp = abs(bound - i) if tmp < interval: interval = tmp","vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\"","flip(i): if i == 0: i = 1 elif i == 1: i","> i > limits[0]: tmp = abs(bound - i) if tmp < interval:","= [(i, i) for i in range(0, oldlen)] combnew = [] if oldlen","miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\" return abs(domain1[1]","domains1, domains2): \"\"\" :param target1: :param target2: :param domains1: :param domains2: :return: \"\"\"","domains1: :param domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1)","i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p])","ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d","tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for","closetd = () for i in domains: dist = get_absdist(i, target) if dist","miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return:","vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return","in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1","if n == 1: return 0 else: return 1 def get_edge_info(e): v =","tup: tup[1]) mindist = 10000 mint = None for t in targets: dist","continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0])","check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 == l1[i] and","domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def","newlen: for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j))","in a: for j in b: if i + 1 == j or","= 0 for x in e: v[t], n[t] = x t += 1","def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return: \"\"\" closet = 10000","in range(2)] n = [0 for i in range(2)] t = 0 for","= x t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def","val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return","dist = get_absdist(i, target) if dist < closet: closet = dist closetd =","get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] ==","return (a > b) - (a < b) def flip(i): if i ==","list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] !=","- 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True","closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return","dist < closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1,","t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist","indices, startstrand): d = [] for i in indices: vi, ni = get_edge_info(edges[i][0])","- domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param target: :param domains: :return: \"\"\" closet","e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i]","mint = t return mint def check_continuity(a, b): for i in a: for","in range(0, len(e)): if j != i and not visited[j]: e[j] = list(e[j])","in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1])","+ 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1,","target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets):","1 == j or i - 1 == j: return i, j return","> limits[0]: tmp = abs(bound - i) if tmp < interval: interval =","in tpl: if edge in i: return True return False def compare(a, b):","== t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1] - 1","sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] -","in b: if i + 1 == j or i - 1 ==","d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e","= abs(bound - i) if tmp < interval: interval = tmp return interval","copy def get_reverse(n): if n == 1: return 0 else: return 1 def","== domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2,","for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if","= True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j","for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1]","def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist","0 else: return 1 def get_edge_info(e): v = [0 for i in range(2)]","[] cnt = -1 for i in range(0, len(e)): if visited[i]: continue e[i]","= 1 elif i == 1: i = 0 return i def get_free_domains(limits,","for i in blocks: if limits[1] > i > limits[0]: tmp = abs(bound","closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif","dist < mindist: mint = t return mint def check_continuity(a, b): for i","b): return (a > b) - (a < b) def flip(i): if i","d def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e = copy.copy(edges) visited","for i in a: for j in b: if i + 1 ==","(t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda","range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew","combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for","abs(bound - i) if tmp < interval: interval = tmp return interval def","tup[1]) mindist = 10000 mint = None for t in targets: dist =","in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist <","j != i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2","1 == j: return i, j return None def check_bond_existence(d1, d2, l1, l2):","tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist):","else: return 1 def get_edge_info(e): v = [0 for i in range(2)] n",":param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \"\"\" :param","num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] +","combnew = [] if oldlen != newlen: for i in range(0, oldlen): for","closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\"","closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2):","domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]:","\"\"\" closet = 10000 closetd = () for i in domains: dist =","1 def get_edge_info(e): v = [0 for i in range(2)] n = [0","[] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand:","domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\" return abs(domain1[1] - domain2[1]) def","0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1]","sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in","check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e = copy.copy(edges) visited = [False",":param target2: :param domains1: :param domains2: :return: \"\"\" if target1[0] == domains1[0][0]: closetd1","i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param target2:","in blocks: if limits[1] > i > limits[0]: tmp = abs(bound - i)","else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\"","!= i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 =",":return: \"\"\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint =","oldlen)] combnew = [] if oldlen != newlen: for i in range(0, oldlen):","continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if","list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True","i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p],","x in e: v[t], n[t] = x t += 1 return v, n","== t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break","\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]):","10000 closetd = () for i in domains: dist = get_absdist(i, target) if","j or i - 1 == j: return i, j return None def","itertools import combinations import copy def get_reverse(n): if n == 1: return 0","for _ in e] miggroup = [] cnt = -1 for i in","newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand):","v = [0 for i in range(2)] n = [0 for i in","get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t return mint","tpl): for i in tpl: if edge in i: return True return False","break return miggroup def get_absdist(domain1, domain2): \"\"\" :param domain1: :param domain2: :return: \"\"\"","(t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] =","in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True return","t = 0 for x in e: v[t], n[t] = x t +=","i == 0: i = 1 elif i == 1: i = 0","limits[0] for i in blocks: if limits[1] > i > limits[0]: tmp =","= 10000 mint = None for t in targets: dist = min(get_absdist(t, domains[0]),","t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\ or (t1[0][1] - 1 ==","min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t","visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if","(a > b) - (a < b) def flip(i): if i == 0:","= dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\"","= list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] =","1 == t2[1][1]) \\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] +","1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2):","return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \"\"\" :param target1: :param target2: :param","def compare(a, b): return (a > b) - (a < b) def flip(i):","= None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) -","for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0])","a: for j in b: if i + 1 == j or i","return closetd1, closetd2 def get_closest_target(domains, targets): \"\"\" :return: \"\"\" domains = sorted(domains, key=lambda","def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for","if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in","= [False for _ in e] miggroup = [] cnt = -1 for","== t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\"","tpl: if edge in i: return True return False def compare(a, b): return","i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0]","v[t], n[t] = x t += 1 return v, n def sort_e_by_domain(val): return","if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0):","= list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or","if edge in i: return True return False def compare(a, b): return (a","miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j != i","in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1","target1: :param target2: :param domains1: :param domains2: :return: \"\"\" if target1[0] == domains1[0][0]:","True return False def compare(a, b): return (a > b) - (a <","b) - (a < b) def flip(i): if i == 0: i =","tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself","i in range(2)] t = 0 for x in e: v[t], n[t] =",":param target1: :param target2: :param domains1: :param domains2: :return: \"\"\" if target1[0] ==","target: :param domains: :return: \"\"\" closet = 10000 closetd = () for i","return d def check_following_migration(edges, p=0): \"\"\" :param edges: :return: \"\"\" e = copy.copy(edges)","cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in","for i in range(0, oldlen)] combnew = [] if oldlen != newlen: for","not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0,","j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d =","t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \"\"\" :param"],"string":"[\n \"from itertools import combinations import copy def get_reverse(n): if n == 1: return\",\n \"elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains,\",\n \"visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0, len(e)):\",\n \"\\\"\\\"\\\" e = copy.copy(edges) visited = [False for _ in e] miggroup =\",\n \"for j in range(0, len(e)): if j != i and not visited[j]: e[j]\",\n \"b): for i in a: for j in b: if i + 1\",\n \"1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j)\",\n \"if tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor,\",\n \"closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1,\",\n \"t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and\",\n \"return True return False def compare(a, b): return (a > b) - (a\",\n \"l1, l2): for i in range(len(l1)): if d1 == l1[i] and d2 ==\",\n \"t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1] and\",\n \"\\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains:\",\n \"= True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param\",\n \"def flip(i): if i == 0: i = 1 elif i == 1:\",\n \"\\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target,\",\n \":param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif\",\n \"for i in tpl: if edge in i: return True return False def\",\n \"< mindist: mint = t return mint def check_continuity(a, b): for i in\",\n \"domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2)\",\n \"() for i in domains: dist = get_absdist(i, target) if dist < closet:\",\n \"return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = []\",\n \"tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num\",\n \"== startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param\",\n \"e = copy.copy(edges) visited = [False for _ in e] miggroup = []\",\n \":return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] ==\",\n \"abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\"\",\n \"target) if dist < closet: closet = dist closetd = i return closetd\",\n \"sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt +=\",\n \"return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return:\",\n \"val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if\",\n \"1 elif i == 1: i = 0 return i def get_free_domains(limits, blocks,\",\n \"interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold =\",\n \"in range(0, oldlen)] combnew = [] if oldlen != newlen: for i in\",\n \"get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges,\",\n \"for i in domains: dist = get_absdist(i, target) if dist < closet: closet\",\n \"list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0]\",\n \"combinations import copy def get_reverse(n): if n == 1: return 0 else: return\",\n \"interval = limits[1] - limits[0] for i in blocks: if limits[1] > i\",\n \"!= newlen: for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i,\",\n \"get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] ==\",\n \"mindist = 10000 mint = None for t in targets: dist = min(get_absdist(t,\",\n \"= min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint =\",\n \"def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return True\",\n \"limits[1] > i > limits[0]: tmp = abs(bound - i) if tmp <\",\n \"= i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param\",\n \"for i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return\",\n \"range(0, oldlen)] combnew = [] if oldlen != newlen: for i in range(0,\",\n \"target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 =\",\n \"visited = [False for _ in e] miggroup = [] cnt = -1\",\n \"compare(a, b): return (a > b) - (a < b) def flip(i): if\",\n \"[False for _ in e] miggroup = [] cnt = -1 for i\",\n \"mint = None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains)\",\n \"== j: return i, j return None def check_bond_existence(d1, d2, l1, l2): for\",\n \"== j or i - 1 == j: return i, j return None\",\n \"< closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2,\",\n \"in range(2)] t = 0 for x in e: v[t], n[t] = x\",\n \"\\\"\\\"\\\" :param target1: :param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0]\",\n \"return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1\",\n \"d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return:\",\n \":param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False for _ in\",\n \"def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge\",\n \"tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1])\",\n \"copy.copy(edges) visited = [False for _ in e] miggroup = [] cnt =\",\n \"== domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2,\",\n \"list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]:\",\n \"return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl:\",\n \"= t return mint def check_continuity(a, b): for i in a: for j\",\n \"key=lambda tup: tup[1]) mindist = 10000 mint = None for t in targets:\",\n \":return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param\",\n \"== domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\"\",\n \"i) if tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen,\",\n \"range(2)] t = 0 for x in e: v[t], n[t] = x t\",\n \"def get_reverse(n): if n == 1: return 0 else: return 1 def get_edge_info(e):\",\n \"interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen],\",\n \"or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p],\",\n \"visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1:\",\n \"= () for i in domains: dist = get_absdist(i, target) if dist <\",\n \"\\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False for _\",\n \"j in range(0, len(e)): if j != i and not visited[j]: e[j] =\",\n \"closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1:\",\n \"mindist: mint = t return mint def check_continuity(a, b): for i in a:\",\n \"j in b: if i + 1 == j or i - 1\",\n \"d2, l1, l2): for i in range(len(l1)): if d1 == l1[i] and d2\",\n \"i = 1 elif i == 1: i = 0 return i def\",\n \"e: v[t], n[t] = x t += 1 return v, n def sort_e_by_domain(val):\",\n \"def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] -\",\n \"+ 1 == j or i - 1 == j: return i, j\",\n \"+ combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices:\",\n \"\\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet = 10000 closetd = ()\",\n \"= list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)] combnew\",\n \"in i: return True return False def compare(a, b): return (a > b)\",\n \"i > limits[0]: tmp = abs(bound - i) if tmp < interval: interval\",\n \"t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return\",\n \"target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1: :param domains2: :return:\",\n \"1 for j in range(0, len(e)): if j != i and not visited[j]:\",\n \"p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False for\",\n \"= list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not\",\n \"1])) if dist < mindist: mint = t return mint def check_continuity(a, b):\",\n \"range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 =\",\n \"get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1])\",\n \"import combinations import copy def get_reverse(n): if n == 1: return 0 else:\",\n \"check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return True return\",\n \"== 0: i = 1 elif i == 1: i = 0 return\",\n \"v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl):\",\n \"= copy.copy(edges) visited = [False for _ in e] miggroup = [] cnt\",\n \"get_reverse(n): if n == 1: return 0 else: return 1 def get_edge_info(e): v\",\n \"def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited =\",\n \"in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold +\",\n \"return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return\",\n \"j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def\",\n \"t return mint def check_continuity(a, b): for i in a: for j in\",\n \"len(e)): if j != i and not visited[j]: e[j] = list(e[j]) e[j][p] =\",\n \"or i - 1 == j: return i, j return None def check_bond_existence(d1,\",\n \"i in range(0, oldlen)] combnew = [] if oldlen != newlen: for i\",\n \"domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0]\",\n \"- limits[0] for i in blocks: if limits[1] > i > limits[0]: tmp\",\n \"combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)]\",\n \"t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def\",\n \"i in tpl: if edge in i: return True return False def compare(a,\",\n \"< interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold\",\n \"i, j return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)):\",\n \"get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet = 10000 closetd\",\n \"n[t] = x t += 1 return v, n def sort_e_by_domain(val): return val[0][1]\",\n \"[] if oldlen != newlen: for i in range(0, oldlen): for j in\",\n \"return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] -\",\n \"return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i:\",\n \"indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort()\",\n \"domains[len(domains) - 1])) if dist < mindist: mint = t return mint def\",\n \"+ 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1]\",\n \"> b) - (a < b) def flip(i): if i == 0: i\",\n \"e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0])\",\n \"domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2)\",\n \"0: i = 1 elif i == 1: i = 0 return i\",\n \"if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda\",\n \"(a < b) def flip(i): if i == 0: i = 1 elif\",\n \"newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i\",\n \"and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda\",\n \"tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 ==\",\n \"i: return True return False def compare(a, b): return (a > b) -\",\n \"d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e =\",\n \"domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains,\",\n \"domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]:\",\n \"get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains =\",\n \"i in domains: dist = get_absdist(i, target) if dist < closet: closet =\",\n \"return False def compare(a, b): return (a > b) - (a < b)\",\n \"and t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1]\",\n \"key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1\",\n \"in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges,\",\n \"len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1]\",\n \"i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else:\",\n \"range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices,\",\n \"[(i, i) for i in range(0, oldlen)] combnew = [] if oldlen !=\",\n \"- 1 == t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1]\",\n \"or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j]\",\n \"get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi, ni =\",\n \"e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if\",\n \"sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue\",\n \"[0 for i in range(2)] t = 0 for x in e: v[t],\",\n \"domains: :return: \\\"\\\"\\\" closet = 10000 closetd = () for i in domains:\",\n \"tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j\",\n \"get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for\",\n \":param domains: :return: \\\"\\\"\\\" closet = 10000 closetd = () for i in\",\n \"targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000\",\n \"return mint def check_continuity(a, b): for i in a: for j in b:\",\n \"target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 =\",\n \"= list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0]\",\n \"e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if\",\n \"(t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\ or\",\n \"indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0,\",\n \"i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True\",\n \"def check_continuity(a, b): for i in a: for j in b: if i\",\n \"return 0 else: return 1 def get_edge_info(e): v = [0 for i in\",\n \"2)) combself = [(i, i) for i in range(0, oldlen)] combnew = []\",\n \"combself = [(i, i) for i in range(0, oldlen)] combnew = [] if\",\n \"key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1\",\n \"1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1] -\",\n \"= [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] ==\",\n \"- i) if tmp < interval: interval = tmp return interval def get_combinations(oldlen,\",\n \"edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False for _ in e]\",\n \"dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param\",\n \"if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2\",\n \"- (a < b) def flip(i): if i == 0: i = 1\",\n \"oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew +\",\n \"i == 1: i = 0 return i def get_free_domains(limits, blocks, bound): limits\",\n \"cnt = -1 for i in range(0, len(e)): if visited[i]: continue e[i] =\",\n \"if limits[1] > i > limits[0]: tmp = abs(bound - i) if tmp\",\n \"= sorted(limits) interval = limits[1] - limits[0] for i in blocks: if limits[1]\",\n \"if oldlen != newlen: for i in range(0, oldlen): for j in range(oldlen,\",\n \"i in a: for j in b: if i + 1 == j\",\n \"t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i])\",\n \"1: i = 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits)\",\n \"None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1]))\",\n \"= [0 for i in range(2)] t = 0 for x in e:\",\n \"in e: v[t], n[t] = x t += 1 return v, n def\",\n \"= get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains\",\n \"key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for\",\n \"== 1: i = 0 return i def get_free_domains(limits, blocks, bound): limits =\",\n \"def get_edge_info(e): v = [0 for i in range(2)] n = [0 for\",\n \"0 for x in e: v[t], n[t] = x t += 1 return\",\n \"startstrand): d = [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if\",\n \"i in blocks: if limits[1] > i > limits[0]: tmp = abs(bound -\",\n \"+= 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0]\",\n \"10000 mint = None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t,\",\n \"= get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def\",\n \"= [] if oldlen != newlen: for i in range(0, oldlen): for j\",\n \"edge in i: return True return False def compare(a, b): return (a >\",\n \"t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 =\",\n \":param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains):\",\n \"sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in\",\n \"i - 1 == j: return i, j return None def check_bond_existence(d1, d2,\",\n \"sorted(limits) interval = limits[1] - limits[0] for i in blocks: if limits[1] >\",\n \"list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in range(0, oldlen)] combnew =\",\n \"for i in range(2)] n = [0 for i in range(2)] t =\",\n \"closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup:\",\n \"1: return 0 else: return 1 def get_edge_info(e): v = [0 for i\",\n \"None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 ==\",\n \"t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0])\",\n \"closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1:\",\n \"domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for\",\n \"< b) def flip(i): if i == 0: i = 1 elif i\",\n \"domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t return\",\n \"x t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val):\",\n \"combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d\",\n \"sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for t in\",\n \"= 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval =\",\n \"visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup:\",\n \"return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge,\",\n \"= [0 for i in range(2)] n = [0 for i in range(2)]\",\n \"= 10000 closetd = () for i in domains: dist = get_absdist(i, target)\",\n \"domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return:\",\n \"= sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None for t\",\n \"oldlen != newlen: for i in range(0, oldlen): for j in range(oldlen, newlen):\",\n \"if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1\",\n \"targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist:\",\n \"= limits[1] - limits[0] for i in blocks: if limits[1] > i >\",\n \"= sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt\",\n \"= -1 for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i])\",\n \"- 1 == j: return i, j return None def check_bond_existence(d1, d2, l1,\",\n \"return i, j return None def check_bond_existence(d1, d2, l1, l2): for i in\",\n \"for x in e: v[t], n[t] = x t += 1 return v,\",\n \":param target: :param domains: :return: \\\"\\\"\\\" closet = 10000 closetd = () for\",\n \"l2): for i in range(len(l1)): if d1 == l1[i] and d2 == l2[i]:\",\n \"dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint\",\n \"get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for i\",\n \"visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0])\",\n \"and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup\",\n \"= sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]):\",\n \"get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1: :param domains2:\",\n \"= sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1]\",\n \"range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True return False\",\n \"_ in e] miggroup = [] cnt = -1 for i in range(0,\",\n \"interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i,\",\n \"domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target:\",\n \"range(2)] n = [0 for i in range(2)] t = 0 for x\",\n \"+= 1 for j in range(0, len(e)): if j != i and not\",\n \"== domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1,\",\n \"get_absdist(i, target) if dist < closet: closet = dist closetd = i return\",\n \"-1 for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p]\",\n \"j: return i, j return None def check_bond_existence(d1, d2, l1, l2): for i\",\n \"mint def check_continuity(a, b): for i in a: for j in b: if\",\n \"= get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1,\",\n \"i) for i in range(0, oldlen)] combnew = [] if oldlen != newlen:\",\n \"for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew + combself\",\n \"elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2\",\n \"n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for\",\n \"if dist < closet: closet = dist closetd = i return closetd def\",\n \"n == 1: return 0 else: return 1 def get_edge_info(e): v = [0\",\n \"def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 == l1[i]\",\n \"- 1])) if dist < mindist: mint = t return mint def check_continuity(a,\",\n \"[0 for i in range(2)] n = [0 for i in range(2)] t\",\n \"limits = sorted(limits) interval = limits[1] - limits[0] for i in blocks: if\",\n \"not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup:\",\n \"startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges:\",\n \"blocks: if limits[1] > i > limits[0]: tmp = abs(bound - i) if\",\n \"combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi,\",\n \"for j in b: if i + 1 == j or i -\",\n \"= get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0]\",\n \"domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1)\",\n \"blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for i in\",\n \"if i + 1 == j or i - 1 == j: return\",\n \"== 1: return 0 else: return 1 def get_edge_info(e): v = [0 for\",\n \"def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1: :param\",\n \"= get_absdist(i, target) if dist < closet: closet = dist closetd = i\",\n \":return: \\\"\\\"\\\" closet = 10000 closetd = () for i in domains: dist\",\n \"t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return\",\n \"domains: dist = get_absdist(i, target) if dist < closet: closet = dist closetd\",\n \"t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 ==\",\n \"domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if\",\n \"domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\"\",\n \"tmp = abs(bound - i) if tmp < interval: interval = tmp return\",\n \"return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself =\",\n \"if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\\",\n \"for i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] =\",\n \"b: if i + 1 == j or i - 1 == j:\",\n \"get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist =\",\n \"1 return v, n def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def\",\n \"== t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1\",\n \"in domains: dist = get_absdist(i, target) if dist < closet: closet = dist\",\n \"i + 1 == j or i - 1 == j: return i,\",\n \"closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1])\",\n \"e] miggroup = [] cnt = -1 for i in range(0, len(e)): if\",\n \"cnt += 1 for j in range(0, len(e)): if j != i and\",\n \"\\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]:\",\n \"elif i == 1: i = 0 return i def get_free_domains(limits, blocks, bound):\",\n \"closetd1 = get_closet_domain_to_target(target1, domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if\",\n \"= tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2))\",\n \"\\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint\",\n \"= [] cnt = -1 for i in range(0, len(e)): if visited[i]: continue\",\n \"miggroup = [] cnt = -1 for i in range(0, len(e)): if visited[i]:\",\n \"t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] !=\",\n \"!= t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup:\",\n \"len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p],\",\n \"for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return\",\n \"+ combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i\",\n \"target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 =\",\n \":param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1,\",\n \"b) def flip(i): if i == 0: i = 1 elif i ==\",\n \"domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet =\",\n \"tup[0]) if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in\",\n \"return 1 def get_edge_info(e): v = [0 for i in range(2)] n =\",\n \"in e] miggroup = [] cnt = -1 for i in range(0, len(e)):\",\n \"False def compare(a, b): return (a > b) - (a < b) def\",\n \"True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j !=\",\n \"range(0, len(e)): if j != i and not visited[j]: e[j] = list(e[j]) e[j][p]\",\n \"(t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])):\",\n \"get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2\",\n \"target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 =\",\n \"True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2:\",\n \"i in range(2)] n = [0 for i in range(2)] t = 0\",\n \"combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for i in\",\n \"for i in range(2)] t = 0 for x in e: v[t], n[t]\",\n \"if j != i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p])\",\n \"def get_migrate_nodes(edges, indices, startstrand): d = [] for i in indices: vi, ni\",\n \"if dist < mindist: mint = t return mint def check_continuity(a, b): for\",\n \"= get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0]\",\n \"import copy def get_reverse(n): if n == 1: return 0 else: return 1\",\n \"d = [] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0]\",\n \"n = [0 for i in range(2)] t = 0 for x in\",\n \"check_continuity(a, b): for i in a: for j in b: if i +\",\n \"bound): limits = sorted(limits) interval = limits[1] - limits[0] for i in blocks:\",\n \"\\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint = None\",\n \"closet = 10000 closetd = () for i in domains: dist = get_absdist(i,\",\n \"limits[1] - limits[0] for i in blocks: if limits[1] > i > limits[0]:\",\n \"if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0,\",\n \"def sort_e_by_domain(val): return val[0][1] def sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i\",\n \"range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 ==\",\n \"i = 0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval\",\n \"!= t1[0][0]) or (t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1\",\n \"if i == 0: i = 1 elif i == 1: i =\",\n \"def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i)\",\n \":return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False for _ in e] miggroup\",\n \"i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold\",\n \"get_edge_info(e): v = [0 for i in range(2)] n = [0 for i\",\n \"domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet = 10000 closetd =\",\n \"j return None def check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if\",\n \"limits[0]: tmp = abs(bound - i) if tmp < interval: interval = tmp\",\n \"vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\"\",\n \"flip(i): if i == 0: i = 1 elif i == 1: i\",\n \"> i > limits[0]: tmp = abs(bound - i) if tmp < interval:\",\n \"= [(i, i) for i in range(0, oldlen)] combnew = [] if oldlen\",\n \"miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1]\",\n \"domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\"\",\n \"domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target1, domains1)\",\n \"i in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p])\",\n \"ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d\",\n \"tup: tup[0]) if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for\",\n \"closetd = () for i in domains: dist = get_absdist(i, target) if dist\",\n \"miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return:\",\n \"vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return\",\n \"in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1\",\n \"if n == 1: return 0 else: return 1 def get_edge_info(e): v =\",\n \"tup: tup[1]) mindist = 10000 mint = None for t in targets: dist\",\n \"continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0])\",\n \"check_bond_existence(d1, d2, l1, l2): for i in range(len(l1)): if d1 == l1[i] and\",\n \"domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def\",\n \"newlen: for i in range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j))\",\n \"in a: for j in b: if i + 1 == j or\",\n \"= 0 for x in e: v[t], n[t] = x t += 1\",\n \"def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet = 10000\",\n \"in range(2)] n = [0 for i in range(2)] t = 0 for\",\n \"= x t += 1 return v, n def sort_e_by_domain(val): return val[0][1] def\",\n \"val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in i: return\",\n \"dist = get_absdist(i, target) if dist < closet: closet = dist closetd =\",\n \"get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] ==\",\n \"return (a > b) - (a < b) def flip(i): if i ==\",\n \"list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] !=\",\n \"- 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True\",\n \"closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return\",\n \"dist < closet: closet = dist closetd = i return closetd def get_domains_on_2sides(target1,\",\n \"t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist\",\n \"indices, startstrand): d = [] for i in indices: vi, ni = get_edge_info(edges[i][0])\",\n \"- domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param target: :param domains: :return: \\\"\\\"\\\" closet\",\n \"e[i][p] = list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i]\",\n \"mint = t return mint def check_continuity(a, b): for i in a: for\",\n \"in range(0, len(e)): if j != i and not visited[j]: e[j] = list(e[j])\",\n \"in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1])\",\n \"+ 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1,\",\n \"target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets):\",\n \"1 == j or i - 1 == j: return i, j return\",\n \"> limits[0]: tmp = abs(bound - i) if tmp < interval: interval =\",\n \"in tpl: if edge in i: return True return False def compare(a, b):\",\n \"== t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1] - 1\",\n \"sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] + 1 == t2[0][1] and t1[1][1] -\",\n \"in b: if i + 1 == j or i - 1 ==\",\n \"d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e\",\n \"= abs(bound - i) if tmp < interval: interval = tmp return interval\",\n \"copy def get_reverse(n): if n == 1: return 0 else: return 1 def\",\n \"== domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2,\",\n \"for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if\",\n \"= True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j\",\n \"for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1]\",\n \"def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist\",\n \"0 else: return 1 def get_edge_info(e): v = [0 for i in range(2)]\",\n \"[] cnt = -1 for i in range(0, len(e)): if visited[i]: continue e[i]\",\n \"= 1 elif i == 1: i = 0 return i def get_free_domains(limits,\",\n \"for i in blocks: if limits[1] > i > limits[0]: tmp = abs(bound\",\n \"closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target1, domains2) elif\",\n \"dist < mindist: mint = t return mint def check_continuity(a, b): for i\",\n \"b): return (a > b) - (a < b) def flip(i): if i\",\n \"d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited\",\n \"for i in a: for j in b: if i + 1 ==\",\n \"(t2[1][0] != t1[1][0]): continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda\",\n \"range(0, oldlen): for j in range(oldlen, newlen): combnew.append((i, j)) return combold + combnew\",\n \"combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d = [] for\",\n \"abs(bound - i) if tmp < interval: interval = tmp return interval def\",\n \"tup[1]) mindist = 10000 mint = None for t in targets: dist =\",\n \"in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist <\",\n \"j != i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2\",\n \"1 == j: return i, j return None def check_bond_existence(d1, d2, l1, l2):\",\n \"tmp < interval: interval = tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist):\",\n \"else: return 1 def get_edge_info(e): v = [0 for i in range(2)] n\",\n \":param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def get_closet_domain_to_target(target, domains): \\\"\\\"\\\" :param\",\n \"num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if (t1[0][1] +\",\n \"combnew = [] if oldlen != newlen: for i in range(0, oldlen): for\",\n \"closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\"\",\n \"closet = dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2):\",\n \"domains1) elif target2[0] == domains1[0][0]: closetd1 = get_closet_domain_to_target(target2, domains1) if target1[0] == domains2[0][0]:\",\n \"\\\"\\\"\\\" closet = 10000 closetd = () for i in domains: dist =\",\n \"1 def get_edge_info(e): v = [0 for i in range(2)] n = [0\",\n \"[] for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand:\",\n \"domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\" return abs(domain1[1] - domain2[1]) def\",\n \"0 return i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1]\",\n \"sort_by_strand(val): return val[0][0] def check_edge_in_tuplelist(edge, tpl): for i in tpl: if edge in\",\n \"check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges) visited = [False\",\n \":param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]: closetd1\",\n \"i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2:\",\n \"in blocks: if limits[1] > i > limits[0]: tmp = abs(bound - i)\",\n \"else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\"\",\n \"!= i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 =\",\n \":return: \\\"\\\"\\\" domains = sorted(domains, key=lambda tup: tup[1]) mindist = 10000 mint =\",\n \"oldlen)] combnew = [] if oldlen != newlen: for i in range(0, oldlen):\",\n \"continue for num in range(0, len(miggroup[cnt])): t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0]) if\",\n \"list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] = True\",\n \"i and not visited[j]: e[j] = list(e[j]) e[j][p] = list(e[j][p]) t2 = sorted(e[j][p],\",\n \"x in e: v[t], n[t] = x t += 1 return v, n\",\n \"== t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break\",\n \"\\\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]):\",\n \"10000 closetd = () for i in domains: dist = get_absdist(i, target) if\",\n \"j or i - 1 == j: return i, j return None def\",\n \"itertools import combinations import copy def get_reverse(n): if n == 1: return 0\",\n \"for _ in e] miggroup = [] cnt = -1 for i in\",\n \"newlen): combnew.append((i, j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand):\",\n \"v = [0 for i in range(2)] n = [0 for i in\",\n \"get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t return mint\",\n \"tpl): for i in tpl: if edge in i: return True return False\",\n \"break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param domain1: :param domain2: :return: \\\"\\\"\\\"\",\n \"(t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]): visited[j] =\",\n \"in range(len(l1)): if d1 == l1[i] and d2 == l2[i]: return True return\",\n \"t = 0 for x in e: v[t], n[t] = x t +=\",\n \"i == 0: i = 1 elif i == 1: i = 0\",\n \"limits[0] for i in blocks: if limits[1] > i > limits[0]: tmp =\",\n \"= 10000 mint = None for t in targets: dist = min(get_absdist(t, domains[0]),\",\n \"t2[0][1] and t1[1][1] - 1 == t2[1][1]) \\\\ or (t1[0][1] - 1 ==\",\n \"min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1])) if dist < mindist: mint = t\",\n \"visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if\",\n \"(a > b) - (a < b) def flip(i): if i == 0:\",\n \"= dist closetd = i return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\"\",\n \"= list(e[i][p]) t1 = sorted(e[i][p], key=lambda tup: tup[0]) if not visited[i]: visited[i] =\",\n \"1 == t2[1][1]) \\\\ or (t1[0][1] - 1 == t2[0][1] and t1[1][1] +\",\n \"1 == t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2):\",\n \"return closetd def get_domains_on_2sides(target1, target2, domains1, domains2): \\\"\\\"\\\" :param target1: :param target2: :param\",\n \"def compare(a, b): return (a > b) - (a < b) def flip(i):\",\n \"= None for t in targets: dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) -\",\n \"for i in indices: vi, ni = get_edge_info(edges[i][0]) if vi[0] == startstrand: d.append(ni[0])\",\n \"a: for j in b: if i + 1 == j or i\",\n \"return closetd1, closetd2 def get_closest_target(domains, targets): \\\"\\\"\\\" :return: \\\"\\\"\\\" domains = sorted(domains, key=lambda\",\n \"def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0] for\",\n \"if not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in\",\n \"= [False for _ in e] miggroup = [] cnt = -1 for\",\n \"== t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\"\",\n \"tpl: if edge in i: return True return False def compare(a, b): return\",\n \"i def get_free_domains(limits, blocks, bound): limits = sorted(limits) interval = limits[1] - limits[0]\",\n \"v[t], n[t] = x t += 1 return v, n def sort_e_by_domain(val): return\",\n \"if vi[0] == startstrand: d.append(ni[0]) else: d.append(ni[1]) d.sort() return d def check_following_migration(edges, p=0):\",\n \"= list(e[j][p]) t2 = sorted(e[j][p], key=lambda tup: tup[0]) if (t2[0][0] != t1[0][0]) or\",\n \"if edge in i: return True return False def compare(a, b): return (a\",\n \"miggroup.append([i]) cnt += 1 for j in range(0, len(e)): if j != i\",\n \"in range(0, len(e)): if visited[i]: continue e[i] = list(e[i]) e[i][p] = list(e[i][p]) t1\",\n \"target1: :param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] == domains1[0][0]:\",\n \"True return False def compare(a, b): return (a > b) - (a <\",\n \"b) - (a < b) def flip(i): if i == 0: i =\",\n \"tmp return interval def get_combinations(oldlen, newlen, cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself\",\n \"i in range(2)] t = 0 for x in e: v[t], n[t] =\",\n \":param target1: :param target2: :param domains1: :param domains2: :return: \\\"\\\"\\\" if target1[0] ==\",\n \"target: :param domains: :return: \\\"\\\"\\\" closet = 10000 closetd = () for i\",\n \"return d def check_following_migration(edges, p=0): \\\"\\\"\\\" :param edges: :return: \\\"\\\"\\\" e = copy.copy(edges)\",\n \"cursor, indexlist): combold = list(combinations(indexlist[cursor:oldlen], 2)) combself = [(i, i) for i in\",\n \"for i in range(0, oldlen)] combnew = [] if oldlen != newlen: for\",\n \"not visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in range(0,\",\n \"j)) return combold + combnew + combself def get_migrate_nodes(edges, indices, startstrand): d =\",\n \"t2[1][1]): visited[j] = True miggroup[cnt].append(j) break return miggroup def get_absdist(domain1, domain2): \\\"\\\"\\\" :param\"\n]","isConversational":false}}},{"rowIdx":2593,"cells":{"ngram":{"kind":"list like","value":["# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","writing, software # distributed under the License is distributed on an \"AS IS\"","specific language governing permissions and # limitations under the License. \"\"\" Sparse Fine-tuning","task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from","KIND, either express or implied. # See the License for the specific language","You can also adapt this script on your own question answering task. Pointers","Unless required by applicable law or agreed to in writing, software # distributed","# SparseTrainer should appear first in the base classes, as its functions must","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","# See the License for the specific language governing permissions and # limitations","Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under the","License. # You may obtain a copy of the License at # #","rights reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\");","must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self,","own question answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer","its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self,","law or agreed to in writing, software # distributed under the License is","the License for the specific language governing permissions and # limitations under the","compliance with the License. # You may obtain a copy of the License","first in the base classes, as its functions must override QATrainer and its","appear first in the base classes, as its functions must override QATrainer and","classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs)","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","this file except in compliance with the License. # You may obtain a","library models for question answering. \"\"\" # You can also adapt this script","# Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under","the Apache License, Version 2.0 (the \"License\"); # you may not use this","you may not use this file except in compliance with the License. #","Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train","\"\"\" Sparse Fine-tuning the library models for question answering. \"\"\" # You can","Fine-tuning the library models for question answering. \"\"\" # You can also adapt","question answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","the specific language governing permissions and # limitations under the License. \"\"\" Sparse","ANY KIND, either express or implied. # See the License for the specific","class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs) SparseTrainer.__init__(self, sparse_args)",".qa_train import QATrainer # SparseTrainer should appear first in the base classes, as","from .qa_train import QATrainer # SparseTrainer should appear first in the base classes,","in compliance with the License. # You may obtain a copy of the","models for question answering. \"\"\" # You can also adapt this script on","HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear","use this file except in compliance with the License. # You may obtain","also adapt this script on your own question answering task. Pointers for this","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","for the specific language governing permissions and # limitations under the License. \"\"\"","not use this file except in compliance with the License. # You may","should appear first in the base classes, as its functions must override QATrainer","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","import QATrainer # SparseTrainer should appear first in the base classes, as its","classes, as its functions must override QATrainer and its base classes (Trainer) class","See the License for the specific language governing permissions and # limitations under","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","governing permissions and # limitations under the License. \"\"\" Sparse Fine-tuning the library","License, Version 2.0 (the \"License\"); # you may not use this file except","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","permissions and # limitations under the License. \"\"\" Sparse Fine-tuning the library models","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","this script on your own question answering task. Pointers for this are left","QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args,","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in","OF ANY KIND, either express or implied. # See the License for the","your own question answering task. Pointers for this are left as comments. from","left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer","2.0 (the \"License\"); # you may not use this file except in compliance","and # limitations under the License. \"\"\" Sparse Fine-tuning the library models for","on your own question answering task. Pointers for this are left as comments.","import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in the","# you may not use this file except in compliance with the License.","agreed to in writing, software # distributed under the License is distributed on","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","(the \"License\"); # you may not use this file except in compliance with","# limitations under the License. \"\"\" Sparse Fine-tuning the library models for question","# # Unless required by applicable law or agreed to in writing, software","the License. \"\"\" Sparse Fine-tuning the library models for question answering. \"\"\" #","Sparse Fine-tuning the library models for question answering. \"\"\" # You can also","express or implied. # See the License for the specific language governing permissions","Version 2.0 (the \"License\"); # you may not use this file except in","# Unless required by applicable law or agreed to in writing, software #","except in compliance with the License. # You may obtain a copy of","for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import","question answering. \"\"\" # You can also adapt this script on your own","by applicable law or agreed to in writing, software # distributed under the","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","either express or implied. # See the License for the specific language governing","\"\"\" # You can also adapt this script on your own question answering","software # distributed under the License is distributed on an \"AS IS\" BASIS,","adapt this script on your own question answering task. Pointers for this are","base classes, as its functions must override QATrainer and its base classes (Trainer)","Team All rights reserved. # # Licensed under the Apache License, Version 2.0","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def","# coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # #","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args,","script on your own question answering task. Pointers for this are left as","file except in compliance with the License. # You may obtain a copy","are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer #","for question answering. \"\"\" # You can also adapt this script on your","as its functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer,","the library models for question answering. \"\"\" # You can also adapt this","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","limitations under the License. \"\"\" Sparse Fine-tuning the library models for question answering.","License for the specific language governing permissions and # limitations under the License.","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","the License. # You may obtain a copy of the License at #","base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args,","the base classes, as its functions must override QATrainer and its base classes","to in writing, software # distributed under the License is distributed on an","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","# You can also adapt this script on your own question answering task.","answering. \"\"\" # You can also adapt this script on your own question","# distributed under the License is distributed on an \"AS IS\" BASIS, #","implied. # See the License for the specific language governing permissions and #","\"License\"); # you may not use this file except in compliance with the","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer","required by applicable law or agreed to in writing, software # distributed under","SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in the base","in the base classes, as its functions must override QATrainer and its base","applicable law or agreed to in writing, software # distributed under the License","and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs):","The HuggingFace Team All rights reserved. # # Licensed under the Apache License,","can also adapt this script on your own question answering task. Pointers for","language governing permissions and # limitations under the License. \"\"\" Sparse Fine-tuning the","QATrainer # SparseTrainer should appear first in the base classes, as its functions","or agreed to in writing, software # distributed under the License is distributed","or implied. # See the License for the specific language governing permissions and","its functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer):","(Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs) SparseTrainer.__init__(self,","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","CONDITIONS OF ANY KIND, either express or implied. # See the License for","All rights reserved. # # Licensed under the Apache License, Version 2.0 (the","Apache License, Version 2.0 (the \"License\"); # you may not use this file","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","SparseTrainer should appear first in the base classes, as its functions must override","coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed","with the License. # You may obtain a copy of the License at","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","2020 The HuggingFace Team All rights reserved. # # Licensed under the Apache","in writing, software # distributed under the License is distributed on an \"AS","as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should","under the License. \"\"\" Sparse Fine-tuning the library models for question answering. \"\"\"","License. \"\"\" Sparse Fine-tuning the library models for question answering. \"\"\" # You","under the Apache License, Version 2.0 (the \"License\"); # you may not use"],"string":"[\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"specific language governing permissions and # limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning\",\n \"task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"You can also adapt this script on your own question answering task. Pointers\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"# SparseTrainer should appear first in the base classes, as its functions must\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under the\",\n \"License. # You may obtain a copy of the License at # #\",\n \"rights reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self,\",\n \"own question answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer\",\n \"its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self,\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"first in the base classes, as its functions must override QATrainer and its\",\n \"appear first in the base classes, as its functions must override QATrainer and\",\n \"classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs)\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"library models for question answering. \\\"\\\"\\\" # You can also adapt this script\",\n \"# Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"you may not use this file except in compliance with the License. #\",\n \"Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train\",\n \"\\\"\\\"\\\" Sparse Fine-tuning the library models for question answering. \\\"\\\"\\\" # You can\",\n \"Fine-tuning the library models for question answering. \\\"\\\"\\\" # You can also adapt\",\n \"question answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"the specific language governing permissions and # limitations under the License. \\\"\\\"\\\" Sparse\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs) SparseTrainer.__init__(self, sparse_args)\",\n \".qa_train import QATrainer # SparseTrainer should appear first in the base classes, as\",\n \"from .qa_train import QATrainer # SparseTrainer should appear first in the base classes,\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"models for question answering. \\\"\\\"\\\" # You can also adapt this script on\",\n \"HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"also adapt this script on your own question answering task. Pointers for this\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"for the specific language governing permissions and # limitations under the License. \\\"\\\"\\\"\",\n \"not use this file except in compliance with the License. # You may\",\n \"should appear first in the base classes, as its functions must override QATrainer\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"import QATrainer # SparseTrainer should appear first in the base classes, as its\",\n \"classes, as its functions must override QATrainer and its base classes (Trainer) class\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"governing permissions and # limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the library\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"permissions and # limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the library models\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"this script on your own question answering task. Pointers for this are left\",\n \"QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args,\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"answering task. Pointers for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"your own question answering task. Pointers for this are left as comments. from\",\n \"left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"and # limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the library models for\",\n \"on your own question answering task. Pointers for this are left as comments.\",\n \"import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in the\",\n \"# you may not use this file except in compliance with the License.\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"# limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the library models for question\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"the License. \\\"\\\"\\\" Sparse Fine-tuning the library models for question answering. \\\"\\\"\\\" #\",\n \"Sparse Fine-tuning the library models for question answering. \\\"\\\"\\\" # You can also\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"for this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import\",\n \"question answering. \\\"\\\"\\\" # You can also adapt this script on your own\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"either express or implied. # See the License for the specific language governing\",\n \"\\\"\\\"\\\" # You can also adapt this script on your own question answering\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"adapt this script on your own question answering task. Pointers for this are\",\n \"base classes, as its functions must override QATrainer and its base classes (Trainer)\",\n \"Team All rights reserved. # # Licensed under the Apache License, Version 2.0\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def\",\n \"# coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # #\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args,\",\n \"script on your own question answering task. Pointers for this are left as\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer #\",\n \"for question answering. \\\"\\\"\\\" # You can also adapt this script on your\",\n \"as its functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer,\",\n \"the library models for question answering. \\\"\\\"\\\" # You can also adapt this\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the library models for question answering.\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"the License. # You may obtain a copy of the License at #\",\n \"base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args,\",\n \"the base classes, as its functions must override QATrainer and its base classes\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"# You can also adapt this script on your own question answering task.\",\n \"answering. \\\"\\\"\\\" # You can also adapt this script on your own question\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"this are left as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"SparseTrainer from .qa_train import QATrainer # SparseTrainer should appear first in the base\",\n \"in the base classes, as its functions must override QATrainer and its base\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs):\",\n \"The HuggingFace Team All rights reserved. # # Licensed under the Apache License,\",\n \"can also adapt this script on your own question answering task. Pointers for\",\n \"language governing permissions and # limitations under the License. \\\"\\\"\\\" Sparse Fine-tuning the\",\n \"QATrainer # SparseTrainer should appear first in the base classes, as its functions\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"its functions must override QATrainer and its base classes (Trainer) class QASparseTrainer(SparseTrainer, QATrainer):\",\n \"(Trainer) class QASparseTrainer(SparseTrainer, QATrainer): def __init__(self, sparse_args, *args, **kwargs): QATrainer.__init__(self, *args, **kwargs) SparseTrainer.__init__(self,\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"All rights reserved. # # Licensed under the Apache License, Version 2.0 (the\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"SparseTrainer should appear first in the base classes, as its functions must override\",\n \"coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed\",\n \"with the License. # You may obtain a copy of the License at\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"2020 The HuggingFace Team All rights reserved. # # Licensed under the Apache\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"as comments. from nn_pruning.sparse_trainer import SparseTrainer from .qa_train import QATrainer # SparseTrainer should\",\n \"under the License. \\\"\\\"\\\" Sparse Fine-tuning the library models for question answering. \\\"\\\"\\\"\",\n \"License. \\\"\\\"\\\" Sparse Fine-tuning the library models for question answering. \\\"\\\"\\\" # You\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\"\n]","isConversational":false}}},{"rowIdx":2594,"cells":{"ngram":{"kind":"list like","value":["# Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid')","Unless required by applicable law or agreed to in writing, software # distributed","# Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if","on failure to execute step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node,","self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This","\"with the following target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object =","Default is True. :param create_nonroot_volumes: If True, non-root volumes are created. If False,","is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are created.","is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to be","Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']):",":param task: a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure,","RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid","'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False)","Apache License, Version 2.0 (the \"License\"); you may # not use this file","*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info =","the License. You may obtain # a copy of the License at #","execute step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info","may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","= driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete","= task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def","msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for","'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create raid configuration","\"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","@METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create","task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description':","created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are created. If","found to be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure,","Raid configuration failed msg = \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot',","on the given node. :param task: a TaskManager instance. :param create_root_volume: If True,","to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise","\"\"\" from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils","with the License. You may obtain # a copy of the License at","raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values(","utils as manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules","for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task,","If False, no non-root volumes are created. Default is True. :raises: MissingParameterValue, if","`True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a","root volume is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes","class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties","(_(\"Failed to create raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot',","driver_internal_info node.save() else: # Raid configuration failed msg = \"Unable to create raid\"","raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is done.\",","disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf","'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf =","# Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid')","= (\"Unable to delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress',","\"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError:","use this file except in compliance with the License. You may obtain #","Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to","to create raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","RAID configuration. :param task: a TaskManager instance containing the node to act on.","not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean step is done.\",","= ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'):","exc): LOG.error(\"RAID configuration job failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid,","ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task:","BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration","%(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def","is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF","a RAID configuration on a bare metal using agent ramdisk. This method creates","LOG.error(\"RAID configuration job failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message':","interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed for","LOG.info(\"No logical drive found to delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError","implied. See the # License for the specific language governing permissions and limitations","\"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task,","step create_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid,","except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on node %(node)s\", {'node': node.uuid})","the RAID configuration. :param task: a TaskManager instance containing the node to act","was found to be empty after skipping root volume and/or non-root volumes. :raises:","create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root volumes are","create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node","<reponame>armohamm/ironic # Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under","is created during RAID configuration. Otherwise, no root volume is created. Default is","driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating","drive found to delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception:","LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class","logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _","raid from ironic.common import states from ironic.conductor import utils as manager_utils from ironic","create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare metal using agent ramdisk.","task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info =","you may # not use this file except in compliance with the License.","in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else:","= raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB","driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info","not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done,","node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as","% raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure(","task): \"\"\"Delete the RAID configuration. :param task: a TaskManager instance containing the node","specific methods \"\"\" from ironic_lib import metrics_utils from oslo_log import log as logging","KIND, either express or implied. See the # License for the specific language","the License. \"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib import metrics_utils from","node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete","properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration","( 'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.'","updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s","file except in compliance with the License. You may obtain # a copy","'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg","%s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation,","\"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express","node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = \"Unable to","2018 Hewlett Packard Enterprise Development LP # # Licensed under the Apache License,","root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \"\"\"","node.save() else: # Raid configuration failed msg = \"Unable to create raid\" self._pop_driver_internal_values(","to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create","None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task,","driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info =","software # distributed under the License is distributed on an \"AS IS\" BASIS,","is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {}","keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts =","RAID configuration on a bare metal using agent ramdisk. This method creates a","= driver_internal_info node.save() else: # Raid configuration failed msg = (\"Unable to delete","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key","OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return","if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is done.\", {'uuid':","'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \"","create raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY","MissingParameterValue, if node.target_raid_config is missing or was found to be empty after skipping","= conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB","task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid':","import metrics_utils from oslo_log import log as logging from oslo_utils import importutils from","the # License for the specific language governing permissions and limitations # under","for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def","_prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step ==","'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={","self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether","Development LP # # Licensed under the Apache License, Version 2.0 (the \"License\");","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","volume is created during RAID configuration. Otherwise, no root volume is created. Default","node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config']","to execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for node","a RAID configuration on the given node. :param task: a TaskManager instance. :param","'create_root_volume': { 'description': ( 'This specifies whether to create the root volume. '","task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object","You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","= driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in","for node %(node)s \" \"with the following target RAID configuration: %(target)s\", {'node': node.uuid,","LP # # Licensed under the Apache License, Version 2.0 (the \"License\"); you","required by applicable law or agreed to in writing, software # distributed under","progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: #","log as logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n","raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation","no root volume is created. Default is True. :param create_nonroot_volumes: If True, non-root","LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object =","applicable law or agreed to in writing, software # distributed under the License","ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo","states from ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers","task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value(","failed \" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg})","= task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info","clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","== 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step')","%(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to","in compliance with the License. You may obtain # a copy of the","ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF","volumes are created. If False, no non-root volumes are created. Default is True.","True, non-root volumes are created. If False, no non-root volumes are created. Default","node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node","or agreed to in writing, software # distributed under the License is distributed","manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress')","), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID","delete_configuration invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try:","def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed for node %(node)s. \"","self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0,","'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self,","deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task,","try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task,","\"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib import metrics_utils from oslo_log import","logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","{'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete raid configuration","ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config","'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed","False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for","create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare metal using","ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common","METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for","node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking","with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation","with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive","to delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation =","License is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS","writing, software # distributed under the License is distributed on an \"AS IS\"","%s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation,","'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \"","task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key]","(\"Unable to delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',","else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not","the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': {","agent ramdisk. This method creates a RAID configuration on the given node. :param","'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status","driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node)","is missing or was found to be empty after skipping root volume and/or",":param task: a TaskManager instance. :param create_root_volume: If True, a root volume is","from ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers import","def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step","empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to","ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config","%s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in","= task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save()","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception')","node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node","keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info","True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for","\"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info","node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info =","Licensed under the Apache License, Version 2.0 (the \"License\"); you may # not","invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: #","'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed","task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key","is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under the Apache","2.0 (the \"License\"); you may # not use this file except in compliance","'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the","Raid configuration failed msg = (\"Unable to delete this logical disks: %s\" %","raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task,","node.target_raid_config is missing or was found to be empty after skipping root volume","'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies","raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value(","create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False } })","task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration","= task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save()","task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare metal using agent","ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on node %(node)s\", {'node': node.uuid}) except","# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT","key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task,","@base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task: a TaskManager","'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on","License, Version 2.0 (the \"License\"); you may # not use this file except","node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress,","operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param","a root volume is created during RAID configuration. Otherwise, no root volume is","driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save()","from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS","created during RAID configuration. Otherwise, no root volume is created. Default is True.","# Raid configuration failed msg = \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress',","after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute","configuration failed msg = (\"Unable to delete this logical disks: %s\" % raid_conf['logical_disks'])","or was found to be empty after skipping root volume and/or non-root volumes.","create_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err':","given node. :param task: a TaskManager instance. :param create_root_volume: If True, a root","this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info =","}, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes.","_set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False","from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__)","key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task,","{'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: #","(_(\"Failed to delete raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',","msg, exc): LOG.error(\"RAID configuration job failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node':","ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config)","{'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on","delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed","agreed to in writing, software # distributed under the License is distributed on","method creates a RAID configuration on the given node. :param task: a TaskManager","volumes. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node target_raid_config","checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid","import _ from ironic.common import raid from ironic.common import states from ironic.conductor import","_set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True","as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error =","node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task,","using agent ramdisk. This method creates a RAID configuration on the given node.","task: a TaskManager instance. :param create_root_volume: If True, a root volume is created","{'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: #","updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s","if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is","on. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node LOG.debug(\"OOB","# Unless required by applicable law or agreed to in writing, software #","\"Clean step delete_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node':","try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task,","RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES","the following target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node)","by applicable law or agreed to in writing, software # distributed under the","import log as logging from oslo_utils import importutils from ironic.common import exception from","RAID configuration on the given node. :param task: a TaskManager instance. :param create_root_volume:","step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","under the License is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is","configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT","'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on node %(node)s\",","ironic.common.i18n import _ from ironic.common import raid from ironic.common import states from ironic.conductor","= (_(\"Failed to delete raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress',","node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete raid configuration on","`True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to","node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid","msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:","ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean step","{} LOG.debug(\"Node %(uuid)s raid delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task,",":param create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root volumes","driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:","LOG.debug(\"Node %(uuid)s raid delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress',","created. If False, no non-root volumes are created. Default is True. :raises: MissingParameterValue,","= (_(\"Failed to create raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress',","return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration()","driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s","is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf)",":raises: MissingParameterValue, if node.target_raid_config is missing or was found to be empty after","), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create","True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to be empty","import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__)","ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create raid configuration on node %s\")","len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step is done.\", {'uuid': node.uuid})","# # Licensed under the Apache License, Version 2.0 (the \"License\"); you may","driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \" \"with the","failure to execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for","except in compliance with the License. You may obtain # a copy of","return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = (","methods \"\"\" from ironic_lib import metrics_utils from oslo_log import log as logging from","and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node =","target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling","as ilo_exception: operation = (_(\"Failed to create raid configuration on node %s\") %","for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self,","to in writing, software # distributed under the License is distributed on an","oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common","driver_internal_info node.save() else: # Raid configuration failed msg = (\"Unable to delete this","import exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common import","\"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the","def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] =","'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True):","done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node","task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration","a TaskManager instance. :param create_root_volume: If True, a root volume is created during","'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf =","Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of","RAID configuration. Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes:","node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration","act on. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node","distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT #","node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task,","RAID specific methods \"\"\" from ironic_lib import metrics_utils from oslo_log import log as","def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare metal","a bare metal using agent ramdisk. This method creates a RAID configuration on","driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node %(node)s with","failure to execute step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume,","in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts","conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface","# not use this file except in compliance with the License. You may","self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration","task, msg, exc): LOG.error(\"RAID configuration job failed for node %(node)s. \" \"Message: '%(message)s'.\",","step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed","\" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except","as manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules import","task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key,","NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config(","# License for the specific language governing permissions and limitations # under the","raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid","non-root volumes are created. If False, no non-root volumes are created. Default is","if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot')","states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if","configuration. Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes: If","create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure(","ironic.common import states from ironic.conductor import utils as manager_utils from ironic import conf","ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete raid configuration on node %s\")","If True, a root volume is created during RAID configuration. Otherwise, no root","raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean","states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task,","configuration failed msg = \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on","in writing, software # distributed under the License is distributed on an \"AS","Version 2.0 (the \"License\"); you may # not use this file except in","self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf","else: # Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config))","\"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc):","node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \" \"with","specifies whether to create the root volume. ' 'Defaults to `True`.' ), 'required':","ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed for node %(node)s.","configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node,","= logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface):","non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing","task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config","return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed for node","} }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a","\"License\"); you may # not use this file except in compliance with the","else: # Raid configuration failed msg = (\"Unable to delete this logical disks:","delete raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node %(node)s with error: %(err)s\" %","node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No","root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description':","import importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common import","'description': ( 'This specifies whether to create the root volume. ' 'Defaults to","the Apache License, Version 2.0 (the \"License\"); you may # not use this","def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None)","# Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']):","task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed","node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create raid","= node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \"","= {} LOG.debug(\"Node %(uuid)s raid delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values(","_ from ironic.common import raid from ironic.common import states from ironic.conductor import utils","not use this file except in compliance with the License. You may obtain","driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info","}) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare","driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the","len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean step is done.\", {'uuid':","Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config","self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': {","target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: #","done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else:","for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg","ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers import base","key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step):","NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration","License for the specific language governing permissions and limitations # under the License.","from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid from","node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail')","raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid","node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node %(node)s with error:","= deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress')","clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","msg = (\"Unable to delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task,","are created. If False, no non-root volumes are created. Default is True. :raises:","on a bare metal using agent ramdisk. This method creates a RAID configuration","task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info","during RAID configuration. Otherwise, no root volume is created. Default is True. :param","configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT","'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = \"Unable","create_configuration for node %(node)s \" \"with the following target RAID configuration: %(target)s\", {'node':","if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean step is","whether to create the root volume. ' 'Defaults to `True`.' ), 'required': False","If True, non-root volumes are created. If False, no non-root volumes are created.","instance. :param create_root_volume: If True, a root volume is created during RAID configuration.","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the #","driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:","driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node %(node)s with","root volume is created during RAID configuration. Otherwise, no root volume is created.","driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info =",":raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID",":param create_root_volume: If True, a root volume is created during RAID configuration. Otherwise,","task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key","= \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root","governing permissions and limitations # under the License. \"\"\" iLO5 RAID specific methods","def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task: a TaskManager instance containing","Enterprise Development LP # # Licensed under the Apache License, Version 2.0 (the","OF ANY KIND, either express or implied. See the # License for the","= driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task,","@METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task: a","on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def","target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if","' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This","target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \" \"with the following target","task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if","node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on node","exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common import states","import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG","TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on failure to","if node.target_raid_config is missing or was found to be empty after skipping root","# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the","% node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception)","language governing permissions and limitations # under the License. \"\"\" iLO5 RAID specific","task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key]","on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID","volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node","create the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes':","from ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from","task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self,","the specific language governing permissions and limitations # under the License. \"\"\" iLO5","True, a root volume is created during RAID configuration. Otherwise, no root volume","(the \"License\"); you may # not use this file except in compliance with","failed msg = (\"Unable to delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values(","from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import","abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task: a TaskManager instance","( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean step","False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root","of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the interface.\"\"\"","TaskManager instance. :param create_root_volume: If True, a root volume is created during RAID","CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of","# # Unless required by applicable law or agreed to in writing, software","ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import importutils","node %(node)s \" \"with the following target RAID configuration: %(target)s\", {'node': node.uuid, 'target':","= msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in","delete_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err':","{'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress,","= node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status","under the License. \"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib import metrics_utils","failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error =","created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found","ilo_exception: operation = (_(\"Failed to create raid configuration on node %s\") % node.uuid)","in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys):","progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: #","driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating","%(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed","%(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical","for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self,","volumes. ' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task,","msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create raid configuration on","configuration job failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg})","for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid","RAID create_configuration for node %(node)s \" \"with the following target RAID configuration: %(target)s\",","to act on. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node =","License. You may obtain # a copy of the License at # #","%s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise","the License is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR","for the specific language governing permissions and limitations # under the License. \"\"\"","missing or was found to be empty after skipping root volume and/or non-root","= driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node %(node)s","# under the License. \"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib import","the given node. :param task: a TaskManager instance. :param create_root_volume: If True, a","to execute step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes)","done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else:","= False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info","oslo_log import log as logging from oslo_utils import importutils from ironic.common import exception","volume is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are","raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete","ANY KIND, either express or implied. See the # License for the specific","delete_configuration(self, task): \"\"\"Delete the RAID configuration. :param task: a TaskManager instance containing the","the non-root volumes. ' 'Defaults to `True`.' ), 'required': False } }) def","'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ),","# Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under the","step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\", node.uuid)","Hewlett Packard Enterprise Development LP # # Licensed under the Apache License, Version","create_nonroot_volumes=True): \"\"\"Create a RAID configuration on a bare metal using agent ramdisk. This","step delete_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node': node.uuid,","delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","\"on node %(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError","be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure","node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task):","ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not","msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete on node %(node)s\", {'node':","'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg =","raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task,","except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete raid configuration on node","%(uuid)s raid delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',","= ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'):","True. :param create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root","task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts)","driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT)","to create the root volume. ' 'Defaults to `True`.' ), 'required': False },","%(node)s \" \"with the following target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config})","LOG.debug(\"Node %(uuid)s raid create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress',","keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info","task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def","to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether","job failed for node %(node)s. \" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error","'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \"\"\"Create a RAID configuration","deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task,","argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root volume.","%(uuid)s raid create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot',","raid delete clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","%(node)s with error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception:","under the Apache License, Version 2.0 (the \"License\"); you may # not use","WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See","{ 'description': ( 'This specifies whether to create the root volume. ' 'Defaults","conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common","configuration on the given node. :param task: a TaskManager instance. :param create_root_volume: If","'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = (\"Unable","' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True,","as logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import","to be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on","= ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create clean","execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked for node %s.\",","= ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean","as ilo_exception: operation = (_(\"Failed to delete raid configuration on node %s\") %","See the # License for the specific language governing permissions and limitations #","volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or","permissions and limitations # under the License. \"\"\" iLO5 RAID specific methods \"\"\"","importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return the","node.save() else: # Raid configuration failed msg = (\"Unable to delete this logical","= driver_internal_info node.save() else: # Raid configuration failed msg = \"Unable to create","from oslo_log import log as logging from oslo_utils import importutils from ironic.common import","bare metal using agent ramdisk. This method creates a RAID configuration on the","Packard Enterprise Development LP # # Licensed under the Apache License, Version 2.0","configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config =","raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean","%(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to delete raid","create_root_volume: If True, a root volume is created during RAID configuration. Otherwise, no","%(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in","law or agreed to in writing, software # distributed under the License is","deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF","% node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception)","self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step","operation = (_(\"Failed to delete raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task,","a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on failure","common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error","'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults to","express or implied. See the # License for the specific language governing permissions","are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was","{ 'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults","an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either","# a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","CONDITIONS OF ANY KIND, either express or implied. See the # License for","ramdisk. This method creates a RAID configuration on the given node. :param task:","def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] =","skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step.","if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is","done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node","node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration')","metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def","configuration on a bare metal using agent ramdisk. This method creates a RAID","ilo_exception: operation = (_(\"Failed to delete raid configuration on node %s\") % node.uuid)","in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else:","from ironic.common import raid from ironic.common import states from ironic.conductor import utils as","manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils","checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid","specific language governing permissions and limitations # under the License. \"\"\" iLO5 RAID","is True. :param create_nonroot_volumes: If True, non-root volumes are created. If False, no","is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()","else: # Raid configuration failed msg = \"Unable to create raid\" self._pop_driver_internal_values( task,","\" \"Message: '%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self,","not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done,","'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies","from ironic.common.i18n import _ from ironic.common import raid from ironic.common import states from","operation = (_(\"Failed to create raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task,","configuration. :param task: a TaskManager instance containing the node to act on. :raises:","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to","iLO5.\"\"\" def get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self,","create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for","RAID delete_configuration invoked for node %s.\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node)","metrics_utils from oslo_log import log as logging from oslo_utils import importutils from ironic.common","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG =","ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration()","compliance with the License. You may obtain # a copy of the License","status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration","\"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info","node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if","= driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node %(node)s","import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import","task: a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on","IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","msg = \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _ from","configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info","node.save() raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node %(node)s with error:","\" \"with the following target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object","task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info","the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","= target_raid_config LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \" \"with the following","= metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\"","= driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in","{'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create","logical drive found to delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as","creates a RAID configuration on the given node. :param task: a TaskManager instance.",":raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node target_raid_config =","status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration","\"\"\"Delete the RAID configuration. :param task: a TaskManager instance containing the node to","False, no non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config","on failure to execute step. \"\"\" node = task.node LOG.debug(\"OOB RAID delete_configuration invoked","whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False","'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on","containing the node to act on. :raises: NodeCleaningFailure, on failure to execute step.","delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False,","error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found","driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking","{'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info","'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration')","may # not use this file except in compliance with the License. You","in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys):","Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return","to delete this logical disks: %s\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","instance containing the node to act on. :raises: NodeCleaningFailure, on failure to execute","node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid","self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \"\"\"Delete the RAID configuration.","either express or implied. See the # License for the specific language governing","*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info =","% {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\"No logical drive found to delete","'%(message)s'.\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys):","node.raid_config = {} LOG.debug(\"Node %(uuid)s raid delete clean step is done.\", {'uuid': node.uuid})","get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg,","self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \"Clean step","this file except in compliance with the License. You may obtain # a","raise exception.NodeCleaningFailure( \"Clean step delete_configuration failed \" \"on node %(node)s with error: %(err)s\"","deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else:","self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0,","_set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed for node %(node)s. \" \"Message:","or implied. See the # License for the specific language governing permissions and","limitations # under the License. \"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib","LOG.debug(\"Calling OOB RAID create_configuration for node %(node)s \" \"with the following target RAID","import states from ironic.conductor import utils as manager_utils from ironic import conf from","configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration","raise exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node %(node)s with error: %(err)s\"","no non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is","= importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self): \"\"\"Return","*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info","import utils as manager_utils from ironic import conf from ironic.drivers import base from","step. \"\"\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info =","iLO5 RAID specific methods \"\"\" from ironic_lib import metrics_utils from oslo_log import log","( 'This specifies whether to create the root volume. ' 'Defaults to `True`.'","found to delete on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation","_pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info","# Raid configuration failed msg = (\"Unable to delete this logical disks: %s\"","import raid from ironic.common import states from ironic.conductor import utils as manager_utils from","states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration(","import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF =","on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND,","'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': (","'This specifies whether to create the root volume. ' 'Defaults to `True`.' ),","raid create clean step is done.\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job","importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid","def get_properties(self): \"\"\"Return the properties of the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task,","volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': (","Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return","failed msg = \"Unable to create raid\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info","following target RAID configuration: %(target)s\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try:","'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def","\"\"\"Create a RAID configuration on a bare metal using agent ramdisk. This method","self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration","= task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] =","% {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to","to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False }","ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common","ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \"\"\"Implementation of OOB RAIDInterface for iLO5.\"\"\" def get_properties(self):","from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as","on node %(node)s\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to","'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes. '","OR CONDITIONS OF ANY KIND, either express or implied. See the # License","OOB RAID create_configuration for node %(node)s \" \"with the following target RAID configuration:","raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\"Node %(uuid)s raid create","obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","node to act on. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node","non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \"\"\" node = task.node","except ilo_error.IloError as ilo_exception: operation = (_(\"Failed to create raid configuration on node","the interface.\"\"\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\"RAID configuration job failed","# Licensed under the Apache License, Version 2.0 (the \"License\"); you may #","= True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info","This method creates a RAID configuration on the given node. :param task: a","to delete raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')","ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS =","License. \"\"\" iLO5 RAID specific methods \"\"\" from ironic_lib import metrics_utils from oslo_log","else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume':","exception.NodeCleaningFailure( \"Clean step create_configuration failed \" \"on node %(node)s with error: %(err)s\" %","raid configuration on node %s\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =","specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required':","non-root volumes. ' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self,","raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\"Calling OOB RAID","error: %(err)s\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation =","'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info","@base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the","Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes: If True,","metal using agent ramdisk. This method creates a RAID configuration on the given","ironic.common import raid from ironic.common import states from ironic.conductor import utils as manager_utils","the node to act on. :raises: NodeCleaningFailure, on failure to execute step. \"\"\"","\"Clean step create_configuration failed \" \"on node %(node)s with error: %(err)s\" % {'node':","node. :param task: a TaskManager instance. :param create_root_volume: If True, a root volume","node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = (\"Unable to","and limitations # under the License. \"\"\" iLO5 RAID specific methods \"\"\" from","from ironic.common import states from ironic.conductor import utils as manager_utils from ironic import","'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg"],"string":"[\n \"# Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid')\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"# Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if\",\n \"on failure to execute step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node,\",\n \"self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This\",\n \"\\\"with the following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object =\",\n \"Default is True. :param create_nonroot_volumes: If True, non-root volumes are created. If False,\",\n \"is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are created.\",\n \"is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to be\",\n \"Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']):\",\n \":param task: a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure,\",\n \"RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid\",\n \"'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False)\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); you may # not use this file\",\n \"*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info =\",\n \"the License. You may obtain # a copy of the License at #\",\n \"execute step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info\",\n \"may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"= driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete\",\n \"= task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def\",\n \"msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for\",\n \"'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create raid configuration\",\n \"\\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"@METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create\",\n \"task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description':\",\n \"created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are created. If\",\n \"found to be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure,\",\n \"Raid configuration failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot',\",\n \"on the given node. :param task: a TaskManager instance. :param create_root_volume: If True,\",\n \"to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise\",\n \"\\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils\",\n \"with the License. You may obtain # a copy of the License at\",\n \"raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values(\",\n \"utils as manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules\",\n \"for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task,\",\n \"If False, no non-root volumes are created. Default is True. :raises: MissingParameterValue, if\",\n \"`True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a\",\n \"root volume is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes\",\n \"class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties\",\n \"(_(\\\"Failed to create raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot',\",\n \"driver_internal_info node.save() else: # Raid configuration failed msg = \\\"Unable to create raid\\\"\",\n \"raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\",\",\n \"disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf\",\n \"'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf =\",\n \"# Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid')\",\n \"= (\\\"Unable to delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress',\",\n \"\\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError:\",\n \"use this file except in compliance with the License. You may obtain #\",\n \"Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to\",\n \"to create raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"RAID configuration. :param task: a TaskManager instance containing the node to act on.\",\n \"not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\",\",\n \"= ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'):\",\n \"exc): LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid,\",\n \"ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task:\",\n \"BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration\",\n \"%(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def\",\n \"is distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF\",\n \"a RAID configuration on a bare metal using agent ramdisk. This method creates\",\n \"LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message':\",\n \"interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed for\",\n \"LOG.info(\\\"No logical drive found to delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError\",\n \"implied. See the # License for the specific language governing permissions and limitations\",\n \"\\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task,\",\n \"step create_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid,\",\n \"except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on node %(node)s\\\", {'node': node.uuid})\",\n \"the RAID configuration. :param task: a TaskManager instance containing the node to act\",\n \"was found to be empty after skipping root volume and/or non-root volumes. :raises:\",\n \"create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root volumes are\",\n \"create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node\",\n \"<reponame>armohamm/ironic # Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under\",\n \"is created during RAID configuration. Otherwise, no root volume is created. Default is\",\n \"driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating\",\n \"drive found to delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception:\",\n \"LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class\",\n \"logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _\",\n \"raid from ironic.common import states from ironic.conductor import utils as manager_utils from ironic\",\n \"create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare metal using agent ramdisk.\",\n \"task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info =\",\n \"you may # not use this file except in compliance with the License.\",\n \"in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else:\",\n \"= raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB\",\n \"driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info\",\n \"not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done,\",\n \"node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as\",\n \"% raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure(\",\n \"task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager instance containing the node\",\n \"specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import log as logging\",\n \"KIND, either express or implied. See the # License for the specific language\",\n \"the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from\",\n \"node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete\",\n \"properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration\",\n \"( 'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.'\",\n \"updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s\",\n \"file except in compliance with the License. You may obtain # a copy\",\n \"'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg\",\n \"%s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation,\",\n \"\\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = \\\"Unable to\",\n \"2018 Hewlett Packard Enterprise Development LP # # Licensed under the Apache License,\",\n \"root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\"\",\n \"node.save() else: # Raid configuration failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values(\",\n \"to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create\",\n \"None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task,\",\n \"driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info =\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {}\",\n \"keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts =\",\n \"RAID configuration on a bare metal using agent ramdisk. This method creates a\",\n \"= driver_internal_info node.save() else: # Raid configuration failed msg = (\\\"Unable to delete\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key\",\n \"OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return\",\n \"if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\", {'uuid':\",\n \"'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\"\",\n \"create raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY\",\n \"MissingParameterValue, if node.target_raid_config is missing or was found to be empty after skipping\",\n \"= conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB\",\n \"task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid':\",\n \"import metrics_utils from oslo_log import log as logging from oslo_utils import importutils from\",\n \"the # License for the specific language governing permissions and limitations # under\",\n \"for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def\",\n \"_prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step ==\",\n \"'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={\",\n \"self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether\",\n \"Development LP # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"volume is created during RAID configuration. Otherwise, no root volume is created. Default\",\n \"node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config']\",\n \"to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node\",\n \"a RAID configuration on the given node. :param task: a TaskManager instance. :param\",\n \"'create_root_volume': { 'description': ( 'This specifies whether to create the root volume. '\",\n \"task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object\",\n \"You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"= driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in\",\n \"for node %(node)s \\\" \\\"with the following target RAID configuration: %(target)s\\\", {'node': node.uuid,\",\n \"LP # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: #\",\n \"log as logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n\",\n \"raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation\",\n \"no root volume is created. Default is True. :param create_nonroot_volumes: If True, non-root\",\n \"LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object =\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo\",\n \"states from ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers\",\n \"task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value(\",\n \"failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg})\",\n \"= task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info\",\n \"clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"== 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step')\",\n \"%(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to\",\n \"in compliance with the License. You may obtain # a copy of the\",\n \"ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF\",\n \"volumes are created. If False, no non-root volumes are created. Default is True.\",\n \"True, non-root volumes are created. If False, no non-root volumes are created. Default\",\n \"node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress')\",\n \"), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID\",\n \"delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try:\",\n \"def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\"\",\n \"self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0,\",\n \"'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self,\",\n \"deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task,\",\n \"try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task,\",\n \"\\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import\",\n \"logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"{'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete raid configuration\",\n \"ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config\",\n \"'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed\",\n \"False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for\",\n \"create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare metal using\",\n \"ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common\",\n \"METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for\",\n \"node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking\",\n \"with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation\",\n \"with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive\",\n \"to delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation =\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"%s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation,\",\n \"'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\"\",\n \"task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key]\",\n \"(\\\"Unable to delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',\",\n \"else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not\",\n \"the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': {\",\n \"agent ramdisk. This method creates a RAID configuration on the given node. :param\",\n \"'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status\",\n \"driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node)\",\n \"is missing or was found to be empty after skipping root volume and/or\",\n \":param task: a TaskManager instance. :param create_root_volume: If True, a root volume is\",\n \"from ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers import\",\n \"def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step\",\n \"empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to\",\n \"ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config\",\n \"%s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in\",\n \"= task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save()\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception')\",\n \"node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node\",\n \"keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info\",\n \"True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for\",\n \"\\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info\",\n \"node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info =\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may # not\",\n \"invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: #\",\n \"'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed\",\n \"task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key\",\n \"is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under the Apache\",\n \"2.0 (the \\\"License\\\"); you may # not use this file except in compliance\",\n \"'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the\",\n \"Raid configuration failed msg = (\\\"Unable to delete this logical disks: %s\\\" %\",\n \"raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task,\",\n \"node.target_raid_config is missing or was found to be empty after skipping root volume\",\n \"'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies\",\n \"raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value(\",\n \"create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False } })\",\n \"task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration\",\n \"= task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save()\",\n \"task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare metal using agent\",\n \"ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on node %(node)s\\\", {'node': node.uuid}) except\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\",\n \"key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task,\",\n \"@base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager\",\n \"'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on\",\n \"License, Version 2.0 (the \\\"License\\\"); you may # not use this file except\",\n \"node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress,\",\n \"operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param\",\n \"a root volume is created during RAID configuration. Otherwise, no root volume is\",\n \"driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save()\",\n \"from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS\",\n \"created during RAID configuration. Otherwise, no root volume is created. Default is True.\",\n \"# Raid configuration failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress',\",\n \"after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute\",\n \"configuration failed msg = (\\\"Unable to delete this logical disks: %s\\\" % raid_conf['logical_disks'])\",\n \"or was found to be empty after skipping root volume and/or non-root volumes.\",\n \"create_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err':\",\n \"given node. :param task: a TaskManager instance. :param create_root_volume: If True, a root\",\n \"this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info =\",\n \"}, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes.\",\n \"_set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False\",\n \"from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__)\",\n \"key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task,\",\n \"{'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: #\",\n \"(_(\\\"Failed to delete raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',\",\n \"msg, exc): LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node':\",\n \"ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config)\",\n \"{'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on\",\n \"delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"method creates a RAID configuration on the given node. :param task: a TaskManager\",\n \"volumes. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node target_raid_config\",\n \"checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid\",\n \"import _ from ironic.common import raid from ironic.common import states from ironic.conductor import\",\n \"_set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True\",\n \"as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error =\",\n \"node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task,\",\n \"using agent ramdisk. This method creates a RAID configuration on the given node.\",\n \"task: a TaskManager instance. :param create_root_volume: If True, a root volume is created\",\n \"{'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: #\",\n \"updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s\",\n \"if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is\",\n \"on. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"\\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node':\",\n \"try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task,\",\n \"RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES\",\n \"the following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node)\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"import log as logging from oslo_utils import importutils from ironic.common import exception from\",\n \"RAID configuration on the given node. :param task: a TaskManager instance. :param create_root_volume:\",\n \"step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is\",\n \"configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT\",\n \"'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on node %(node)s\\\",\",\n \"ironic.common.i18n import _ from ironic.common import raid from ironic.common import states from ironic.conductor\",\n \"= (_(\\\"Failed to delete raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress',\",\n \"node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete raid configuration on\",\n \"`True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to\",\n \"node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid\",\n \"msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:\",\n \"ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean step\",\n \"{} LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task,\",\n \":param create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root volumes\",\n \"driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:\",\n \"LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress',\",\n \"created. If False, no non-root volumes are created. Default is True. :raises: MissingParameterValue,\",\n \"= (_(\\\"Failed to create raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress',\",\n \"return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration()\",\n \"driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s\",\n \"is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf)\",\n \":raises: MissingParameterValue, if node.target_raid_config is missing or was found to be empty after\",\n \"), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create\",\n \"True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found to be empty\",\n \"import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__)\",\n \"ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create raid configuration on node %s\\\")\",\n \"len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\", {'uuid': node.uuid})\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may\",\n \"driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\" \\\"with the\",\n \"failure to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for\",\n \"except in compliance with the License. You may obtain # a copy of\",\n \"return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = (\",\n \"methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import log as logging from\",\n \"and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node =\",\n \"target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling\",\n \"as ilo_exception: operation = (_(\\\"Failed to create raid configuration on node %s\\\") %\",\n \"for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self,\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common\",\n \"driver_internal_info node.save() else: # Raid configuration failed msg = (\\\"Unable to delete this\",\n \"import exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common import\",\n \"\\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the\",\n \"def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] =\",\n \"'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True):\",\n \"done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node\",\n \"task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration\",\n \"a TaskManager instance. :param create_root_volume: If True, a root volume is created during\",\n \"'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf =\",\n \"Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of\",\n \"RAID configuration. Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes:\",\n \"node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration\",\n \"act on. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT #\",\n \"node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task,\",\n \"RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import log as\",\n \"def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare metal\",\n \"a bare metal using agent ramdisk. This method creates a RAID configuration on\",\n \"driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node %(node)s with\",\n \"failure to execute step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume,\",\n \"in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts\",\n \"conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface\",\n \"# not use this file except in compliance with the License. You may\",\n \"self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration\",\n \"task, msg, exc): LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\",\",\n \"step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed\",\n \"\\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except\",\n \"as manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules import\",\n \"task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key,\",\n \"NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config(\",\n \"# License for the specific language governing permissions and limitations # under the\",\n \"raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid\",\n \"non-root volumes are created. If False, no non-root volumes are created. Default is\",\n \"if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot')\",\n \"states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if\",\n \"configuration. Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes: If\",\n \"create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure(\",\n \"ironic.common import states from ironic.conductor import utils as manager_utils from ironic import conf\",\n \"ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete raid configuration on node %s\\\")\",\n \"If True, a root volume is created during RAID configuration. Otherwise, no root\",\n \"raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean\",\n \"states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task,\",\n \"configuration failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"Version 2.0 (the \\\"License\\\"); you may # not use this file except in\",\n \"self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf\",\n \"else: # Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config))\",\n \"\\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc):\",\n \"node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\" \\\"with\",\n \"specifies whether to create the root volume. ' 'Defaults to `True`.' ), 'required':\",\n \"ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed for node %(node)s.\",\n \"configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node,\",\n \"= logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface):\",\n \"non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing\",\n \"task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config\",\n \"return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed for node\",\n \"} }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a\",\n \"\\\"License\\\"); you may # not use this file except in compliance with the\",\n \"else: # Raid configuration failed msg = (\\\"Unable to delete this logical disks:\",\n \"delete raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" %\",\n \"node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No\",\n \"root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description':\",\n \"import importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common import\",\n \"'description': ( 'This specifies whether to create the root volume. ' 'Defaults to\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); you may # not use this\",\n \"def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None)\",\n \"# Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']):\",\n \"task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed\",\n \"node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create raid\",\n \"= node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\"\",\n \"= {} LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values(\",\n \"_ from ironic.common import raid from ironic.common import states from ironic.conductor import utils\",\n \"not use this file except in compliance with the License. You may obtain\",\n \"driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info\",\n \"}) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare\",\n \"driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the\",\n \"len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\", {'uuid':\",\n \"Raid configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config\",\n \"self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': {\",\n \"target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: #\",\n \"done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else:\",\n \"for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg\",\n \"ironic.conductor import utils as manager_utils from ironic import conf from ironic.drivers import base\",\n \"key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step):\",\n \"NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration\",\n \"License for the specific language governing permissions and limitations # under the License.\",\n \"from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid from\",\n \"node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail')\",\n \"raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid\",\n \"node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node %(node)s with error:\",\n \"= deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress')\",\n \"clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"msg = (\\\"Unable to delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task,\",\n \"are created. If False, no non-root volumes are created. Default is True. :raises:\",\n \"on a bare metal using agent ramdisk. This method creates a RAID configuration\",\n \"task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info\",\n \"during RAID configuration. Otherwise, no root volume is created. Default is True. :param\",\n \"configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT\",\n \"'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = \\\"Unable\",\n \"create_configuration for node %(node)s \\\" \\\"with the following target RAID configuration: %(target)s\\\", {'node':\",\n \"if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean step is\",\n \"whether to create the root volume. ' 'Defaults to `True`.' ), 'required': False\",\n \"If True, non-root volumes are created. If False, no non-root volumes are created.\",\n \"instance. :param create_root_volume: If True, a root volume is created during RAID configuration.\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the #\",\n \"driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys:\",\n \"driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s with\",\n \"root volume is created during RAID configuration. Otherwise, no root volume is created.\",\n \"driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info =\",\n \":raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID\",\n \":param create_root_volume: If True, a root volume is created during RAID configuration. Otherwise,\",\n \"task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key\",\n \"= \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root\",\n \"governing permissions and limitations # under the License. \\\"\\\"\\\" iLO5 RAID specific methods\",\n \"def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager instance containing\",\n \"Enterprise Development LP # # Licensed under the Apache License, Version 2.0 (the\",\n \"OF ANY KIND, either express or implied. See the # License for the\",\n \"= driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task,\",\n \"@METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a\",\n \"on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def\",\n \"target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if\",\n \"' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This\",\n \"target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\" \\\"with the following target\",\n \"task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if\",\n \"node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on node\",\n \"exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common import states\",\n \"import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG\",\n \"TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on failure to\",\n \"if node.target_raid_config is missing or was found to be empty after skipping root\",\n \"# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\",\n \"% node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception)\",\n \"language governing permissions and limitations # under the License. \\\"\\\"\\\" iLO5 RAID specific\",\n \"task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key]\",\n \"on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID\",\n \"volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node\",\n \"create the root volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes':\",\n \"from ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from\",\n \"task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self,\",\n \"the specific language governing permissions and limitations # under the License. \\\"\\\"\\\" iLO5\",\n \"True, a root volume is created during RAID configuration. Otherwise, no root volume\",\n \"(the \\\"License\\\"); you may # not use this file except in compliance with\",\n \"failed msg = (\\\"Unable to delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values(\",\n \"from ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import\",\n \"abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager instance\",\n \"( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean step\",\n \"False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root\",\n \"of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\"\",\n \"TaskManager instance. :param create_root_volume: If True, a root volume is created during RAID\",\n \"CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"ironic_lib import metrics_utils from oslo_log import log as logging from oslo_utils import importutils\",\n \"node %(node)s \\\" \\\"with the following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target':\",\n \"= msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in\",\n \"delete_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err':\",\n \"{'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress,\",\n \"= node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status\",\n \"under the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils\",\n \"failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error =\",\n \"created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was found\",\n \"ilo_exception: operation = (_(\\\"Failed to create raid configuration on node %s\\\") % node.uuid)\",\n \"in keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys):\",\n \"progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: #\",\n \"driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done, updating\",\n \"%(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed\",\n \"%(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical\",\n \"for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self,\",\n \"volumes. ' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task,\",\n \"msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create raid configuration on\",\n \"configuration job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg})\",\n \"for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid\",\n \"RAID create_configuration for node %(node)s \\\" \\\"with the following target RAID configuration: %(target)s\\\",\",\n \"to act on. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node =\",\n \"License. You may obtain # a copy of the License at # #\",\n \"%s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR\",\n \"for the specific language governing permissions and limitations # under the License. \\\"\\\"\\\"\",\n \"missing or was found to be empty after skipping root volume and/or non-root\",\n \"= driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node %(node)s\",\n \"# under the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import\",\n \"the given node. :param task: a TaskManager instance. :param create_root_volume: If True, a\",\n \"to execute step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes)\",\n \"done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else:\",\n \"= False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info\",\n \"oslo_log import log as logging from oslo_utils import importutils from ironic.common import exception\",\n \"volume is created. Default is True. :param create_nonroot_volumes: If True, non-root volumes are\",\n \"raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete\",\n \"ANY KIND, either express or implied. See the # License for the specific\",\n \"delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager instance containing the\",\n \"the non-root volumes. ' 'Defaults to `True`.' ), 'required': False } }) def\",\n \"'This specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ),\",\n \"# Copyright 2018 Hewlett Packard Enterprise Development LP # # Licensed under the\",\n \"step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\", node.uuid)\",\n \"Hewlett Packard Enterprise Development LP # # Licensed under the Apache License, Version\",\n \"create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration on a bare metal using agent ramdisk. This\",\n \"step delete_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid,\",\n \"delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"\\\"on node %(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError\",\n \"be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure\",\n \"node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task):\",\n \"ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not\",\n \"msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete on node %(node)s\\\", {'node':\",\n \"'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg =\",\n \"raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task,\",\n \"except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete raid configuration on node\",\n \"%(uuid)s raid delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot',\",\n \"= ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'):\",\n \"True. :param create_nonroot_volumes: If True, non-root volumes are created. If False, no non-root\",\n \"task.node.driver_internal_info = driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts)\",\n \"driver_internal_info task.node.save() def _prepare_for_read_raid(self, task, raid_step): deploy_opts = deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT)\",\n \"to create the root volume. ' 'Defaults to `True`.' ), 'required': False },\",\n \"%(node)s \\\" \\\"with the following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config})\",\n \"LOG.debug(\\\"Node %(uuid)s raid create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress',\",\n \"keys: driver_internal_info[key] = True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info\",\n \"task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def\",\n \"to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': ( 'This specifies whether\",\n \"job failed for node %(node)s. \\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error\",\n \"'required': False } }) def create_configuration(self, task, create_root_volume=True, create_nonroot_volumes=True): \\\"\\\"\\\"Create a RAID configuration\",\n \"deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task,\",\n \"argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the root volume.\",\n \"%(uuid)s raid create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot',\",\n \"raid delete clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"%(node)s with error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception:\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); you may # not use\",\n \"WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See\",\n \"{ 'description': ( 'This specifies whether to create the root volume. ' 'Defaults\",\n \"conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common\",\n \"configuration on the given node. :param task: a TaskManager instance. :param create_root_volume: If\",\n \"'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = (\\\"Unable\",\n \"' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self, task, create_root_volume=True,\",\n \"as logging from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import\",\n \"to be empty after skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on\",\n \"= ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create clean\",\n \"execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked for node %s.\\\",\",\n \"= ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean\",\n \"as ilo_exception: operation = (_(\\\"Failed to delete raid configuration on node %s\\\") %\",\n \"See the # License for the specific language governing permissions and limitations #\",\n \"volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or\",\n \"permissions and limitations # under the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\"\",\n \"importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the\",\n \"node.save() else: # Raid configuration failed msg = (\\\"Unable to delete this logical\",\n \"= driver_internal_info node.save() else: # Raid configuration failed msg = \\\"Unable to create\",\n \"from oslo_log import log as logging from oslo_utils import importutils from ironic.common import\",\n \"bare metal using agent ramdisk. This method creates a RAID configuration on the\",\n \"Packard Enterprise Development LP # # Licensed under the Apache License, Version 2.0\",\n \"configuration is done, updating raid_config raid_conf = ilo_object.read_raid_configuration() if not len(raid_conf['logical_disks']): node.raid_config =\",\n \"raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean\",\n \"%(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to delete raid\",\n \"create_root_volume: If True, a root volume is created during RAID configuration. Otherwise, no\",\n \"%(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF\",\n \"% node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception)\",\n \"self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step\",\n \"operation = (_(\\\"Failed to delete raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task,\",\n \"a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on failure\",\n \"common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS = metrics_utils.get_metrics_logger(__name__) ilo_error\",\n \"'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults to\",\n \"express or implied. See the # License for the specific language governing permissions\",\n \"are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is missing or was\",\n \"{ 'description': ( 'This specifies whether to create the non-root volumes. ' 'Defaults\",\n \"an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"# a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"CONDITIONS OF ANY KIND, either express or implied. See the # License for\",\n \"ramdisk. This method creates a RAID configuration on the given node. :param task:\",\n \"def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] =\",\n \"skipping root volume and/or non-root volumes. :raises: NodeCleaningFailure, on failure to execute step.\",\n \"if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is\",\n \"done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node\",\n \"node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration')\",\n \"metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def\",\n \"configuration on a bare metal using agent ramdisk. This method creates a RAID\",\n \"ilo_exception: operation = (_(\\\"Failed to delete raid configuration on node %s\\\") % node.uuid)\",\n \"in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else:\",\n \"from ironic.common import raid from ironic.common import states from ironic.conductor import utils as\",\n \"manager_utils from ironic import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils\",\n \"checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid\",\n \"specific language governing permissions and limitations # under the License. \\\"\\\"\\\" iLO5 RAID\",\n \"is True. :param create_nonroot_volumes: If True, non-root volumes are created. If False, no\",\n \"is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save()\",\n \"else: # Raid configuration failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task,\",\n \"\\\" \\\"Message: '%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self,\",\n \"not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration is done,\",\n \"'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies\",\n \"from ironic.common.i18n import _ from ironic.common import raid from ironic.common import states from\",\n \"operation = (_(\\\"Failed to create raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task,\",\n \"configuration. :param task: a TaskManager instance containing the node to act on. :raises:\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to\",\n \"iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self,\",\n \"create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for\",\n \"RAID delete_configuration invoked for node %s.\\\", node.uuid) driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node)\",\n \"metrics_utils from oslo_log import log as logging from oslo_utils import importutils from ironic.common\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG =\",\n \"ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration()\",\n \"compliance with the License. You may obtain # a copy of the License\",\n \"status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return states.CLEANWAIT else: # Raid configuration\",\n \"\\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info\",\n \"node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking status if\",\n \"= driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s\",\n \"import conf from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import\",\n \"task: a TaskManager instance containing the node to act on. :raises: NodeCleaningFailure, on\",\n \"IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"from oslo_utils import importutils from ironic.common import exception from ironic.common.i18n import _ from\",\n \"configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info\",\n \"node.save() raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s with error:\",\n \"\\\" \\\"with the following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object\",\n \"task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = False task.node.driver_internal_info\",\n \"the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"= target_raid_config LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\" \\\"with the following\",\n \"= metrics_utils.get_metrics_logger(__name__) ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\"\",\n \"= driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in\",\n \"{'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create\",\n \"logical drive found to delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as\",\n \"creates a RAID configuration on the given node. :param task: a TaskManager instance.\",\n \":raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node target_raid_config =\",\n \"status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return states.CLEANWAIT else: # Raid configuration\",\n \"\\\"\\\"\\\"Delete the RAID configuration. :param task: a TaskManager instance containing the node to\",\n \"False, no non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config\",\n \"on failure to execute step. \\\"\\\"\\\" node = task.node LOG.debug(\\\"OOB RAID delete_configuration invoked\",\n \"whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False\",\n \"'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on\",\n \"containing the node to act on. :raises: NodeCleaningFailure, on failure to execute step.\",\n \"delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False,\",\n \"error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found\",\n \"driver_internal_info = node.driver_internal_info ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration in progress, checking\",\n \"{'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info\",\n \"'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration')\",\n \"may # not use this file except in compliance with the License. You\",\n \"in keys: driver_internal_info[key] = False task.node.driver_internal_info = driver_internal_info task.node.save() def _pop_driver_internal_values(self, task, *keys):\",\n \"Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid') return\",\n \"to delete this logical disks: %s\\\" % raid_conf['logical_disks']) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"instance containing the node to act on. :raises: NodeCleaningFailure, on failure to execute\",\n \"node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid\",\n \"self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def delete_configuration(self, task): \\\"\\\"\\\"Delete the RAID configuration.\",\n \"either express or implied. See the # License for the specific language governing\",\n \"*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info[key] = True task.node.driver_internal_info =\",\n \"% {'node': node.uuid, 'err': msg}) except ilo_error.IloLogicalDriveNotFoundError: LOG.info(\\\"No logical drive found to delete\",\n \"'%(message)s'.\\\", {'node': task.node.uuid, 'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys):\",\n \"node.raid_config = {} LOG.debug(\\\"Node %(uuid)s raid delete clean step is done.\\\", {'uuid': node.uuid})\",\n \"get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg,\",\n \"self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() raise exception.NodeCleaningFailure( \\\"Clean step\",\n \"this file except in compliance with the License. You may obtain # a\",\n \"raise exception.NodeCleaningFailure( \\\"Clean step delete_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\"\",\n \"deploy_utils.build_agent_options(task.node) task.driver.boot.prepare_ramdisk(task, deploy_opts) manager_utils.node_power_action(task, states.REBOOT) if raid_step == 'create_raid': self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else:\",\n \"self._set_driver_internal_true_value( task, 'ilo_raid_create_in_progress') else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0,\",\n \"_set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed for node %(node)s. \\\" \\\"Message:\",\n \"or implied. See the # License for the specific language governing permissions and\",\n \"limitations # under the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib\",\n \"LOG.debug(\\\"Calling OOB RAID create_configuration for node %(node)s \\\" \\\"with the following target RAID\",\n \"import states from ironic.conductor import utils as manager_utils from ironic import conf from\",\n \"configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try: # Raid configuration\",\n \"raise exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\"\",\n \"no non-root volumes are created. Default is True. :raises: MissingParameterValue, if node.target_raid_config is\",\n \"= importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self): \\\"\\\"\\\"Return\",\n \"*keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info = driver_internal_info\",\n \"import utils as manager_utils from ironic import conf from ironic.drivers import base from\",\n \"step. \\\"\\\"\\\" node = task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info =\",\n \"iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log import log\",\n \"( 'This specifies whether to create the root volume. ' 'Defaults to `True`.'\",\n \"found to delete on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation\",\n \"_pop_driver_internal_values(self, task, *keys): driver_internal_info = task.node.driver_internal_info for key in keys: driver_internal_info.pop(key, None) task.node.driver_internal_info\",\n \"# Raid configuration failed msg = (\\\"Unable to delete this logical disks: %s\\\"\",\n \"import raid from ironic.common import states from ironic.conductor import utils as manager_utils from\",\n \"states.CLEANWAIT else: # Raid configuration is done, updating raid_config raid_conf = ( ilo_object.read_raid_configuration(\",\n \"import deploy_utils from ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF =\",\n \"on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': (\",\n \"'This specifies whether to create the root volume. ' 'Defaults to `True`.' ),\",\n \"raid create clean step is done.\\\", {'uuid': node.uuid}) self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job\",\n \"importutils from ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid\",\n \"def get_properties(self): \\\"\\\"\\\"Return the properties of the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task,\",\n \"volume. ' 'Defaults to `True`.' ), 'required': False }, 'create_nonroot_volumes': { 'description': (\",\n \"Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_delete_in_progress'): ilo_object.delete_raid_configuration() self._prepare_for_read_raid(task, 'delete_raid') return\",\n \"failed msg = \\\"Unable to create raid\\\" self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info\",\n \"following target RAID configuration: %(target)s\\\", {'node': node.uuid, 'target': target_raid_config}) ilo_object = ilo_common.get_ilo_object(node) try:\",\n \"'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() self._set_clean_failed(task, operation, ilo_exception) @METRICS.timer('Ilo5RAID.delete_configuration') @base.clean_step(priority=0, abortable=False) def\",\n \"\\\"\\\"\\\"Create a RAID configuration on a bare metal using agent ramdisk. This method\",\n \"self._pop_driver_internal_values( task, 'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration\",\n \"= task.node target_raid_config = raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] =\",\n \"% {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to\",\n \"to create the non-root volumes. ' 'Defaults to `True`.' ), 'required': False }\",\n \"ironic.common import exception from ironic.common.i18n import _ from ironic.common import raid from ironic.common\",\n \"ilo_error = importutils.try_import('proliantutils.exception') class Ilo5RAID(base.RAIDInterface): \\\"\\\"\\\"Implementation of OOB RAIDInterface for iLO5.\\\"\\\"\\\" def get_properties(self):\",\n \"from ironic.drivers import base from ironic.drivers.modules import deploy_utils from ironic.drivers.modules.ilo import common as\",\n \"on node %(node)s\\\", {'node': node.uuid}) except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to\",\n \"'create_nonroot_volumes': { 'description': ( 'This specifies whether to create the non-root volumes. '\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. See the # License\",\n \"OOB RAID create_configuration for node %(node)s \\\" \\\"with the following target RAID configuration:\",\n \"raid_conf = ( ilo_object.read_raid_configuration( raid_config=target_raid_config)) if len(raid_conf['logical_disks']): raid.update_raid_info(node, raid_conf) LOG.debug(\\\"Node %(uuid)s raid create\",\n \"obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"node to act on. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node\",\n \"non-root volumes. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\" node = task.node\",\n \"except ilo_error.IloError as ilo_exception: operation = (_(\\\"Failed to create raid configuration on node\",\n \"the interface.\\\"\\\"\\\" return ilo_common.REQUIRED_PROPERTIES def _set_clean_failed(self, task, msg, exc): LOG.error(\\\"RAID configuration job failed\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may #\",\n \"= True task.node.driver_internal_info = driver_internal_info task.node.save() def _set_driver_internal_false_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info\",\n \"This method creates a RAID configuration on the given node. :param task: a\",\n \"to delete raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step')\",\n \"ironic.drivers.modules.ilo import common as ilo_common LOG = logging.getLogger(__name__) CONF = conf.CONF METRICS =\",\n \"License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from ironic_lib import metrics_utils from oslo_log\",\n \"else: self._set_driver_internal_true_value( task, 'ilo_raid_delete_in_progress') self._set_driver_internal_true_value(task, 'cleaning_reboot') self._set_driver_internal_false_value(task, 'skip_current_clean_step') @METRICS.timer('Ilo5RAID.create_configuration') @base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume':\",\n \"exception.NodeCleaningFailure( \\\"Clean step create_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" %\",\n \"raid configuration on node %s\\\") % node.uuid) self._pop_driver_internal_values(task, 'ilo_raid_delete_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info =\",\n \"specifies whether to create the non-root volumes. ' 'Defaults to `True`.' ), 'required':\",\n \"non-root volumes. ' 'Defaults to `True`.' ), 'required': False } }) def create_configuration(self,\",\n \"raid.filter_target_raid_config( node, create_root_volume=create_root_volume, create_nonroot_volumes=create_nonroot_volumes) driver_internal_info = node.driver_internal_info driver_internal_info['target_raid_config'] = target_raid_config LOG.debug(\\\"Calling OOB RAID\",\n \"error: %(err)s\\\" % {'node': node.uuid, 'err': msg}) except ilo_error.IloError as ilo_exception: operation =\",\n \"'message': msg}) task.node.last_error = msg task.process_event('fail') def _set_driver_internal_true_value(self, task, *keys): driver_internal_info = task.node.driver_internal_info\",\n \"@base.clean_step(priority=0, abortable=False, argsinfo={ 'create_root_volume': { 'description': ( 'This specifies whether to create the\",\n \"Otherwise, no root volume is created. Default is True. :param create_nonroot_volumes: If True,\",\n \"metal using agent ramdisk. This method creates a RAID configuration on the given\",\n \"ironic.common import raid from ironic.common import states from ironic.conductor import utils as manager_utils\",\n \"the node to act on. :raises: NodeCleaningFailure, on failure to execute step. \\\"\\\"\\\"\",\n \"\\\"Clean step create_configuration failed \\\" \\\"on node %(node)s with error: %(err)s\\\" % {'node':\",\n \"node. :param task: a TaskManager instance. :param create_root_volume: If True, a root volume\",\n \"node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg = (\\\"Unable to\",\n \"and limitations # under the License. \\\"\\\"\\\" iLO5 RAID specific methods \\\"\\\"\\\" from\",\n \"from ironic.common import states from ironic.conductor import utils as manager_utils from ironic import\",\n \"'ilo_raid_create_in_progress', 'cleaning_reboot', 'skip_current_clean_step') node.driver_internal_info = driver_internal_info node.save() else: # Raid configuration failed msg\"\n]","isConversational":false}}},{"rowIdx":2595,"cells":{"ngram":{"kind":"list like","value":[") from ...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent,","...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH,",") from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( #","These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, )","( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION","CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent,","QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import","QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from","an extension. This should be imported along with the `simp` module. \"\"\" from","the `simp` module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct,","from ...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener,","bootstrap parts of an extension. This should be imported along with the `simp`","imports for bootstrap parts of an extension. This should be imported along with","module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD,","ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from","...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent,","QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events","TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, )","This should be imported along with the `simp` module. \"\"\" from ...base.bus import","( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES","as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION,","INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import","as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION,","Common Petronia imports for bootstrap parts of an extension. This should be imported","QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import (","import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO,","from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener, TARGET_ID_SYSTEM_SHUTDOWN,","...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener, TARGET_ID_SYSTEM_SHUTDOWN, )","PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import (","events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel,",") from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, )","as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION,","for bootstrap parts of an extension. This should be imported along with the","`simp` module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event,","( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just","ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant","import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener,","extension. This should be imported along with the `simp` module. \"\"\" from ...base.bus","with the `simp` module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority,","parts of an extension. This should be imported along with the `simp` module.","along with the `simp` module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup,","from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL,","( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent,","of an extension. This should be imported along with the `simp` module. \"\"\"","generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus","...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api","\"\"\" Common Petronia imports for bootstrap parts of an extension. This should be","are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from","bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import (","import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import","be imported along with the `simp` module. \"\"\" from ...base.bus import ( EventBus,","register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity,","DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION,","SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION,","just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import",") from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener,","imported along with the `simp` module. \"\"\" from ...base.bus import ( EventBus, ListenerRegistrar,","Petronia imports for bootstrap parts of an extension. This should be imported along","GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api","should be imported along with the `simp` module. \"\"\" from ...base.bus import (","\"\"\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD,","import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally","create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just bootstrap","QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import","REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener,","EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT,","ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import (","RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent,","...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are","EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES )","# These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener,","RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION,","from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These","NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just bootstrap events.","EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from","from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from"],"string":"[\n \") from ...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent,\",\n \"...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH,\",\n \") from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( #\",\n \"These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, )\",\n \"( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION\",\n \"CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent,\",\n \"QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import\",\n \"QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from\",\n \"an extension. This should be imported along with the `simp` module. \\\"\\\"\\\" from\",\n \"the `simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct,\",\n \"from ...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener,\",\n \"bootstrap parts of an extension. This should be imported along with the `simp`\",\n \"imports for bootstrap parts of an extension. This should be imported along with\",\n \"module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD,\",\n \"ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from\",\n \"...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent,\",\n \"QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events\",\n \"TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, )\",\n \"This should be imported along with the `simp` module. \\\"\\\"\\\" from ...base.bus import\",\n \"( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES\",\n \"as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION,\",\n \"INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import\",\n \"as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION,\",\n \"Common Petronia imports for bootstrap parts of an extension. This should be imported\",\n \"QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import (\",\n \"import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO,\",\n \"from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener, TARGET_ID_SYSTEM_SHUTDOWN,\",\n \"...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener, TARGET_ID_SYSTEM_SHUTDOWN, )\",\n \"PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import (\",\n \"events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel,\",\n \") from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, )\",\n \"as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION,\",\n \"for bootstrap parts of an extension. This should be imported along with the\",\n \"`simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event,\",\n \"( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just\",\n \"ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant\",\n \"import ( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener,\",\n \"extension. This should be imported along with the `simp` module. \\\"\\\"\\\" from ...base.bus\",\n \"with the `simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority,\",\n \"parts of an extension. This should be imported along with the `simp` module.\",\n \"along with the `simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup,\",\n \"from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL,\",\n \"( # These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent,\",\n \"of an extension. This should be imported along with the `simp` module. \\\"\\\"\\\"\",\n \"generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus\",\n \"...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api\",\n \"\\\"\\\"\\\" Common Petronia imports for bootstrap parts of an extension. This should be\",\n \"are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from\",\n \"bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import (\",\n \"import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import\",\n \"be imported along with the `simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus,\",\n \"register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity,\",\n \"DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION,\",\n \"SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION,\",\n \"just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import\",\n \") from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent, as_system_shutdown_finalize_listener,\",\n \"imported along with the `simp` module. \\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar,\",\n \"Petronia imports for bootstrap parts of an extension. This should be imported along\",\n \"GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api\",\n \"should be imported along with the `simp` module. \\\"\\\"\\\" from ...base.bus import (\",\n \"\\\"\\\"\\\" from ...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD,\",\n \"import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally\",\n \"create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just bootstrap\",\n \"QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import\",\n \"REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener,\",\n \"EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT,\",\n \"ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES ) from ...base.participant import (\",\n \"RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from ...core.shutdown.api import ( SystemShutdownEvent, as_system_shutdown_listener, SystemShutdownFinalizeEvent,\",\n \"...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These are\",\n \"EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH, QUEUE_EVENT_IO, QUEUE_EVENT_TYPES )\",\n \"# These are generally just bootstrap events. DisposeCompleteEvent, as_dispose_complete_listener, RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener,\",\n \"RequestDisposeEvent, as_request_dispose_listener, SystemStartedEvent, as_system_started_listener, ) from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION,\",\n \"from ...base.participant import ( create_singleton_identity, NOT_PARTICIPANT, ) from ...base.events import ( # These\",\n \"NOT_PARTICIPANT, ) from ...base.events import ( # These are generally just bootstrap events.\",\n \"EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from ...core.extensions.api import ANY_VERSION from\",\n \"from ...base.events.bus import ( EventProtectionModel, GLOBAL_EVENT_PROTECTION, INTERNAL_EVENT_PROTECTION, PRODUCE_EVENT_PROTECTION, CONSUME_EVENT_PROTECTION, REQUEST_EVENT_PROTECTION, RESPONSE_EVENT_PROTECTION, ) from\"\n]","isConversational":false}}},{"rowIdx":2596,"cells":{"ngram":{"kind":"list like","value":["\"<unknown device partition property %d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\")","== cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v,","= result.replace(\"FLOAT\", \"F\") return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel","\"to_string\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \"<error>\"","parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name","= result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result","itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except:","for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for","= OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\") (options, args) =","\"<error>\" if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}:","if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\"-\")","cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\"","pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\",","action=\"store_true\", help=\"don't print all device properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls):","info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info)","for v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name)","platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx","info_value = obj.get_info(info) except: info_value = \"<error>\" if (info_cls == cl.device_info and info_name","<error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short:","except: print(\"%s: <error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if","in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats","mf, itype) except: formats = \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown","itype) except: formats = \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel","result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\",","result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\",","in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device in","from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all","result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result formats = \", \".join( \"{}-{}\".format(","in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info)","= parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and","= cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in","result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result","\"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result","order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS: {}\\n\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf),","if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name,","= \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in","% info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform,","properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if","result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result formats = \",","v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for","str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result = result.replace(\"_INT\", \"\")","cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not options.short:","optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device","and info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except:","not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\"-\") print(device)","\"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\")","= \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\")","for device in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\")","else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result =","print all device properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name","\"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS: {}\\n\".format(","= getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \"<error>\" if (info_cls","getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \"<error>\" if (info_cls ==","if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [","parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\") (options, args) = parser.parse_args() def","info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls,","channel data type %d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result","property %d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\"","\"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\")","options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE,","#cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats =","args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\")","\"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {}","partition property %d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s:","if not info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value","\".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{}","info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition","all device properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in","formats = \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type","for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\": info =","cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\",","\"<unknown channel data type %d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\")","as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't","data type %d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result =","\"N\") result = result.replace(\"FLOAT\", \"F\") return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order,","= obj.get_info(info) except: info_value = \"<error>\" if (info_cls == cl.device_info and info_name ==","\"--short\", action=\"store_true\", help=\"don't print all device properties\") (options, args) = parser.parse_args() def print_info(obj,","print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if","print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not","options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if","print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]:","in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls, info_name)","sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls, info_name) try:","<filename>examples/dump-properties.py import pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\",","cl from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print","ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype","[ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try:","%d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\")","info_value = \"<error>\" if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value,","cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v in info_value])) else: try: print(f\"{info_name}:","cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype,","== \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property","result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result formats","\", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats)","OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\") (options, args) = parser.parse_args()","!= \"to_string\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value =","= result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result =","formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform","device properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)):","str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS: {}\\n\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats)) del","import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\")","result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return","print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short:","info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\": info","print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v in info_value]))","cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices():","print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY","try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\")","info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \"<error>\" if","]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf,","result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result","= result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result formats = \", \".join(","in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for platform","not info_name.startswith(\"_\") and info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value =","result.replace(\"FLOAT\", \"F\") return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order","return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type))","except: info_value = \"<error>\" if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and","options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for","(info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [","for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype)","cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else:","\"F\") return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"),","[ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v in info_value])) else: try:","{info_value}\") except: print(\"%s: <error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\")","result = result.replace(\"FLOAT\", \"F\") return result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown","platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device","info_name) try: info_value = obj.get_info(info) except: info_value = \"<error>\" if (info_cls == cl.device_info","isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v","and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device","= \"<error>\" if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)):","help=\"don't print all device properties\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for","OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\") (options,","#cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx,","\"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\")","cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for","channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS: {}\\n\".format( cl.mem_object_type.to_string(itype),","result formats = \", \".join( \"{}-{}\".format( cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for","in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]:","]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else: def","formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else: def str_chd_type(chdtype): result","cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [","info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for platform in","not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device])","= cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else: def str_chd_type(chdtype): result =","except: formats = \"<error>\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data","for iform in formats) print(\"{} {} FORMATS: {}\\n\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats)) del ctx","%d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" %","not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY,","try: info_value = obj.get_info(info) except: info_value = \"<error>\" if (info_cls == cl.device_info and","device partition property %d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\") except:","import pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\"-s\", \"--short\",","try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \"<error>\" else: def str_chd_type(chdtype):","for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D,","result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result = result.replace(\"_INT\", \"\") result","mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D","cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats","type %d>\") result = result.replace(\"_INT\", \"\") result = result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\",","def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name !=","print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform)","0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS: {}\\n\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats))","\"S\") result = result.replace(\"NORM\", \"N\") result = result.replace(\"FLOAT\", \"F\") return result formats =","obj.get_info(info) except: info_value = \"<error>\" if (info_cls == cl.device_info and info_name == \"PARTITION_TYPES_EXT\"","[ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats =","= cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result = result.replace(\"_INT\", \"\") result =","print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in","cl.channel_order.to_string(iform.channel_order, \"<unknown channel order 0x%x>\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\"{} {} FORMATS:","parser = OptionParser() parser.add_option(\"-s\", \"--short\", action=\"store_true\", help=\"don't print all device properties\") (options, args)","device in platform.get_devices(): if not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device,","= result.replace(\"UNSIGNED\", \"U\") result = result.replace(\"SIGNED\", \"S\") result = result.replace(\"NORM\", \"N\") result =","list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v in","else: try: print(f\"{info_name}: {info_value}\") except: print(\"%s: <error>\" % info_name) for platform in cl.get_platforms():","(options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not","cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown","if not options.short: print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx =","print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not","info_name != \"to_string\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value","print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\"_\") and info_name != \"to_string\":","def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \"<unknown channel data type %d>\") result = result.replace(\"_INT\",","and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for","{}\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \"<unknown device partition property %d>\") for v in info_value])) else:","print(\"%s: <error>\" % info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not","print(75*\"-\") print(device) if not options.short: print(75*\"-\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf","info_name) for platform in cl.get_platforms(): print(75*\"=\") print(platform) print(75*\"=\") if not options.short: print_info(platform, cl.platform_info)"],"string":"[\n \"\\\"<unknown device partition property %d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\")\",\n \"== cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v,\",\n \"= result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel\",\n \"\\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\"\",\n \"parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name\",\n \"= result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result\",\n \"itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except:\",\n \"for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for\",\n \"= OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args) =\",\n \"\\\"<error>\\\" if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}:\",\n \"if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\")\",\n \"cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\"\",\n \"pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\",\",\n \"action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls):\",\n \"info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info)\",\n \"for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name)\",\n \"platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx\",\n \"info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info and info_name\",\n \"<error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short:\",\n \"except: print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if\",\n \"in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats\",\n \"mf, itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown\",\n \"itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel\",\n \"result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\",\",\n \"result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\",\",\n \"in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device in\",\n \"from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all\",\n \"result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format(\",\n \"in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info)\",\n \"= parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and\",\n \"= cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in\",\n \"result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result\",\n \"\\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result\",\n \"order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf),\",\n \"if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name,\",\n \"= \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in\",\n \"% info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform,\",\n \"properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if\",\n \"result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\",\",\n \"v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for\",\n \"str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\")\",\n \"cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not options.short:\",\n \"optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device\",\n \"and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except:\",\n \"not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device)\",\n \"\\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\")\",\n \"= \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\")\",\n \"for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\")\",\n \"else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result =\",\n \"print all device properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name\",\n \"\\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format(\",\n \"= getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls\",\n \"getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls ==\",\n \"if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [\",\n \"parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args) = parser.parse_args() def\",\n \"info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls,\",\n \"channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result\",\n \"property %d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\"\",\n \"\\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\")\",\n \"options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE,\",\n \"#cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats =\",\n \"args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\")\",\n \"\\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {}\",\n \"partition property %d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s:\",\n \"if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value\",\n \"\\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{}\",\n \"info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition\",\n \"all device properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in\",\n \"formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type\",\n \"for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info =\",\n \"cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\",\",\n \"\\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\")\",\n \"as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't\",\n \"data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result =\",\n \"\\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order,\",\n \"= obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info and info_name ==\",\n \"\\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args) = parser.parse_args() def print_info(obj,\",\n \"print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if\",\n \"print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not\",\n \"options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if\",\n \"print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]:\",\n \"in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name)\",\n \"sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try:\",\n \"<filename>examples/dump-properties.py import pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\",\",\n \"cl from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print\",\n \"ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype\",\n \"[ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try:\",\n \"%d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\")\",\n \"info_value = \\\"<error>\\\" if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value,\",\n \"cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}:\",\n \"cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype,\",\n \"== \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property\",\n \"result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats\",\n \"\\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats)\",\n \"OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args) = parser.parse_args()\",\n \"!= \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value =\",\n \"= result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result =\",\n \"formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform\",\n \"device properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)):\",\n \"str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats)) del\",\n \"import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\")\",\n \"result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return\",\n \"print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not options.short:\",\n \"info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info\",\n \"print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v in info_value]))\",\n \"cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices():\",\n \"print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY\",\n \"try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\")\",\n \"info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if\",\n \"]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf,\",\n \"result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result\",\n \"= result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join(\",\n \"in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform\",\n \"not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value =\",\n \"result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order\",\n \"return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type))\",\n \"except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and\",\n \"options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for\",\n \"(info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [\",\n \"for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype)\",\n \"cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else:\",\n \"\\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"),\",\n \"[ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v in info_value])) else: try:\",\n \"{info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\")\",\n \"result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown\",\n \"platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device\",\n \"info_name) try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info\",\n \"isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v\",\n \"and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device\",\n \"= \\\"<error>\\\" if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)):\",\n \"help=\\\"don't print all device properties\\\") (options, args) = parser.parse_args() def print_info(obj, info_cls): for\",\n \"OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options,\",\n \"#cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx,\",\n \"\\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\")\",\n \"cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for\",\n \"channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format( cl.mem_object_type.to_string(itype),\",\n \"result formats = \\\", \\\".join( \\\"{}-{}\\\".format( cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for\",\n \"in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]:\",\n \"]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else: def\",\n \"formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result\",\n \"cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [\",\n \"info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform in\",\n \"not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device])\",\n \"= cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result =\",\n \"except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data\",\n \"for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats)) del ctx\",\n \"%d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" %\",\n \"not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in [ cl.mem_flags.READ_ONLY,\",\n \"try: info_value = obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info and\",\n \"device partition property %d>\\\") for v in info_value])) else: try: print(f\\\"{info_name}: {info_value}\\\") except:\",\n \"import pyopencl as cl from optparse import OptionParser parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\",\",\n \"try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats = \\\"<error>\\\" else: def str_chd_type(chdtype):\",\n \"for mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D,\",\n \"result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result\",\n \"mf in [ cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D\",\n \"cl.mem_flags.READ_ONLY, #cl.mem_flags.READ_WRITE, #cl.mem_flags.WRITE_ONLY ]: for itype in [ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats\",\n \"type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result = result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\",\",\n \"def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name !=\",\n \"print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform)\",\n \"0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS: {}\\\\n\\\".format( cl.mem_object_type.to_string(itype), cl.mem_flags.to_string(mf), formats))\",\n \"\\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result = result.replace(\\\"FLOAT\\\", \\\"F\\\") return result formats =\",\n \"obj.get_info(info) except: info_value = \\\"<error>\\\" if (info_cls == cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\"\",\n \"[ cl.mem_object_type.IMAGE2D, cl.mem_object_type.IMAGE3D ]: try: formats = cl.get_supported_image_formats(ctx, mf, itype) except: formats =\",\n \"= cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\", \\\"\\\") result =\",\n \"print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf in\",\n \"cl.channel_order.to_string(iform.channel_order, \\\"<unknown channel order 0x%x>\\\"), str_chd_type(iform.channel_data_type)) for iform in formats) print(\\\"{} {} FORMATS:\",\n \"parser = OptionParser() parser.add_option(\\\"-s\\\", \\\"--short\\\", action=\\\"store_true\\\", help=\\\"don't print all device properties\\\") (options, args)\",\n \"device in platform.get_devices(): if not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device,\",\n \"= result.replace(\\\"UNSIGNED\\\", \\\"U\\\") result = result.replace(\\\"SIGNED\\\", \\\"S\\\") result = result.replace(\\\"NORM\\\", \\\"N\\\") result =\",\n \"list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v in\",\n \"else: try: print(f\\\"{info_name}: {info_value}\\\") except: print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms():\",\n \"(options, args) = parser.parse_args() def print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not\",\n \"cl.device_info and info_name == \\\"PARTITION_TYPES_EXT\\\" and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown\",\n \"if not options.short: print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx =\",\n \"print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info) for device in platform.get_devices(): if not\",\n \"info_name != \\\"to_string\\\": info = getattr(info_cls, info_name) try: info_value = obj.get_info(info) except: info_value\",\n \"print_info(obj, info_cls): for info_name in sorted(dir(info_cls)): if not info_name.startswith(\\\"_\\\") and info_name != \\\"to_string\\\":\",\n \"def str_chd_type(chdtype): result = cl.channel_type.to_string(chdtype, \\\"<unknown channel data type %d>\\\") result = result.replace(\\\"_INT\\\",\",\n \"and isinstance(info_value, list)): print(\\\"{}: {}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for\",\n \"{}\\\".format(info_name, [ cl.device_partition_property_ext.to_string(v, \\\"<unknown device partition property %d>\\\") for v in info_value])) else:\",\n \"print(\\\"%s: <error>\\\" % info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not\",\n \"print(75*\\\"-\\\") print(device) if not options.short: print(75*\\\"-\\\") print_info(device, cl.device_info) ctx = cl.Context([device]) for mf\",\n \"info_name) for platform in cl.get_platforms(): print(75*\\\"=\\\") print(platform) print(75*\\\"=\\\") if not options.short: print_info(platform, cl.platform_info)\"\n]","isConversational":false}}},{"rowIdx":2597,"cells":{"ngram":{"kind":"list like","value":["and the following disclaimer in the documentation # and/or other materials provided with","render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and","ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}],","Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],","0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only","ocp solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model","self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python","{}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\","generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost","+= 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data,","raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if","nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e:","template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir =","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field,","dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path","Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension","in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields","in binary form must reproduce the above copyright notice, # this list of","generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\")","c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p,","in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost","ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent","# terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file","f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ +","== 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]),","{}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]","# cpu time for integrator contribution of external function calls 'time_sim_la', # cpu","field_ == 'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve():","[c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\","field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be","= np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.')","# cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', #","from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from","setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path):","ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent","= constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent","'su'] field = field_ field = field.encode('utf-8') if (field_ not in out_fields +","in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c'","option {} must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes","constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi","self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in,","json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict","elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name #","acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h'","regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0]","\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C,","dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:","constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu","dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np =","!= ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field","self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data,","solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of","THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,","'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir =","slack variables of soft lower inequality constraints \\n su: slack variables of soft","Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu =","constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e","status def get(self, stage_, field_): \"\"\" get the last solution of the solver:","stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p,","cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields","> 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e >","out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile'","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field,","for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif","ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field =","loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{}","constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't']","render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h'","Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields,","self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p,","\\n sl: slack variables of soft lower inequality constraints \\n su: slack variables","wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0]","elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg =","False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,","self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\","nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] !=","provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf","ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct, v in","(you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),","SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #","inequalities (at the solution) \\n sl: slack variables of soft lower inequality constraints","ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else:","= c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype =","{nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}')","float): raise Exception('solver option {} must be of type float. You have {}.'.format(field_,","= dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh >","= np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.')","nsbx + nsbu + nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx =","# discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf /","value_): \"\"\" set numerical data in the cost module of the solver: :param","np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints =","\\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS':","dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif","# uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes):","attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct]","acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not","or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) /","Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0]","CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR","Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be","(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS","lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e","should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,","TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR","solver: :param stage: integer corresponding to shooting node :param field_: string in ['x',","'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type","acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type ==","'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver:","ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() #","c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p","if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim},","Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type","for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not isinstance(v,","ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []):","= 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir","multipliers for dynamics equality constraints \\n lam: multipliers for inequalities \\n t: slack","= ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not in","self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else:","self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in,","following conditions are met: # # 1. Redistributions of source code must retain","np from ctypes import * from casadi import CasadiMeta, Function, SX from copy","template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","\"\"\" get the information of the last solver call: :param field_: string in","# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN","stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format(","value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\","else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise","dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent","e.g. 'lbx' :param value_: of appropriate size \"\"\" # cast value_ to avoid","> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file,","{} is an invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields +","either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf -","value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values 0,","modification, are permitted provided that the following conditions are met: # # 1.","if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should","idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent","elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0:","if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')","acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx))","if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can","!= nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e,","{nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif","cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e,","ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib')","on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0:","ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict,","= 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints:","solution) \\n sl: slack variables of soft lower inequality constraints \\n su: slack","print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if","value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p,","out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir","# make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator","!= nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx,","generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model)","field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts,","cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) #","= np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.')","dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] !=","nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.')","nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi","= {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e =","= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat","cpu time for integrator contribution of linear algebra 'time_qp', # cpu time qp","following disclaimer. # # 2. Redistributions in binary form must reproduce the above","dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =","= np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.')","= \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif","is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding","= acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts =","= ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if","['time_tot', # total cpu time previous call 'time_lin', # cpu time for linearization","(without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number","is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np","strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims)","Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics","ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load","dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict","cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension","lbu lbx lg lh lphi ubu ubx ug uh uphi; \\n lsbu lsbx","out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear","render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,","out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes","field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {}","= nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi =","self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type","make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model)","regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0","if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else:","regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0]","min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data","= '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type ==","constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e","stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p,","ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,","CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode","Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding","self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts()","nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension:","out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file","ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent","np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e","AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\","# render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib')","constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e","template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e >","from ctypes import * from casadi import CasadiMeta, Function, SX from copy import","value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu))","values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p,","model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ##","should not be used anymore, better use cost_set, constraints_set def set(self, stage_, field_,","v in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue","acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif","import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list,","if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims =","raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent","!= 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take","else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding","WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #","'{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and","raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else:","# cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution of","+ \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0:","ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field =","field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p,","in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c'","c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields:","and ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if","- opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf","but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0]","constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e","skip non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct)","\\n \"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl',","LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,","if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid","if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_):","'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu time previous call","\\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise","+ nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field =","[] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape","= \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim","dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver","constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e,","f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description","of conditions and the following disclaimer in the documentation # and/or other materials","template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file,","* np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N,","cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny,","c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free()","\"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0]","isinstance(value_, int): raise Exception('solver option {} must be of type int. You have","column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u)","must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 =","dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0]","\"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su']",":param value_: of type int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields","nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh","cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' +","time previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu time","conditions and the following disclaimer. # # 2. Redistributions in binary form must","if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer","c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_,","json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)","value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field,","+ \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] !=","!= nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu,","field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\","= constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent","conditions are met: # # 1. Redistributions of source code must retain the","not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if","np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e =","# cpu time for integrator contribution of linear algebra 'time_qp', # cpu time","Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0]","ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT","= constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent","stage: integer corresponding to shooting node :param field_: string in ['x', 'u', 'z',",".utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def","opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise","f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise","cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes =","regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e =","IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os,","in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h'","usphi] .. note:: pi: multipliers for dynamics equality constraints \\n lam: multipliers for","is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding","json_path) class AcadosOcpSolver: \"\"\" class to interact with the acados ocp solver C","integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_: of","nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,))","+ nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e =","ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\ or","out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field","nsbu + nsh + nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0] !=","<NAME>, <NAME>, <NAME> # # This file is part of acados. # #","Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension","elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] !=","'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir,","# cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without","True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims()","stage_, field_): \"\"\" get the last solution of the solver: :param stage: integer","= np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type ==","'take only value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8')","= cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding","from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from","' for field \"{}\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data","c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_,","regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and","(at the solution) \\n sl: slack variables of soft lower inequality constraints \\n","ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model --","Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx","in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on","equality constraints \\n lam: multipliers for inequalities \\n t: slack variables corresponding to","wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\"","np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding","constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file","field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file,","upper inequality constraints \\n \"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't']","wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\"","constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush","is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr):","+ \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e ==","elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] !=","Redistribution and use in source and binary forms, with or without # modification,","not a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields","POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","= constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent","+ nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg},","= c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp =","argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\","BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR","constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx","cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ##","to avoid conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8')","{})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)","Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns =","raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial","'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp","usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh + nsg","is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims","nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e","if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')","usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif","lh lphi ubu ubx ug uh uphi; \\n lsbu lsbx lsg lsh lsphi","self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\") if","{wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns} = nsbx +","= acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp,","argument \\'rti_phase\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers')","con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx","ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model']","json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file =","+ out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible values are","self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can '","out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)","acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS':","if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] !=","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return","nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e:","= get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO:","dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] !=","out_file, template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type ==","object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model #","= constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent","json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() #","json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout","= c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype =","== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e","= nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng","corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate","'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not","'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu","raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif","regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise","raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if","uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr =","= ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] #","value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_","not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file","in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be of",".generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model","out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path)","if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name)","= {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns =","and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name)","2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,","nsh_e + nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field","field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,","0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e,","= '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir =","self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out()","= \"zu\" dim = cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for","acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi >","vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path","cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] !=","out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name)","= np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes =","\"\"\" set options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase',","np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)),","= ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny","regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0]","= opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)):","'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear","nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,))","acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file,","constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx',","int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type ==","field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size","!= dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg +=","acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file,","ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field =","stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif","t: slack variables corresponding to evaluation of all inequalities (at the solution) \\n","regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.'","{nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e","np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if","'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" # cast value_ to avoid","inequality constraints \\n su: slack variables of soft upper inequality constraints \\n \"\"\"","nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e:","stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\" set numerical data","# TODO: maybe make one funciton with formatting for acados_struct, v in ocp_layout.items():","(constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape =","Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] !=","\"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field","field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts,","attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model'])","mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims","= acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4,","constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh","# nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =","self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type","# initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0","{tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json',","ubx ug uh uphi; \\n lsbu lsbx lsg lsh lsphi usbu usbx usg","templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data)","f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent","is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding","in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h'","constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension","constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu","['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise","!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg","nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi","= json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout","nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,))","the constraint module of the solver: Parameters: :param stage_: integer corresponding to shooting","Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict","+ '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'):","= np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter =","time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf)","np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] !=","template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file","raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if","dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m","tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\","from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from","if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding","cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns:","# 1. Redistributions of source code must retain the above copyright notice, #","np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\","sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))","{}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_,","regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e","yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == [] and","out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name)","dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e):","= value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape !=","'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field =","raise Exception('solver option {} must be of type str. You have {}.'.format(field_, type(value_)))","\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1:","'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e ==","-*- coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>,","in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if","ug uh uphi; \\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush","dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu =","\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes","ny, regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz","Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e):","if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p):","ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING","self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes =","use in source and binary forms, with or without # modification, are permitted","and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not","template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL","linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for integrator","= '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH'","if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp,","templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =","self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp with","cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if","raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0]","nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e)","'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution","'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external","stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get the information","used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields =","following order: \\n [ lbu lbx lg lh lphi ubu ubx ug uh","0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np,","dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e):","(you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =","of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_","for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} =","# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN","time for integrator 'time_sim_ad', # cpu time for integrator contribution of external function","values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_","nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh:","discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module =","= 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class","'{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH'","nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu","acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model,","self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p,","else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p,","C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e","\\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e +","last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_","template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ##","= self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in","== 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e","Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file","have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.'","cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field","GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)","separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data","stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter')","IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED","constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e","= ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues","'{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir","constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension","cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0]","tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent","on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file,","if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0]","integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) #","\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\")","lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e)","dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh):","elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e =","field, value_data_p) return def options_set(self, field_, value_): \"\"\" set options of the solver:","stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray(","self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\" get the last solution of","def solve(self): \"\"\" solve the ocp with current input \"\"\" status = self.shared_lib.acados_solve()","Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type","discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0]","constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng: raise","= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","order: \\n [ lbu lbx lg lh lphi ubu ubx ug uh uphi;","self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp with current input \"\"\"","nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise","\\n\\t'\\ + f'Detected ns = {ns} = nsbx + nsbu + nsg +","= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p,","must retain the above copyright notice, # this list of conditions and the","wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size","constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,","options_set(self, field_, value_): \"\"\" set options of the solver: Parameters: :param field_: string,","usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\n","cast value_ to avoid conversion issues value_ = value_.astype(float) field = field_ field","dimension for field \"{}\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims,","template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file","0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS':","W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if","column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 #","= {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and","== 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e:","solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model =","tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__)","'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created =","for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve():","raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0]","range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj]))","constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\","def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx',","else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise","nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.')","or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else:","dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for","dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =","W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0","regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,","corresponding to evaluation of all inequalities (at the solution) \\n sl: slack variables","unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts =","the above copyright notice, # this list of conditions and the following disclaimer.","ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0]","= casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and","nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg =","generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name","# POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy as","ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict =","ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct,","!= ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints","note:: regarding lam, t: \\n the inequalities are internally organized in the following","dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg","self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]):","Exception('solver option {} must be of type int. You have {}.'.format(field_, type(value_))) else:","json import numpy as np from ctypes import * from casadi import CasadiMeta,","cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file =","\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self):","Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e","open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate","wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\","fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n Possible values are","of acados. # # The 2-Clause BSD License # # Redistribution and use","nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise","== 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type ==","out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name)","value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\" set numerical data in the","if field_ == 'rti_phase': if value_ < 0 or value_ > 2: raise","np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)","nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise","return out # Note: this function should not be used anymore, better use","regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type","with dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e","= self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp with current","with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) #","= self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros(","'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e ==","np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS':","WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND","from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict,","'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal","'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise","ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column","acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict =","lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension","Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e =","# terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e:","constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,","in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj],","ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding","constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx,","stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for","\\'rti_phase\\' can ' 'take only value 0 for SQP-type solvers') field = field_","wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\"","field_): \"\"\" get the information of the last solver call: :param field_: string","np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if","the acados ocp solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created =","# # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #","is part of acados. # # The 2-Clause BSD License # # Redistribution","if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding","\"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim =","= 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file,","raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field,","mismatching dimension' \\ ' for field \"{}\" with dimension {} (you have {})'.format(field_,","cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] !=","if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh,","documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE","acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type","in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints","= cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)","+ nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e","'IRK': # implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts)","value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_","Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0]","DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;","0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h'","string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \"\"\"","if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding","ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name =","in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c'","is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0","code must retain the above copyright notice, # this list of conditions and","acados. # # The 2-Clause BSD License # # Redistribution and use in","# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f:","elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] !=","raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif","+ f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e =","= ['time_tot', # total cpu time previous call 'time_lin', # cpu time for","the following conditions are met: # # 1. Redistributions of source code must","1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.') def","string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver","value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\","[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if","msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg += 'with dimension","'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file,","cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","= '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact with","if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True)","stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj],","out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if","'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension","ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny !=","= nsbx + nsbu + nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx","self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver","regarding model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny","Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny","cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e !=","else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but","opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps","THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF","dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e +","the following order: \\n [ lbu lbx lg lh lphi ubu ubx ug","c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_,","constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,","= ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u',","if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f'","# setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not","!= nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu =","= casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p)","get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as","= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape","= 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file,","field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is","self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp","np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if","shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 =","'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts","nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e:","ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment json_path =","else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but","= casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,))",".generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost","Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p,","= 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file","notice, # this list of conditions and the following disclaimer in the documentation","for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for","+ out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype","np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps","c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,","in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n Possible values","uphi; \\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] ..","self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver,","== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp,","if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding","nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e","or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.')","= acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file)","'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if","idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e +","cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if","SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import","lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality","Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e","open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load","'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if","cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\","D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh","acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK':","\"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED","up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise","algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside qp","model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name,","0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field","future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,","stage_, field_, value_): \"\"\" set numerical data in the cost module of the","ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng","ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\ f'\\nGot","= c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] !=","function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer","constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding","integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param","!= 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,","= 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost -","= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p,","field = field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields):","raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if","# strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w')","stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type ==","and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.'","BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR","have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent","generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts =","int): raise Exception('solver option {} must be of type int. You have {}.'.format(field_,","is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims =","dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) #","acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function","of the solver: :param stage: integer corresponding to shooting node :param field_: string","c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat","OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER","## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0","out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7:","this function should not be used anymore, better use cost_set, constraints_set def set(self,","elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for","Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts","binary forms, with or without # modification, are permitted provided that the following","W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e","and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr)","['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t:","argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m',","columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu","OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #","variables corresponding to evaluation of all inequalities (at the solution) \\n sl: slack","POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape)","with dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns} = nsbx + nsbu","is not a valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields)) if","self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in","columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e =","out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p,","\"\"\" set numerical data in the cost module of the solver: :param stage_:","if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,","nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e","import numpy as np from ctypes import * from casadi import CasadiMeta, Function,","set numerical data in the cost module of the solver: :param stage_: integer","nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu +","maybe make one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip","C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C","dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or","binary form must reproduce the above copyright notice, # this list of conditions","= acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx","print('\\n') return def get_stats(self, field_): \"\"\" get the information of the last solver","!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush =","nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.')","CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,","= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,","cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have","PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\","casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if","<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> #","constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e","import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import","= 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0","\\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields + ['p']))","raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS':","Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny","acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file,","c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {}","stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp')","self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \"\"\"","dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] !=","elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True)","!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg =","self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes =",":param stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param","= 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on","nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' +","{} must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes =","'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file,","'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj])))","Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0]","## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0]","== 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0","ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e","lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension","field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if","out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter')","dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg += 'with","idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent","double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_,","# The 2-Clause BSD License # # Redistribution and use in source and","the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad',","if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,","time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump","field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config,","= format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None)","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims =","dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi):","cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have","AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict,","= \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes","in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model","and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0]","if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =","stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_:","= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,))","field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter']","2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values 0, 1, 2",":param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\"",".acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length,","dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0]","field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data,","field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes","constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,","to shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate size","Exception('solver option {} must be of type float. You have {}.'.format(field_, type(value_))) else:","have {})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),","field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately","in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\","acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file)","stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj","external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =","constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg","if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data =","and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)","CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #","if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,","constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e >","ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field =","make one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non","False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model,","return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be","\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\","stage, field, value_data_p) return def options_set(self, field_, value_): \"\"\" set options of the","self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_","constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension","object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod","get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe","acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost","integer corresponding to shooting node :param field_: string in ['x', 'u', 'z', 'pi',","def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model =","ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e:","value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p,","only value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if","copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf","regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0","template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir =","# external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file","cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension","function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir,","= nsbx + nsbu + nsh + nsg + nsphi wrong_field = \"\"","= acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should","== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file,","ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) !=","consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim","c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the QP) 'time_qp_xcond',","raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns","ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) !=","True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name","{} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),","W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')","out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact with the acados ocp","is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding","BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN","ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e","POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p,","if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e","in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam,","min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data","raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif","not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object","isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute)","solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', #","if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name)","# external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h'","= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e","nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg:","usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif","cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e:","ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif","regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg =","integrator contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', #","# # 1. Redistributions of source code must retain the above copyright notice,","Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and","disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright","out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type","nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal","dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,","> 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values 0, 1,","stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj],","nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e =","cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W,","in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in'","if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if","dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns} = nsbx + nsbu +","dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct, v in","OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO","field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note::","NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A","constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if","solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp',","c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,","is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self,","\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =","= self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out","self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0]","make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model","nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0]",".generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint","function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file =","{nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if","in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost","interact with the acados ocp solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'):","msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data =","= nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] !=","field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p,","if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates","'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file =","- terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file","= \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim","else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e =","cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file","def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp')","os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' #","'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" # cast value_ to","nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e:","in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear","self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out =","= [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0])","# import sys, os, json import numpy as np from ctypes import *","ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1]","> 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =","nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): #","Redistributions of source code must retain the above copyright notice, # this list","W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == []","self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape =","raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}],","DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY","nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise","'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp","dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return","{}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_,","# Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ =","cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0]","IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY","nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if","\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib #","acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\","value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape","casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh:","not be used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_):","not a valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields)) if field_",":param stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W',","nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent","dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0]","nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e","'{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e ==","constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension","= casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u):","of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time","NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF","met: # # 1. Redistributions of source code must retain the above copyright","ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension","if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] !=","lower inequality constraints \\n su: slack variables of soft upper inequality constraints \\n","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_,","Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if","def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r')","cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0]","Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0]","soft upper inequality constraints \\n \"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam',","self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat =","self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in()","nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise","or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else:","field, out_data) return out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP':","nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e","clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get","constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else:","if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,","outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file,","render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact with the acados","format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict","dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts)","np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx","dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear","raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0]","= cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p,","range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7:","field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible values are {}.","size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns}","= \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim","(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for","wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\"","elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field !=","terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise","'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file,","'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or","type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0","AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR","if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can","ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE #","'sqp_iter', # number of SQP iterations 'statistics', # table with info about last","< 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take","'t', 'sl', 'su',] .. note:: regarding lam, t: \\n the inequalities are internally","current input \"\"\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\"","[ lbu lbx lg lh lphi ubu ubx ug uh uphi; \\n lsbu","time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps","!= tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\" with dimension","+ f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\","= 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex","elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e):","import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from","pi: multipliers for dynamics equality constraints \\n lam: multipliers for inequalities \\n t:","list of conditions and the following disclaimer in the documentation # and/or other","acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file,","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif","self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return","idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent","Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p,","ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue acados_attribute =","!= dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i","and use in source and binary forms, with or without # modification, are","out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp","lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension","# generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) #","node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',]","# this list of conditions and the following disclaimer in the documentation #","dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny:","sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1])","for field \"{}\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0])","cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get the","in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\","in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\","out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e","have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes","nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.')","self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\"","if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if","constraints \\n su: slack variables of soft upper inequality constraints \\n \"\"\" out_fields","field \"{}\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise","# set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp,","field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p,","dimension', \\ ' for field \"{}\" with dimension {} (you have {})'.format( \\","raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif","constraints_set(self, stage_, field_, value_): \"\"\" set numerical data in the constraint module of","object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting","ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise","Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if","print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get","'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function","'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8')","EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,","copyright notice, # this list of conditions and the following disclaimer. # #","import * from casadi import CasadiMeta, Function, SX from copy import deepcopy from","constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x):","template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file =","value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\","if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should","tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\" with dimension {}","solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =","make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints","= 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0","not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct,","dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0]","'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \"\"\" int_fields = ['print_level',","is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding","Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent","Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import","sys, os, json import numpy as np from ctypes import * from casadi",".generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost","regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e =","ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file)","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_","self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out","def get_stats(self, field_): \"\"\" get the information of the last solver call: :param","dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or","Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\" with dimension {} (you have","self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE:","# setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__)","or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not","lphi ubu ubx ug uh uphi; \\n lsbu lsbx lsg lsh lsphi usbu","# Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip","acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes","if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,","nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e","con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0","render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':","not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh","0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file","Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:","INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,","dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear","== 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching","out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion","terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =","lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh =","# This file is part of acados. # # The 2-Clause BSD License","should be column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu","for integrator contribution of external function calls 'time_sim_la', # cpu time for integrator","if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z):","value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_ > 2:","Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if","= field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is 'p':","= ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model)","SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED","is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr","raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0]","1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension'","Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,","IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR","np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL':","'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear","lam: multipliers for inequalities \\n t: slack variables corresponding to evaluation of all","'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)","<NAME> # # This file is part of acados. # # The 2-Clause","OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.;","PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,","['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims","self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims","idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,))","get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims'])","out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)","dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0]","'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu time previous call 'time_lin', #","c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int))","dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.')","stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj","\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\","field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p,","self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims","!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh","\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p,","cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options #","c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data","mismatching dimension', \\ ' for field \"{}\" with dimension {} (you have {})'.format(","opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name,","load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints,","is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise","out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype =","= nsg ns = nsbx + nsbu + nsh + nsg + nsphi","'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal","' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type !=","\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']:","time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not","is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps)","POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p,","field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]","<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part","over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal","constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx","field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an","0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over","return def cost_set(self, stage_, field_, value_): \"\"\" set numerical data in the cost","numpy as np from ctypes import * from casadi import CasadiMeta, Function, SX","set options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks',","if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_)","!= ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field","!= nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh =","casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.')","stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set numerical data","json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make","self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims","np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if","acados ocp solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False","out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name)","cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field","else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or","is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding","render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir,","Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension:","!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi","dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))","not isinstance(value_, str): raise Exception('solver option {} must be of type str. You","<NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part of acados.","# nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz","corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_:","idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,))","= np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.')","file is part of acados. # # The 2-Clause BSD License # #","is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0]","POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config,","dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and","jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj])))","The 2-Clause BSD License # # Redistribution and use in source and binary","SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode","(not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf =","out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over","above copyright notice, # this list of conditions and the following disclaimer in","found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name)","acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type","value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver","== 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up","if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise","time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with info","out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name)","sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout =","dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts","in source and binary forms, with or without # modification, are permitted provided","# # 2. Redistributions in binary form must reproduce the above copyright notice,","value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p,","regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh","c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p","if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False)","template_dir, json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e ==","!= ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field","'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ ==","nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx","'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type","= c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int,","out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir","'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not in","= 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex","\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj]))","ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug,","description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict","corresponding to shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam',","raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif","solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\'","elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx","json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0:","!= ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng","# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object","linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside","\"\"\" # cast value_ to avoid conversion issues value_ = value_.astype(float) field =","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in","out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m'","ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.')","COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,","ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e !=","Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0]","and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif","is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding","the inequalities are internally organized in the following order: \\n [ lbu lbx","mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims,","ng, regarding lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr):","!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e =","cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields =","constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have","Exception('solver option {} must be of type str. You have {}.'.format(field_, type(value_))) else:","dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi","'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file,","= {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e =","C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else:","ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \"\" if","field \"{}\" with dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data","if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp","== 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]),","int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self,","field_: string, e.g. 'lbx' :param value_: of appropriate size \"\"\" # cast value_","= nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] !=","= get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json,","c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data","self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp with current input","constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e","ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__","is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or","{} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =","nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh","value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p)","elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e):","Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype","acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) #","treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype =","False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True,","# <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,","'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir =","= acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) #","idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent","= self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in =","# strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict =","+ mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values are","dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] !=","render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function if","constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu,","folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file,","acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir","function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file =","render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir","stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p,","INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT","default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()","Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0:","out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m =","usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\n lam:","regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise","for integrator contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call',","raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent","\\ ' for field \"{}\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape))","must reproduce the above copyright notice, # this list of conditions and the","con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0]","lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension","nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if","def constraints_set(self, stage_, field_, value_): \"\"\" set numerical data in the constraint module","nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] !=","'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file,","+ ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int","raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0]","1. Redistributions of source code must retain the above copyright notice, # this","disclaimer in the documentation # and/or other materials provided with the distribution. #","/ dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent","the last solution of the solver: :param stage: integer corresponding to shooting node","value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can '","dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] !=","yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e)","Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json","solver: :param stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref',","dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] !=","[c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims,","dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np =","opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions:","'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot',","W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e","You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if","for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj],","mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values are {}.","dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically","return out def print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if","cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu","!= nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e,","regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e =","'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to","must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_)","get the last solution of the solver: :param stage: integer corresponding to shooting","nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg","nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,))","elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must","model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name =","'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \"\"\" int_fields =","isinstance(value_, str): raise Exception('solver option {} must be of type str. You have","generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column,","# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #","acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type","generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and","parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if","the above copyright notice, # this list of conditions and the following disclaimer","N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1]","else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes))","# terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and","'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex","stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0],","['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_","if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if","lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or","is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double))","json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =","shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f:","dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e","if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi","+ nsbu + nsh + nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0]","(stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64)","constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension","c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field)","out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh >","POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields +",":param field_: string, e.g. 'lbx' :param value_: of appropriate size \"\"\" # cast","render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir =","if not isinstance(value_, float): raise Exception('solver option {} must be of type float.","= nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] !=","ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF","in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue acados_attribute","f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure","dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries","self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \"\"\" set options","field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is","uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if","lg lh lphi ubu ubx ug uh uphi; \\n lsbu lsbx lsg lsh","cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n","# dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile","jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj],","{nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization","'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid","model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny =","regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W,","other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0","ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0]","raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng","cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\","constraints \\n \"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields =","ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,","= nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0]","= self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out =","if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\"","cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if","dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ =","con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e =","but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx =","con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0","= np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.')","make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim =","{nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes):","valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields","as np from ctypes import * from casadi import CasadiMeta, Function, SX from","have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not","= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config,","acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =","option {} must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes","nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e =","json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =","field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not","= nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e","from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from","SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND","raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and","!= 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] !=","+ nsbu + nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx},","+ nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu =","if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function","= False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type ==","self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set numerical","SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,","= 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape ==","ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes',","regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise","idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent","range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and","if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format(","# explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': #","external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file","ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field,","\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\"","!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e:","terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e","self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\","def options_set(self, field_, value_): \"\"\" set options of the solver: Parameters: :param field_:","elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0]","'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage,","in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be of","open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp,","!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e","out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir =","acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,","all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0]","f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =","size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e}","OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,","'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver","# terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e:","idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh +","np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self,","LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)","t: \\n the inequalities are internally organized in the following order: \\n [","field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config,","(field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config,","# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES","['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast","acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f)","stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj],","self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config()","0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e","in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be of","<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #","EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys,","valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter',","not isinstance(value_, int): raise Exception('solver option {} must be of type int. You","usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints","'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu time previous call 'time_lin',","dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise","with current input \"\"\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_):","nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent","os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib","'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj],","if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\"","is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e","stage_, field_, value_): \"\"\" set numerical data in the constraint module of the","POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if","for field \"{}\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data =","idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] !=","that the following conditions are met: # # 1. Redistributions of source code","regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise","function calls 'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp',","ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0","= 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file,","self.nlp_solver, field, out_data) return out # Note: this function should not be used","if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should","!= ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e =","raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e","and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value","import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template,","\"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected","if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding","acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file,","= field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is","dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim = cost.zu.shape[0]","THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #","dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'):","OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS","THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE","= 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension","'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file,","c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims:","field_, value_): \"\"\" set options of the solver: Parameters: :param field_: string, e.g.","if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if","= c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims,","the solver: :param stage_: integer corresponding to shooting node :param field_: string, e.g.","out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e","# implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif","cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","SQP iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n', ]","template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file","0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise","opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either","= cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension","elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format(","= 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints","else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise","casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims","'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total","casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty')","def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment json_path","dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e):","\\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny:","acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts = dict(generate_hess=1)","elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0:","is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding","\\n the inequalities are internally organized in the following order: \\n [ lbu","This file is part of acados. # # The 2-Clause BSD License #","= 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if","have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_","c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if","!= nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e =","else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0]","False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF':","dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) #","nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,))","self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p,","= nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] !=","Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e)","else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np","1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension',","== constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] !=","for field \"{}\" with dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape))","dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list,","lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config,","constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims,","= '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e ==","'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_","out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)","if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","<NAME>, <NAME> # # This file is part of acados. # # The","other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY","\"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0]","nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field","elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu):","THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED","last solution of the solver: :param stage: integer corresponding to shooting node :param","{dim}, \\n\\t'\\ + f'Detected ns = {ns} = nsbx + nsbu + nsg","lbx lg lh lphi ubu ubx ug uh uphi; \\n lsbu lsbx lsg","iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not","render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh","raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise","# load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims,","# # The 2-Clause BSD License # # Redistribution and use in source","terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir =","field_): \"\"\" get the last solution of the solver: :param stage: integer corresponding","dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0]","field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get():","USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY","\\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny !=","dimension' \\ ' for field \"{}\" with dimension {} (you have {})'.format(field_, tuple(dims),","self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see","type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if","self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields:","# Redistribution and use in source and binary forms, with or without #","import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import","dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] !=","ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] !=","acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type ==","stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu']","BSD License # # Redistribution and use in source and binary forms, with","\\n su: slack variables of soft upper inequality constraints \\n \"\"\" out_fields =","2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise",":param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] ..","note:: pi: multipliers for dynamics equality constraints \\n lam: multipliers for inequalities \\n","nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints","def get(self, stage_, field_): \"\"\" get the last solution of the solver: :param","print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n')","argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields +",".generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp","def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model","json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and","if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name)","soft lower inequality constraints \\n su: slack variables of soft upper inequality constraints","value_): \"\"\" set numerical data in the constraint module of the solver: Parameters:","\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))","constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,","cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims,","raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent","field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns} = nsbx","!= nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e =","not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file =","c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {}","solve(self): \"\"\" solve the ocp with current input \"\"\" status = self.shared_lib.acados_solve() return","# Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0]","np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e","= ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int):","constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,","generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint","string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" # cast","> 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0 for","dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0]","\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\","== 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e","= ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 ==","elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file","elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi):","> 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices","distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS","NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY","function should not be used anymore, better use cost_set, constraints_set def set(self, stage_,","> 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type ==","return def constraints_set(self, stage_, field_, value_): \"\"\" set numerical data in the constraint","nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e =","= cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif","mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not","dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] !=","model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype","type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str):","= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in","or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else:","'{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS':","yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS':","constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,","float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields:","USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH","!= ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field","time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time","== 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir","FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE","form must reproduce the above copyright notice, # this list of conditions and","= casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] !=","def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r')","dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0]","\"zu\" dim = cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field","structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f)","{} (you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p","lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension","Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file","avoid conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage","constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension","= acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx","Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny,","= '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e","funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes","= cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes","= nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] !=","\"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make","self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p","is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding","'{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/'","in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear","= 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg += 'with dimension {}","# modification, are permitted provided that the following conditions are met: # #","regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise","# get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts","dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] !=","getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model):","= c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not","elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0]","terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \"\"","OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF","= os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return","nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim =","Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if","if not isinstance(value_, int): raise Exception('solver option {} must be of type int.","= nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e},","= nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e =","EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES","in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\"","= constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent","self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out =","import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import","is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e):","= {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path +","= np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization:","be column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu =","= nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] !=","ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise","lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or","retain the above copyright notice, # this list of conditions and the following","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),","np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)","PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS","are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","get_stats(self, field_): \"\"\" get the information of the last solver call: :param field_:","= field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p,","(tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' =","constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e,","self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\" set","acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost =","json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder","acados_ocp def solve(self): \"\"\" solve the ocp with current input \"\"\" status =","> 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h'","'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal","os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name)","= \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for","AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #","= '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function","\\n t: slack variables corresponding to evaluation of all inequalities (at the solution)","lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension","DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED","return def get_stats(self, field_): \"\"\" get the information of the last solver call:","dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver,","= np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.')","'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file =","for dynamics equality constraints \\n lam: multipliers for inequalities \\n t: slack variables","['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {}","field = field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats():","on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0:","path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise","C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model","are met: # # 1. Redistributions of source code must retain the above","isinstance(value_, float): raise Exception('solver option {} must be of type float. You have","# <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part of","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return","= 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function","value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes","= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field,","or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or","f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}')","else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1)","'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu time","constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,","dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu","{nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e","= cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with","\"\"\" get the last solution of the solver: :param stage: integer corresponding to","field_, value_): \"\"\" set numerical data in the cost module of the solver:","np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if","regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e =","nsbx + nsbu + nsh + nsg + nsphi wrong_field = \"\" if","LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR","explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit","opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x","OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import","cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise","+ nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field =","regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.')","if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not","\"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim =","dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0:","constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu","opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts)","= 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file","= cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if","raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent","constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,","self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes =","cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e =","cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible values","c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in","cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\","Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e =","IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING","!= 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] !=","dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny #","iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n', ] field","in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\","with or without # modification, are permitted provided that the following conditions are","0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e:","field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\","QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations","cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have","of external function calls 'time_sim_la', # cpu time for integrator contribution of linear","raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path if","\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,","idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,))","self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p,","'statistics', # table with info about last iteration 'stat_m', 'stat_n', ] field =","# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS","nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)","'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\'","yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr)","= np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.')","else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu =","'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external","time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time","set numerical data in the constraint module of the solver: Parameters: :param stage_:","acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set","parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int","= np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.')","is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding","size \"\"\" # cast value_ to avoid conversion issues value_ = value_.astype(float) field","constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,","0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0 for SQP-type","'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field","!= ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e","ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e ==","'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m,","constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh","np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for","Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module","import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import","\"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if","be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif","## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' +","OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF","of soft lower inequality constraints \\n su: slack variables of soft upper inequality","self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return","elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh =","nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.')","with formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes if","= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes =","c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_):","), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data =","value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","ns = nsbx + nsbu + nsh + nsg + nsphi wrong_field =","= constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension","nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu:","OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY","Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data","cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if","nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps)","provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT","= [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note:","nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e #","DAMAGE.; # import sys, os, json import numpy as np from ctypes import","if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding","render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost","over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints","casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] !=","and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file","the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_:","= constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent","self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config","options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length'","+ cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","str): raise Exception('solver option {} must be of type str. You have {}.'.format(field_,","can ' 'take only value 0 for SQP-type solvers') field = field_ field","setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type","value_data_p) return def options_set(self, field_, value_): \"\"\" set options of the solver: Parameters:","PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS","Note: this function should not be used anymore, better use cost_set, constraints_set def","dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.')","= 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if","dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny !=","of source code must retain the above copyright notice, # this list of","# <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # #","dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] !=","initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else:","self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set","TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE","a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields +","from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty,","in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field,","+ nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim","dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data,","== 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny:","idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent","field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields):","stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0])","c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_,","== 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent","if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS':","'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file,","if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is","etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not","dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e +","+ nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim","= constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent","for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]),","ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json","'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\n the","cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif","== 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False)","value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\","notice, # this list of conditions and the following disclaimer. # # 2.","INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,","!= nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx =","raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu","ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0]","'su',] .. note:: regarding lam, t: \\n the inequalities are internally organized in","True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment","'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file =","of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_","nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise","f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp","copyright notice, # this list of conditions and the following disclaimer in the","HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,","of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param","nsbu + nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu","WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN","elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.')","'{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e ==","string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be of type","dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu):","sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path","call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call',","= '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e","dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim = cost.zl.shape[0]","dimension ny, regarding W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}],","[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this","must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_)","generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx","multipliers for inequalities \\n t: slack variables corresponding to evaluation of all inequalities","constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e","acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be","= c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)),","tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\" with dimension {}","evaluation of all inequalities (at the solution) \\n sl: slack variables of soft","nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi:","{dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e","'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK':","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_,","in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m'","cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot","np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh","# skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct,","external function calls 'time_sim_la', # cpu time for integrator contribution of linear algebra","function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,","cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}],","nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim =","data in the cost module of the solver: :param stage_: integer corresponding to","raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\" with dimension {} (you","argument \\'rti_phase\\' can ' 'take only value 0 for SQP-type solvers') field =","# -*- coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, #","import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp):","render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,","c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int","nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e +","module of the solver: :param stage_: integer corresponding to shooting node :param field_:","POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config,","ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints #","nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu:","previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu time for","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif","acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options","slack variables of soft upper inequality constraints \\n \"\"\" out_fields = ['x', 'u',","self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype","= acados_ocp def solve(self): \"\"\" solve the ocp with current input \"\"\" status","yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e","os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file =","contribution of external function calls 'time_sim_la', # cpu time for integrator contribution of","raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif","raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx","if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,","acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy","wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\"","== 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir =","string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes)","non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__","generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics","slack variables corresponding to evaluation of all inequalities (at the solution) \\n sl:","You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if","or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values","= \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim","tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) =","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)","== other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0,","c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should not","attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy","get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts =","W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise","name = acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(),","0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr","!= ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e,","nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)","cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0]","constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p,","LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT","idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,))","0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape:","generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate","elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts =",":param value_: of appropriate size \"\"\" # cast value_ to avoid conversion issues","c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function","the information of the last solver call: :param field_: string in ['statistics', 'time_tot',","template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding","+ nsh_e + nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e:","setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict","ubu ubx ug uh uphi; \\n lsbu lsbx lsg lsh lsphi usbu usbx","else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e","lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension","= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p,","constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi","Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if","idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] !=","if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,))","if not isinstance(value_, str): raise Exception('solver option {} must be of type str.","{}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64)","else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise","dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] !=","FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT","NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,","json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =","!= nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh,","nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh:","0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,","byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot","= \"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif","# Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make","field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes =","generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL':","dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding","out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","'lam', 't'] # cast value_ to avoid conversion issues value_ = value_.astype(float) field","cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]","elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu =","is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding","c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created:","module of the solver: Parameters: :param stage_: integer corresponding to shooting node :param","Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if","not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps","acados_struct, v in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict):","sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file,","= casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0]","Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0]","np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out =","c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del","of all inequalities (at the solution) \\n sl: slack variables of soft lower","regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise","nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi =","are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =","= 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file","= acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with","Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0):","template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out","= cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if","= cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.'","ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With","+ \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny","field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int,","COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,","= {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e):","field, value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\" set numerical data in","if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif","ns = {ns} = nsbx + nsbu + nsg + nsh + nsphi.\\n\\t'\\","Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1]","# constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi","== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file,","self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set():","dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =","import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import","get the information of the last solver call: :param field_: string in ['statistics',","the cost module of the solver: :param stage_: integer corresponding to shooting node","'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif","stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in","in the constraint module of the solver: Parameters: :param stage_: integer corresponding to","= nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e:","constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension","dimension ny, regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if","Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\","in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints","forms, with or without # modification, are permitted provided that the following conditions","# number of SQP iterations 'statistics', # table with info about last iteration","field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_","of SQP iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n',","int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields =","= acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else:","dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p","template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file","else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx","Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu = 0 else:","info about last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8')","THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; #","value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0","constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu,","constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,","nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise","= 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file,","= 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost","stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get the information of the last","'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\n","[c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else:","int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type","- opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide","!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e","if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\","vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) #","ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF","Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if","nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h'","f'Detected ns = {ns} = nsbx + nsbu + nsg + nsh +","field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" #","\"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim =","int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def","converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of","= 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex","set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions","dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0]","= dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False,","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return","of the solver: Parameters: :param stage_: integer corresponding to shooting node :param field_:","acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit","be used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields","the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP","inequalities are internally organized in the following order: \\n [ lbu lbx lg","\"\"\" set numerical data in the constraint module of the solver: Parameters: :param","= c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims,","= dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown","= ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if","ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp,","Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent","TODO: maybe make one funciton with formatting for acados_struct, v in ocp_layout.items(): #","= constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx =","= 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created","!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns","# # This file is part of acados. # # The 2-Clause BSD","= casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z)","dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and","dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator","W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')","!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e =","= np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p]","= make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code","= self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config =","!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e =","integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0)","dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0","ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def","template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file","stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get the information of the","import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import","!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}],","= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims,","field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not","IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.","0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu,","POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape)","OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR","e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \"\"\" int_fields","be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if","field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p,","return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()","isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes","return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': #","' 'take only value 0 for SQP-type solvers') field = field_ field =","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),","dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0]","ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding","== []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if","== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file,","OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE","acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting","= 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file,","np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if","= time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or","out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims,","dimension np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type ==","if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,","dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e","Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out","print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format(","is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or","if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name)","= field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if","shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate size \"\"\"","information of the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin',","ny, regarding W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n')","if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\","class to interact with the acados ocp solver C object \"\"\" def __init__(self,","(not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform","# this list of conditions and the following disclaimer. # # 2. Redistributions","= dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0]","\\n [ lbu lbx lg lh lphi ubu ubx ug uh uphi; \\n","set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model","'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\n the inequalities are","out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values","value_: of appropriate size \"\"\" # cast value_ to avoid conversion issues value_","'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam',","!= tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\" with dimension","0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in","is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding","dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] =","acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json =","elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg):","# total cpu time previous call 'time_lin', # cpu time for linearization 'time_sim',","render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL':","ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny","out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name)","regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0]","json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp","stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in","Parameters: :param stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx'","constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx,","= 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension","elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx):","import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import","type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in","string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding","or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else:","raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N):","if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\","acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json","+ mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int","to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code')","as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp","is an invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields))","render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir,","= ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct, v","ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\n lam: multipliers","raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng","'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over","in the documentation # and/or other materials provided with the distribution. # #","data in the constraint module of the solver: Parameters: :param stage_: integer corresponding","constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,","class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip","model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x)","usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif","AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT","be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0","usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif","__init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent","are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)),","dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg):","dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding","else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else:","indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with","> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file,","self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create()","\"{}\" with dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data =","\"{}\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))","# terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file","casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu","external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates","idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent","'lbx' :param value_: of appropriate size \"\"\" # cast value_ to avoid conversion","dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng if not","= nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] !=","opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please","get(self, stage_, field_): \"\"\" get the last solution of the solver: :param stage:","Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary","of conditions and the following disclaimer. # # 2. Redistributions in binary form","0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir,","regarding lam, t: \\n the inequalities are internally organized in the following order:","constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e","raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0]","= {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization","'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir,","only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and","'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir,","template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ '","!= dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have nu","constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file","= getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp,","= \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim","input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton","raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only values 0, 1, 2 for","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage,","dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e):","render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type","= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes =","one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non dict","(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for","'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to","self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_):","if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ##","with the acados ocp solver C object \"\"\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created","c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p,","with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS","out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter","POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","and binary forms, with or without # modification, are permitted provided that the","nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_","== 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file","!= dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have nx","c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims,","= '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear","!= sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with","value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p,","self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def","if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else:","!= ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field","return def options_set(self, field_, value_): \"\"\" set options of the solver: Parameters: :param","out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path)","code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code","acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file,","stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype","if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,",".. note:: regarding lam, t: \\n the inequalities are internally organized in the","if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N *","POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n =","field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64))","0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over","stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\" get the information of","THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy","casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode","= 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension","raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must","field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float","!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e =","regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi =","have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not","self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]),","# nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column","= c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option","c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p,","json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =","Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields:","== [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape","= field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a","model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir,","DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE","OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR","materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE","Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type","json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if","Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0 for SQP-type solvers') field","if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,","ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0]","'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_ =","c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif","cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu","'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', # total cpu time previous","elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e =","POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","and the following disclaimer. # # 2. Redistributions in binary form must reproduce","acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e,","c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should","!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu","if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng:","'{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function if","Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name","dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx =","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data)","acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise","cpu time for integrator contribution of external function calls 'time_sim_la', # cpu time","LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND","dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0","template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not","dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0","cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\","= cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\")","<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>,","ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict)","be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif","c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_,",":param stage: integer corresponding to shooting node :param field_: string in ['x', 'u',","acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx ==","nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e","else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0]","out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact","Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if","== 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file","self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver()","type int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields","acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file,","issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_)","= json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp()","2-Clause BSD License # # Redistribution and use in source and binary forms,","<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME>","0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu","automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to","self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve","+ '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype =","are internally organized in the following order: \\n [ lbu lbx lg lh","part of acados. # # The 2-Clause BSD License # # Redistribution and","values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out =","format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost","dims.nsg = nsg ns = nsbx + nsbu + nsh + nsg +","constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,","cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h'","== 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching","constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name)","dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape","cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns:","= c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option","nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal","not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n Possible","opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh","lam, t: \\n the inequalities are internally organized in the following order: \\n","field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields","Redistributions in binary form must reproduce the above copyright notice, # this list","'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file =","nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx):","stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return def get_stats(self, field_): \"\"\"","if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,","type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ ==","terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file =","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def","np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config,","nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!')","0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise","'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if","type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in","discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise","better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref']","qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the QP)","if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size","\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3:","(you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)","OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER","!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e #","# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"","= cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding","is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding","raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_,","You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if","if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh,","out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if","nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on","discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N","out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE","c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int))","= cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p,","SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument","np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e","self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p,","nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise","if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) #","contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu","regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 ==","= ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps =","0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny:","ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib =","regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh =","lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr","type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float):","'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', #","terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file =","with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): #","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out","elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must","not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model,","is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif","idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,))","dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0]","(field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n","casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e:","'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file =","elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\")","self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib","not in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a","c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes","self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype","-- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model --","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field,","\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \"\"\" set","json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =","lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi:","are permitted provided that the following conditions are met: # # 1. Redistributions","!= 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny:","= constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension","c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =","dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz =","casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) #","\"\"\" solve the ocp with current input \"\"\" status = self.shared_lib.acados_solve() return status","{ns} = nsbx + nsbu + nsg + nsh + nsphi.\\n\\t'\\ + f'With","acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h'","c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self):","ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx","constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e","render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,","node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size","acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if","value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) #","self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p]","else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise","= 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file","dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx:","set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model)","!= ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,","int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields:","nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15:","nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h'","dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if","casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] !=","= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes =","# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS","value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field \"{}\" with","ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,","cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution of external","del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python #","'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_ = value_.astype(float)","# skip non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp,","following disclaimer in the documentation # and/or other materials provided with the distribution.","<NAME>, <NAME>, <NAME>, <NAME> # # This file is part of acados. #","raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e =","acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx =","{}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ <","constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,","= c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the","# Note: this function should not be used anymore, better use cost_set, constraints_set","= sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout","last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la',","field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid","dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] !=","cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\","lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for","invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =","value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set numerical data in the","print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]):","if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':","nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh","{nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0]","ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description","constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes if","constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name)","'time_qp_solver_call', # cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg',","wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \"zl\"","the solver: Parameters: :param stage_: integer corresponding to shooting node :param field_: string,","= \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\ self.nlp_opts, field, byref(value_ctypes)) return def","string, e.g. 'lbx' :param value_: of appropriate size \"\"\" # cast value_ to","nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e =","ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip","{} is not a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields","json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render","'ext_cost_num_hess' :param value_: of appropriate size \"\"\" # cast value_ to avoid conversion","'time_sim_ad', # cpu time for integrator contribution of external function calls 'time_sim_la', #","constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi >","above copyright notice, # this list of conditions and the following disclaimer. #","from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from","value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg += 'with dimension {} (you","cost.zu.shape[0] != ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field != \"\":","cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file","nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns","!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx =","cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return","opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf !=","\"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim =","f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0]","nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e},","else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else:","class AcadosOcpSolver: \"\"\" class to interact with the acados ocp solver C object","is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding","generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model,","without # modification, are permitted provided that the following conditions are met: #","= '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL':","!= \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ +","nsg_e + nsphi_e wrong_field = \"\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\"","constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 =","dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0","elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e):","= min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data =","the solver: :param stage: integer corresponding to shooting node :param field_: string in","# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def","['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_","\"\"\" class to interact with the acados ocp solver C object \"\"\" def","generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render","0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if","node :param field_: string, e.g. 'lbx' :param value_: of appropriate size \"\"\" #","dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes","= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p,","print_statistics(self): stat = self.get_stats(\"statistics\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for","np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu","nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,))","self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_):","render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,","template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","= CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype","self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","call 'time_lin', # cpu time for linearization 'time_sim', # cpu time for integrator","different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0","= 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file","provided that the following conditions are met: # # 1. Redistributions of source","ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with","= 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file","dim = cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field},","acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model =","template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set():","cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e:","'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir,","and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED","from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model","dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr","have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)","dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension","field, out_data) return out # Note: this function should not be used anymore,","Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr)","acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True)","BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF","field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\" set numerical data in","dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise","W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension:","= 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file","ocp_check_against_layout, np_array_to_list, make_model_consistent,\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints","variables of soft lower inequality constraints \\n su: slack variables of soft upper","this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0,","nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if","nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx:","permitted provided that the following conditions are met: # # 1. Redistributions of","nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg:","must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8')","constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e,","ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) #","POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy as np","print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj],","dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0]","= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ '","= json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class","constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx","json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir","ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field =","'/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): #","in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue #","dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_,","is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps =","field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p,","elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush):","{opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__]","numerical data in the constraint module of the solver: Parameters: :param stage_: integer","CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT","is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu",".generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils",":param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg',","+ \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny","np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\"sqp_iter\")","raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise","'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e ==","= c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype =","lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers","raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout():","regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise","0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else:","EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #","e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \"\"\" # cast value_","# cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table","BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL","conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage =","value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields","= np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.')","\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field,","dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi","field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be","= np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.')","c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int","= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field,","Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx","not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e","shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i]","= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","= np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.')","cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e:","ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]')","'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI'","or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng,","ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e:","ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny:","# Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>,","Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0]","if cost.Zl_e.shape[0] != ns_e: wrong_field = \"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] !=","= constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension","= True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims =","nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu","cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ##","= cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim = cost.Zu.shape[0] elif","= AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes","variables of soft upper inequality constraints \\n \"\"\" out_fields = ['x', 'u', 'z',","field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0)","nsg + nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu = {nsbu},","template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)","if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! #","import sys, os, json import numpy as np from ctypes import * from","= field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise","time for integrator contribution of linear algebra 'time_qp', # cpu time qp solution","\\'rti_phase\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if","'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external","def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy","'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file =","+ nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e},","nsg ns = nsbx + nsbu + nsh + nsg + nsphi wrong_field","out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type ==","anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref',","dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e","regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with info about","= cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields +","stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field","['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not in out_fields","out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims,","field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be","description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__)","of the solver: :param stage_: integer corresponding to shooting node :param field_: string,","value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims):","reproduce the above copyright notice, # this list of conditions and the following","solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name +","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_","make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time","idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent","nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\","+ f'Detected ns = {ns} = nsbx + nsbu + nsg + nsh","raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif","Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension","ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT","!= ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent","nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack","SUCH DAMAGE.; # import sys, os, json import numpy as np from ctypes","constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,","HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,","else: if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {}","should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,","!= ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny:","ocp with current input \"\"\" status = self.shared_lib.acados_solve() return status def get(self, stage_,","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in","'step_length' :param value_: of type int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks']","['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields:","acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp)","model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type ==","np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if","time for integrator contribution of external function calls 'time_sim_la', # cpu time for","f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path","raise Exception('solver option {} must be of type int. You have {}.'.format(field_, type(value_)))","nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg =","shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf","W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' +","nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise","out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype =","ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.'","dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] !=","\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching","or without # modification, are permitted provided that the following conditions are met:","acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] #","if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3:","cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x',","if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np:","with info about last iteration 'stat_m', 'stat_n', ] field = field_ field =","generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp","outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file,","if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\","nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e:","from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from","status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\" get the last","json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name)","'t'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_","W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent","numerical data in the cost module of the solver: :param stage_: integer corresponding","int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_","nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh =","elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx =","and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e)","= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data =","= ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e","print('\\niter\\tqp_stat\\tqp_iter') if stat.shape[0]>3: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if","Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr):","np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if","casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty')","# path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny","self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if","input \"\"\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\" get","= constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0]","constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,","constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx","acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if","ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as","c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)]","json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make","['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields =","constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions","the documentation # and/or other materials provided with the distribution. # # THIS","PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;","render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir,","is not a valid argument.\\ \\nPossible values are {}. Exiting.\".format(field, \\ constraints_fields +","* from casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode","this list of conditions and the following disclaimer in the documentation # and/or","np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi","= c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype =","template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0:","nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi:","raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e","i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not","elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi =","values are {}. Exiting.\".format(field, \\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes","this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise","\"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \"Zu\" dim =","model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate","= 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file,","self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype","\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \"\"\"","self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64)","= field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {}","W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if","object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] =","nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e =","= np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.')","elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e =","'{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function","with dimension {} (you have {})'.format( \\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,","constraint module of the solver: Parameters: :param stage_: integer corresponding to shooting node","' for field \"{}\" with dimension {} (you have {})'.format( \\ field_, tuple(dims),","dimension ny_e: regarding W_e, cost_y_expr_e.' + \\ f'\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] !=","!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu =","laod class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): #","table with info about last iteration 'stat_m', 'stat_n', ] field = field_ field","casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif","ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent","is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding","ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp =","about last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if","'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir,","else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh =","matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny,","cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise","'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\" fields = ['time_tot', #","field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,","HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT","raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr =","ctypes import * from casadi import CasadiMeta, Function, SX from copy import deepcopy","!= dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx","template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file","= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg =","if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e","MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT","for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns = {ns} =","current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f:","solve the ocp with current input \"\"\" status = self.shared_lib.acados_solve() return status def","generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':","int(stat[0][jj]), stat[1][jj], stat[2][jj], \\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj],","out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e","nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent","os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout","out # Note: this function should not be used anymore, better use cost_set,","len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set():","= self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\" get the last solution","lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension","wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size","in the following order: \\n [ lbu lbx lg lh lphi ubu ubx","<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This","0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh,","= {ns} = nsbx + nsbu + nsg + nsh + nsphi.\\n\\t'\\ +","stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\n')","ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng =","idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,))","of type int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length']","f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny != 0:","not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0):","source and binary forms, with or without # modification, are permitted provided that","regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu =","acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if","f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh},","uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e =","'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints","Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:","dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz =","(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE","option {} must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes","dim = cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field},","as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object","= {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi =","shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate","field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config,","field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\"","raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only value 0 for SQP-type solvers')","model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model","c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields:","min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data,","self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p,","an invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes","nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent","dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0]","raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\" with dimension {} (you","= c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype =","print('\\niter\\tres_stat\\tres_eq\\t\\tres_ineq\\tres_comp\\tqp_stat\\tqp_iter') if stat.shape[0]>7: print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:e}\\t{:e}\\t{:e}\\t{:e}\\t{:d}\\t{:d}'.format( \\ int(stat[0][jj]), stat[1][jj], stat[2][jj],","self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\","con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if","= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if","= np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.')","AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict #","NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): #","{nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns","1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0:","value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes","nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise","regarding W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if","with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p","if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,","stage = c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes =","acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def","acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def","(field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\","def cost_set(self, stage_, field_, value_): \"\"\" set numerical data in the cost module","str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase':","string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \"\"\"","json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir","cpu time previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu","!= ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n')","= cost.zu_e.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with","functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp,","acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file","of soft upper inequality constraints \\n \"\"\" out_fields = ['x', 'u', 'z', 'pi',","= np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.')","c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64)","field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e","os, json import numpy as np from ctypes import * from casadi import","this list of conditions and the following disclaimer. # # 2. Redistributions in","value_ to avoid conversion issues value_ = value_.astype(float) field = field_ field =","= dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct, v","raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],","elif cost.zl.shape[0] != ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] !=","== 'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument","0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else:","== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file,","template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact with the acados ocp solver","# terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field =","<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,","] field = field_ field = field.encode('utf-8') if (field_ not in fields): raise","W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e =","json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP'","dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\ [c_void_p,","\\ ' for field \"{}\" with dimension {} (you have {})'.format( \\ field_,","self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension","= constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!')","elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e):","ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if","= np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.')","raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\ \\n Possible values are {}.","with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def","cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' +","raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e","shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl',","## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)","int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be of type","for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields:","'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not in fields):","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in","<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file","columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\","Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz != 0 and cost.Vz.shape[0]","<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is","nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu =","time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes","self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) ==","dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_,","dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps","= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)","if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,","value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set():","ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif","and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape =","the following disclaimer in the documentation # and/or other materials provided with the","# np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0]","'{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \"\"\" class to interact with the","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def","# # Redistribution and use in source and binary forms, with or without","and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if","or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else:","json_file): name = acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format(","internally organized in the following order: \\n [ lbu lbx lg lh lphi",".. note:: pi: multipliers for dynamics equality constraints \\n lam: multipliers for inequalities","'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\n the inequalities","or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' +","acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class","{} is not a valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields))","!= 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] !=","for inequalities \\n t: slack variables corresponding to evaluation of all inequalities (at","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage,","= cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \"zl_e\" dim = cost.zl_e.shape[0] elif","'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double))","+ model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True","model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp)","constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e","'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file =","value_): \"\"\" set options of the solver: Parameters: :param field_: string, e.g. 'print_level',","constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,","model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx =","json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_'","self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data,","# NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name):","dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p","= cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif","in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)","in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps))","= ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option","OF SUCH DAMAGE.; # import sys, os, json import numpy as np from","return status def get(self, stage_, field_): \"\"\" get the last solution of the","for integrator 'time_sim_ad', # cpu time for integrator contribution of external function calls","= ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0]","# laod class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items():","!= dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost #","and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name)","# treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype","if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') #","c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p","\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note::","usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif","stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess'","convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: #","dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e =","\"Zl_e\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \"Zu_e\" dim =","0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ >","def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily","nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,))","= self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver =","\"{}\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg)","nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if","constraints \\n lam: multipliers for inequalities \\n t: slack variables corresponding to evaluation","number of SQP iterations 'statistics', # table with info about last iteration 'stat_m',","convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: #","constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,","value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from","+ nsh + nsphi.\\n\\t'\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg","constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg","self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out #","constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape","environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path))","integrator contribution of external function calls 'time_sim_la', # cpu time for integrator contribution","[]): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not","idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] !=","Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent","opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if","json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file","0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\ ' for field","raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0]","'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file =","set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu',","OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR","cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting","should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise","\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p,","list of conditions and the following disclaimer. # # 2. Redistributions in binary","dynamics equality constraints \\n lam: multipliers for inequalities \\n t: slack variables corresponding","function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on","nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\"","else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz","tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\","not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if","attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non","= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","a valid argument.\\ \\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in","cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with","THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" #","constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0]","self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims =","c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes","c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_,","if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding","cost.zu.shape[0] if wrong_field != \"\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension","'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\'rti_phase\\' can ' 'take only","have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!')","raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif","is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not","dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data","casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] !=","np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type","have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.')","C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else:","dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0]","skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp,","= 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","{wrong_field}, with dimension {dim}, \\n\\t'\\ + f'Detected ns_e = {ns_e} = nsbx_e +","= np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps =","raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu = 0","dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape","nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e","== 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent","double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be of type","'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding","su: slack variables of soft upper inequality constraints \\n \"\"\" out_fields = ['x',","# cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', #","# 2. Redistributions in binary form must reproduce the above copyright notice, #","uh uphi; \\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]","value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \"{}\" '.format(field_) msg","raise Exception('solver option {} must be of type float. You have {}.'.format(field_, type(value_)))","elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field !=","W, Vx, Vu, Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1]","raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif","= nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu:","ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e","np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e","'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file):","'.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p","if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,","dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.')","0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0:","idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent","nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr)","out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)","0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0","value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\" with","lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics","True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny,","dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e","SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF","Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i]","ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input","!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\ f'\\nGot","nsh + nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field","class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc","raise Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny =","Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng =","CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY","qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter',","> 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e >","A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER","== 'statistics': sqp_iter = self.get_stats(\"sqp_iter\") stat_m = self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size =","all inequalities (at the solution) \\n sl: slack variables of soft lower inequality","OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF","# path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx:","if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] !=","= casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is","# and/or other materials provided with the distribution. # # THIS SOFTWARE IS","# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE","sl: slack variables of soft lower inequality constraints \\n su: slack variables of","= opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1:","nsphi_e.\\n\\t'\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e","{ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e =","IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,","c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,","= 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file,","is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if","-- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF':","in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\","DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): # dlclose(handle)","IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY","\\n lam: multipliers for inequalities \\n t: slack variables corresponding to evaluation of","is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,))","print('\\tqp_res_stat\\tqp_res_eq\\tqp_res_ineq\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\t{:d}\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\","self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \"\"\" set options of","c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p","not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n","out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field,","c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =","AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL","in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost","cost.zu_e.shape[0] != ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\":","def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims","ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field =","json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =","POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out","constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e","{} must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes =","acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict","+ nsh + nsg + nsphi wrong_field = \"\" if cost.Zl.shape[0] != ns:","usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e",":param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int,","Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e =","Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng if","lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension","conditions and the following disclaimer in the documentation # and/or other materials provided","= 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints:","nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise","np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg","!= ns_e: wrong_field = \"Zu_e\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field","fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data =","'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file =","self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \"\"\" set numerical","\"\"\" fields = ['time_tot', # total cpu time previous call 'time_lin', # cpu","= np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.')","= casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is","over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) #","on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file,","= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class","generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts",".acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\ format_class_dict, ocp_check_against_layout,","the solution) \\n sl: slack variables of soft lower inequality constraints \\n su:","value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes","'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension","not isinstance(value_, float): raise Exception('solver option {} must be of type float. You","np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\","constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\ or constraints.D.shape[0] !=","c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def","/ tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps)","> 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\ f' = tf != sum(opts.time_steps) = {tf}.')","model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C","and ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if","self.get_stats(\"stat_m\") stat_n = self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1,","= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #","'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal","None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f,","if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\ f'\\nGot","!= ns: wrong_field = \"zu\" dim = cost.zu.shape[0] if wrong_field != \"\": raise","is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz","value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields: raise","dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:","of the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim',","__del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!!","dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for","# constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file =","!= ng \\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg,","= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape","acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model","solver: Parameters: :param stage_: integer corresponding to shooting node :param field_: string, e.g.","'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load","Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx +","in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible","dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict)","as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure","field_, value_): \"\"\" set numerical data in the constraint module of the solver:","OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON","nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,))","dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if","= ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field =","nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent","constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e,","in constraints_fields + cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid","self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should not be","acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e","out_data) return out # Note: this function should not be used anymore, better","appropriate size \"\"\" # cast value_ to avoid conversion issues value_ = value_.astype(float)","constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh,","self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\","regarding lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh","of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_","raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 ==","LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,","stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype","implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type","the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND","= constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0","if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0]","model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model,","nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns =","nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e:","# nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir =","Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0]","organized in the following order: \\n [ lbu lbx lg lh lphi ubu","'initialize_t_slacks', 'step_length' :param value_: of type int, float \"\"\" int_fields = ['print_level', 'rti_phase',","Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] !=","else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0]","generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost","= constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension","acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct,","<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,","['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi',","self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) ==","to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of","'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if","= 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file,","fields = ['time_tot', # total cpu time previous call 'time_lin', # cpu time","total cpu time previous call 'time_lin', # cpu time for linearization 'time_sim', #","wrong_field = \"\" if cost.Zl.shape[0] != ns: wrong_field = \"Zl\" dim = cost.Zl.shape[0]","'sl', 'su',] .. note:: regarding lam, t: \\n the inequalities are internally organized","= \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage,","cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,","template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP'","= casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] !=","idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,))","self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_","formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not","json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file =","Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n')","= self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self):","# table with info about last iteration 'stat_m', 'stat_n', ] field = field_","mismatching dimension for field \"{}\" '.format(field_) msg += 'with dimension {} (you have","is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization')","!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\","'time_lin', # cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad',","== 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate","nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0]","cost_set(self, stage_, field_, value_): \"\"\" set numerical data in the cost module of","{} must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes =","{})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if","!= ns_e: wrong_field = \"zu_e\" dim = cost.zu_e.shape[0] if wrong_field != \"\": raise","# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS","'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics':","= {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform","CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype =","raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in","continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp,","== 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\"ocp_generate_external_functions: unknown integrator type.\") if acados_ocp.solver_options.hessian_approx == 'EXACT':","other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different","[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)","if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,","out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else:","+ f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh =","integrator 'time_sim_ad', # cpu time for integrator contribution of external function calls 'time_sim_la',","coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,","code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise","AcadosOcpSolver: \"\"\" class to interact with the acados ocp solver C object \"\"\"","for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf >","deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf","'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics',","= 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file","value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters","float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization']","field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]","elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e =","= constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent","and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf","out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p,","value_: of type int, float \"\"\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields =","= ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] !=","W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type ==","json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)","Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one","c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp","regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] -","constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension","c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes","License # # Redistribution and use in source and binary forms, with or","Vz.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\n') if cost.Vx.shape[1] != dims.nx and","in the cost module of the solver: :param stage_: integer corresponding to shooting","if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,","== 'IRK': # implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model,","self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for","!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e","nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi =","TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE","constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e,","regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise","regarding W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal","yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0]","STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY","= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape","self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape","= ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in","if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape","= value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_ >","INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS","= tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path =","inequalities \\n t: slack variables corresponding to evaluation of all inequalities (at the","raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c'","cost module of the solver: :param stage_: integer corresponding to shooting node :param","if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =","= self.get_stats(\"stat_n\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ),","WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE","+ cost_fields + out_fields: raise Exception(\"AcadosOcpSolver.set(): {} is not a valid argument.\\ \\nPossible","out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))","FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT","is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding","terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise","is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif","f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e","structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict =","= c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype =","regarding W, Vx, Vu.' + \\ f'\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\n') if dims.nz !=","the ocp with current input \"\"\" status = self.shared_lib.acados_solve() return status def get(self,","int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\t{:e}\\t{:e}\\t{:e}\\t{:e}'.format( \\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\n') return","Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\ \\n Possible values are {}. Exiting.'.format(field_, out_fields","field \"{}\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,","from casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import","dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e:","strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as","['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_","non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__))","'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file =","to evaluation of all inequalities (at the solution) \\n sl: slack variables of","regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise","if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data,","the following disclaimer. # # 2. Redistributions in binary form must reproduce the","{})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes =","elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","= {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\n\\t'\\ + f'With nsbx_e","Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if","calls 'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp', #","Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding","W, yref.' + \\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny # terminal if","2. Redistributions in binary form must reproduce the above copyright notice, # this","ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr)","\"\"\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \"\"\" get the","path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or","symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict,","time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the","-*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,","# cast value_ to avoid conversion issues value_ = value_.astype(float) field = field_","solution of the solver: :param stage: integer corresponding to shooting node :param field_:","ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu","'.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data","else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model,","# nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np","out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type ==","to shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't',","# terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir","'t'] # cast value_ to avoid conversion issues value_ = value_.astype(float) field =","to interact with the acados ocp solver C object \"\"\" def __init__(self, acados_ocp,","= np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out","Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\ ' for field \"{}\" with dimension {} (you have","can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type","constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension","c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self,","if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent","of appropriate size \"\"\" # cast value_ to avoid conversion issues value_ =","\\ f'\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny =","c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\ self.nlp_dims, self.nlp_out,","render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..')","= \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \"zu\" dim","= {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,))","if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,","ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf","dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx","nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e","nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e =","c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields:","'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader","!= 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e:","acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e","!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi =","value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver","os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so'","inequality constraints \\n \"\"\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields","= field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\ [c_void_p, c_void_p, c_char_p, c_char_p]","dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if","dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0]","inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization","SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes","cost.zl.shape[0] != ns: wrong_field = \"zl\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns:","or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else:","use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields","if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must","self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype","c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \"\"\" solve the ocp","value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\ [c_void_p, c_void_p,","source code must retain the above copyright notice, # this list of conditions","value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields =","= np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.')","opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] !=","opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx))","'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp', # cpu","elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]","# dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu"],"string":"[\n \"and the following disclaimer in the documentation # and/or other materials provided with\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and\",\n \"ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],\",\n \"0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only\",\n \"ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model\",\n \"self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python\",\n \"{}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\\",\n \"generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost\",\n \"+= 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data,\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if\",\n \"nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e:\",\n \"template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir =\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field,\",\n \"dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path\",\n \"Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension\",\n \"in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields\",\n \"in binary form must reproduce the above copyright notice, # this list of\",\n \"generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\")\",\n \"c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost\",\n \"ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent\",\n \"# terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ +\",\n \"== 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]),\",\n \"{}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]\",\n \"# cpu time for integrator contribution of external function calls 'time_sim_la', # cpu\",\n \"field_ == 'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve():\",\n \"[c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\\",\n \"field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be\",\n \"= np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.')\",\n \"# cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', #\",\n \"from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from\",\n \"setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path):\",\n \"ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent\",\n \"= constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent\",\n \"'su'] field = field_ field = field.encode('utf-8') if (field_ not in out_fields +\",\n \"in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c'\",\n \"option {} must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi\",\n \"self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in,\",\n \"json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict\",\n \"elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name #\",\n \"acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h'\",\n \"regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0]\",\n \"\\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C,\",\n \"dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:\",\n \"constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu\",\n \"dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np =\",\n \"!= ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of\",\n \"THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\",\n \"'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir =\",\n \"slack variables of soft lower inequality constraints \\\\n su: slack variables of soft\",\n \"Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu =\",\n \"constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e\",\n \"status def get(self, stage_, field_): \\\"\\\"\\\" get the last solution of the solver:\",\n \"stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields\",\n \"> 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e >\",\n \"out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile'\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif\",\n \"ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field =\",\n \"loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{}\",\n \"constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't']\",\n \"render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h'\",\n \"Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields,\",\n \"self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"\\\\n sl: slack variables of soft lower inequality constraints \\\\n su: slack variables\",\n \"wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0]\",\n \"elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg =\",\n \"False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\\",\n \"nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] !=\",\n \"provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf\",\n \"ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct, v in\",\n \"(you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #\",\n \"inequalities (at the solution) \\\\n sl: slack variables of soft lower inequality constraints\",\n \"ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else:\",\n \"= c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype =\",\n \"{nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}')\",\n \"float): raise Exception('solver option {} must be of type float. You have {}.'.format(field_,\",\n \"= dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh >\",\n \"= np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.')\",\n \"nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx =\",\n \"# discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf /\",\n \"value_): \\\"\\\"\\\" set numerical data in the cost module of the solver: :param\",\n \"np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints =\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS':\",\n \"dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif\",\n \"# uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes):\",\n \"attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct]\",\n \"acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not\",\n \"or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) /\",\n \"Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0]\",\n \"CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR\",\n \"Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be\",\n \"(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS\",\n \"lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e\",\n \"should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,\",\n \"TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\",\n \"solver: :param stage: integer corresponding to shooting node :param field_: string in ['x',\",\n \"'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type\",\n \"acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type ==\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver:\",\n \"ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() #\",\n \"c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p\",\n \"if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim},\",\n \"Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type\",\n \"for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not isinstance(v,\",\n \"ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []):\",\n \"= 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir\",\n \"multipliers for dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n t: slack\",\n \"= ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not in\",\n \"self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else:\",\n \"self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in,\",\n \"following conditions are met: # # 1. Redistributions of source code must retain\",\n \"np from ctypes import * from casadi import CasadiMeta, Function, SX from copy\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"\\\"\\\"\\\" get the information of the last solver call: :param field_: string in\",\n \"# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN\",\n \"stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format(\",\n \"value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\\",\n \"else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise\",\n \"dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent\",\n \"e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid\",\n \"> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"{} is an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields +\",\n \"either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf -\",\n \"value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0,\",\n \"modification, are permitted provided that the following conditions are met: # # 1.\",\n \"if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should\",\n \"idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent\",\n \"elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0:\",\n \"if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')\",\n \"acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx))\",\n \"if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can\",\n \"!= nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e,\",\n \"{nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif\",\n \"cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib')\",\n \"on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0:\",\n \"ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict,\",\n \"= 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints:\",\n \"solution) \\\\n sl: slack variables of soft lower inequality constraints \\\\n su: slack\",\n \"print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir\",\n \"# make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator\",\n \"!= nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx,\",\n \"generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model)\",\n \"field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts,\",\n \"cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) #\",\n \"= np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.')\",\n \"dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] !=\",\n \"nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.')\",\n \"nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi\",\n \"= {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e =\",\n \"= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat\",\n \"cpu time for integrator contribution of linear algebra 'time_qp', # cpu time qp\",\n \"following disclaimer. # # 2. Redistributions in binary form must reproduce the above\",\n \"dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =\",\n \"= np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.')\",\n \"= \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif\",\n \"is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding\",\n \"= acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts =\",\n \"= ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if\",\n \"['time_tot', # total cpu time previous call 'time_lin', # cpu time for linearization\",\n \"(without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number\",\n \"is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np\",\n \"strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims)\",\n \"Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics\",\n \"ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load\",\n \"dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict\",\n \"cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension\",\n \"lbu lbx lg lh lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx\",\n \"out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes\",\n \"field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {}\",\n \"= nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi =\",\n \"self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type\",\n \"make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model)\",\n \"regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0\",\n \"if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else:\",\n \"regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0]\",\n \"min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data\",\n \"= '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type ==\",\n \"constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e\",\n \"stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e,\",\n \"CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode\",\n \"Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding\",\n \"self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts()\",\n \"nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension:\",\n \"out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file\",\n \"ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent\",\n \"np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e\",\n \"AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\\",\n \"# render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib')\",\n \"constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e\",\n \"template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e >\",\n \"from ctypes import * from casadi import CasadiMeta, Function, SX from copy import\",\n \"value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu))\",\n \"values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ##\",\n \"should not be used anymore, better use cost_set, constraints_set def set(self, stage_, field_,\",\n \"v in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue\",\n \"acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif\",\n \"import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list,\",\n \"if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims =\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent\",\n \"!= 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take\",\n \"else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding\",\n \"WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #\",\n \"'{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and\",\n \"raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else:\",\n \"# cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution of\",\n \"+ \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0:\",\n \"ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field =\",\n \"field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c'\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields:\",\n \"and ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if\",\n \"- opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf\",\n \"but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0]\",\n \"constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e\",\n \"skip non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct)\",\n \"\\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl',\",\n \"LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,\",\n \"if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid\",\n \"if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_):\",\n \"'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time previous call\",\n \"\\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise\",\n \"+ nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field =\",\n \"[] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape\",\n \"= \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim\",\n \"dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver\",\n \"constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e,\",\n \"f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description\",\n \"of conditions and the following disclaimer in the documentation # and/or other materials\",\n \"template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file,\",\n \"* np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N,\",\n \"cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny,\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free()\",\n \"\\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0]\",\n \"isinstance(value_, int): raise Exception('solver option {} must be of type int. You have\",\n \"column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u)\",\n \"must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 =\",\n \"dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0]\",\n \"\\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su']\",\n \":param value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields\",\n \"nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh\",\n \"cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' +\",\n \"time previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu time\",\n \"conditions and the following disclaimer. # # 2. Redistributions in binary form must\",\n \"if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_,\",\n \"json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field,\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] !=\",\n \"!= nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu,\",\n \"field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\\",\n \"= constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent\",\n \"conditions are met: # # 1. Redistributions of source code must retain the\",\n \"not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if\",\n \"np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e =\",\n \"# cpu time for integrator contribution of linear algebra 'time_qp', # cpu time\",\n \"Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0]\",\n \"ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\",\n \"= constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent\",\n \"stage: integer corresponding to shooting node :param field_: string in ['x', 'u', 'z',\",\n \".utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def\",\n \"opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise\",\n \"f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise\",\n \"cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes =\",\n \"regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e =\",\n \"IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os,\",\n \"in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h'\",\n \"usphi] .. note:: pi: multipliers for dynamics equality constraints \\\\n lam: multipliers for\",\n \"is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding\",\n \"json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp solver C\",\n \"integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_: of\",\n \"nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,))\",\n \"+ nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e =\",\n \"ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or\",\n \"out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field\",\n \"nsbu + nsh + nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] !=\",\n \"<NAME>, <NAME>, <NAME> # # This file is part of acados. # #\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension\",\n \"elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] !=\",\n \"'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"# cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without\",\n \"True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims()\",\n \"stage_, field_): \\\"\\\"\\\" get the last solution of the solver: :param stage: integer\",\n \"= np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type ==\",\n \"'take only value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8')\",\n \"= cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding\",\n \"from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from\",\n \"' for field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data\",\n \"c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_,\",\n \"regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and\",\n \"(at the solution) \\\\n sl: slack variables of soft lower inequality constraints \\\\n\",\n \"ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model --\",\n \"Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx\",\n \"in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on\",\n \"equality constraints \\\\n lam: multipliers for inequalities \\\\n t: slack variables corresponding to\",\n \"wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\"\",\n \"np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding\",\n \"constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file,\",\n \"upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't']\",\n \"wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\"\",\n \"constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush\",\n \"is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr):\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e ==\",\n \"elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] !=\",\n \"Redistribution and use in source and binary forms, with or without # modification,\",\n \"not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent\",\n \"+ nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg},\",\n \"= c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp =\",\n \"argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\\",\n \"BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\",\n \"constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx\",\n \"cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ##\",\n \"to avoid conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8')\",\n \"{})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)\",\n \"Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns =\",\n \"raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial\",\n \"'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp\",\n \"usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh + nsg\",\n \"is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims\",\n \"nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e\",\n \"if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')\",\n \"usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif\",\n \"lh lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi\",\n \"self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if\",\n \"{wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx +\",\n \"= acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp,\",\n \"argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers')\",\n \"con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx\",\n \"ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model']\",\n \"json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"+ out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values are\",\n \"self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can '\",\n \"out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS':\",\n \"if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] !=\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return\",\n \"nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e:\",\n \"= get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO:\",\n \"dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] !=\",\n \"out_file, template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type ==\",\n \"object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model #\",\n \"= constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent\",\n \"json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() #\",\n \"json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout\",\n \"= c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype =\",\n \"== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e\",\n \"= nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng\",\n \"corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate\",\n \"'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not\",\n \"'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu\",\n \"raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif\",\n \"regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise\",\n \"raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if\",\n \"uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr =\",\n \"= ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] #\",\n \"value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_\",\n \"not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file\",\n \"in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be of\",\n \".generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model\",\n \"out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path)\",\n \"if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name)\",\n \"= {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns =\",\n \"and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name)\",\n \"2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,\",\n \"nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field\",\n \"field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,\",\n \"0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e,\",\n \"= '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir =\",\n \"self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out()\",\n \"= \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for\",\n \"acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi >\",\n \"vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path\",\n \"cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] !=\",\n \"out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name)\",\n \"= np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes =\",\n \"\\\"\\\"\\\" set options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase',\",\n \"np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)),\",\n \"= ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny\",\n \"regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0]\",\n \"= opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)):\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear\",\n \"nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,))\",\n \"acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file,\",\n \"constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx',\",\n \"int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type ==\",\n \"field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size\",\n \"!= dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg +=\",\n \"acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file,\",\n \"ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field =\",\n \"stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif\",\n \"t: slack variables corresponding to evaluation of all inequalities (at the solution) \\\\n\",\n \"regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.'\",\n \"{nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e\",\n \"np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if\",\n \"'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid\",\n \"inequality constraints \\\\n su: slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\"\",\n \"nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e:\",\n \"stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data\",\n \"# TODO: maybe make one funciton with formatting for acados_struct, v in ocp_layout.items():\",\n \"(constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape =\",\n \"Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] !=\",\n \"\\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field\",\n \"field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts,\",\n \"attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model'])\",\n \"mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims\",\n \"= acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4,\",\n \"constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh\",\n \"# nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type\",\n \"# initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0\",\n \"{tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json',\",\n \"ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg\",\n \"templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data)\",\n \"f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent\",\n \"is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding\",\n \"in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h'\",\n \"constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension\",\n \"constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu\",\n \"['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise\",\n \"!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg\",\n \"nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi\",\n \"= json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout\",\n \"nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,))\",\n \"the constraint module of the solver: Parameters: :param stage_: integer corresponding to shooting\",\n \"Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict\",\n \"+ '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'):\",\n \"= np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter =\",\n \"time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf)\",\n \"np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] !=\",\n \"template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if\",\n \"dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m\",\n \"tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\\",\n \"from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from\",\n \"if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding\",\n \"cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns:\",\n \"# 1. Redistributions of source code must retain the above copyright notice, #\",\n \"np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\\",\n \"sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_,\",\n \"regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e\",\n \"yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == [] and\",\n \"out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name)\",\n \"dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e):\",\n \"= value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape !=\",\n \"'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field =\",\n \"raise Exception('solver option {} must be of type str. You have {}.'.format(field_, type(value_)))\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1:\",\n \"'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e ==\",\n \"-*- coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>,\",\n \"in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if\",\n \"ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush\",\n \"dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu =\",\n \"\\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes\",\n \"ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz\",\n \"Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e):\",\n \"if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p):\",\n \"ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\",\n \"self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes =\",\n \"use in source and binary forms, with or without # modification, are permitted\",\n \"and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL\",\n \"linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for integrator\",\n \"= '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH'\",\n \"if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp,\",\n \"templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with\",\n \"cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if\",\n \"raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0]\",\n \"nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e)\",\n \"'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external\",\n \"stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information\",\n \"used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields =\",\n \"following order: \\\\n [ lbu lbx lg lh lphi ubu ubx ug uh\",\n \"0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np,\",\n \"dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e):\",\n \"(you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_\",\n \"for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} =\",\n \"# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN\",\n \"time for integrator 'time_sim_ad', # cpu time for integrator contribution of external function\",\n \"values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_\",\n \"nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh:\",\n \"discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module =\",\n \"= 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class\",\n \"'{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH'\",\n \"nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu\",\n \"acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model,\",\n \"self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p,\",\n \"C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e\",\n \"\\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e +\",\n \"last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_\",\n \"template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ##\",\n \"= self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in\",\n \"== 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e\",\n \"Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.'\",\n \"cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field\",\n \"GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)\",\n \"separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data\",\n \"stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter')\",\n \"IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\",\n \"constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e\",\n \"= ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues\",\n \"'{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir\",\n \"constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension\",\n \"cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0]\",\n \"tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent\",\n \"on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file,\",\n \"if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0]\",\n \"integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) #\",\n \"\\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\")\",\n \"lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e)\",\n \"dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh):\",\n \"elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e =\",\n \"field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver:\",\n \"stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray(\",\n \"self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get the last solution of\",\n \"def solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve()\",\n \"Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type\",\n \"discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0]\",\n \"constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"order: \\\\n [ lbu lbx lg lh lphi ubu ubx ug uh uphi;\",\n \"self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\"\",\n \"nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise\",\n \"\\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx + nsbu + nsg +\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"must retain the above copyright notice, # this list of conditions and the\",\n \"wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size\",\n \"constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,\",\n \"options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver: Parameters: :param field_: string,\",\n \"usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\\\n\",\n \"cast value_ to avoid conversion issues value_ = value_.astype(float) field = field_ field\",\n \"dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims,\",\n \"template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS':\",\n \"W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if\",\n \"column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 #\",\n \"= {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and\",\n \"== 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e:\",\n \"solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model =\",\n \"tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__)\",\n \"'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created =\",\n \"for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve():\",\n \"raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0]\",\n \"range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj]))\",\n \"constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\\",\n \"def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx',\",\n \"else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise\",\n \"nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.')\",\n \"or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else:\",\n \"dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for\",\n \"dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0\",\n \"regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"corresponding to evaluation of all inequalities (at the solution) \\\\n sl: slack variables\",\n \"unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts =\",\n \"the above copyright notice, # this list of conditions and the following disclaimer.\",\n \"ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0]\",\n \"= casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and\",\n \"nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg =\",\n \"generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name\",\n \"# POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy as\",\n \"ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict =\",\n \"ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct,\",\n \"!= ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints\",\n \"note:: regarding lam, t: \\\\n the inequalities are internally organized in the following\",\n \"dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg\",\n \"self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]):\",\n \"Exception('solver option {} must be of type int. You have {}.'.format(field_, type(value_))) else:\",\n \"json import numpy as np from ctypes import * from casadi import CasadiMeta,\",\n \"cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file =\",\n \"\\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self):\",\n \"Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e\",\n \"open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate\",\n \"wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\\",\n \"fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible values are\",\n \"of acados. # # The 2-Clause BSD License # # Redistribution and use\",\n \"nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise\",\n \"== 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type ==\",\n \"out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name)\",\n \"value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the\",\n \"if field_ == 'rti_phase': if value_ < 0 or value_ > 2: raise\",\n \"np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)\",\n \"nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise\",\n \"return out # Note: this function should not be used anymore, better use\",\n \"regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type\",\n \"with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e\",\n \"= self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current\",\n \"with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) #\",\n \"= self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros(\",\n \"'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e ==\",\n \"np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS':\",\n \"WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND\",\n \"from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict,\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise\",\n \"ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column\",\n \"acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict =\",\n \"lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension\",\n \"Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e =\",\n \"# terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e:\",\n \"constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,\",\n \"in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj],\",\n \"ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding\",\n \"constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx,\",\n \"stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for\",\n \"\\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type solvers') field = field_\",\n \"wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\"\",\n \"field_): \\\"\\\"\\\" get the information of the last solver call: :param field_: string\",\n \"np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if\",\n \"the acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created =\",\n \"# # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #\",\n \"is part of acados. # # The 2-Clause BSD License # # Redistribution\",\n \"if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding\",\n \"\\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim =\",\n \"= 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file,\",\n \"raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field,\",\n \"mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format(field_,\",\n \"cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] !=\",\n \"if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh,\",\n \"documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE\",\n \"acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type\",\n \"in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints\",\n \"= cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)\",\n \"+ nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e\",\n \"'IRK': # implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts)\",\n \"value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_\",\n \"Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0]\",\n \"DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;\",\n \"0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h'\",\n \"string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\"\",\n \"if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding\",\n \"ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name =\",\n \"in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c'\",\n \"is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0\",\n \"code must retain the above copyright notice, # this list of conditions and\",\n \"acados. # # The 2-Clause BSD License # # Redistribution and use in\",\n \"# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f:\",\n \"elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] !=\",\n \"raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif\",\n \"+ f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e =\",\n \"= ['time_tot', # total cpu time previous call 'time_lin', # cpu time for\",\n \"the following conditions are met: # # 1. Redistributions of source code must\",\n \"1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def\",\n \"string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver\",\n \"value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\\",\n \"[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if\",\n \"msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension\",\n \"'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file,\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"= '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with\",\n \"if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True)\",\n \"stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj],\",\n \"out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if\",\n \"'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension\",\n \"ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny !=\",\n \"= nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx\",\n \"self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver\",\n \"regarding model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny\",\n \"Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny\",\n \"cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e !=\",\n \"else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but\",\n \"opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps\",\n \"THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF\",\n \"dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e +\",\n \"the following order: \\\\n [ lbu lbx lg lh lphi ubu ubx ug\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_,\",\n \"constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,\",\n \"= ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u',\",\n \"if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f'\",\n \"# setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not\",\n \"!= nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu =\",\n \"= casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p)\",\n \"get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape\",\n \"= 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file,\",\n \"field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is\",\n \"self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp\",\n \"np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if\",\n \"shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 =\",\n \"'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts\",\n \"nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e:\",\n \"ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment json_path =\",\n \"else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but\",\n \"= casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,))\",\n \".generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost\",\n \"Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"= 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file\",\n \"notice, # this list of conditions and the following disclaimer in the documentation\",\n \"for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time for\",\n \"+ out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype\",\n \"np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps\",\n \"c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,\",\n \"in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible values\",\n \"uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] ..\",\n \"self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver,\",\n \"== 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp,\",\n \"if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding\",\n \"nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e\",\n \"or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.')\",\n \"= acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file)\",\n \"'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if\",\n \"idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e +\",\n \"cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if\",\n \"SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import\",\n \"lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality\",\n \"Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e\",\n \"open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load\",\n \"'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if\",\n \"cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\\",\n \"D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh\",\n \"acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK':\",\n \"\\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\",\n \"up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise\",\n \"algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside qp\",\n \"model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name,\",\n \"0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field\",\n \"future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,\",\n \"stage_, field_, value_): \\\"\\\"\\\" set numerical data in the cost module of the\",\n \"ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng\",\n \"ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot\",\n \"= c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] !=\",\n \"function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer\",\n \"constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding\",\n \"integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param\",\n \"!= 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,\",\n \"= 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost -\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"field = field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields):\",\n \"raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if\",\n \"# strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w')\",\n \"stage_, field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type ==\",\n \"and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.'\",\n \"BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR\",\n \"have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent\",\n \"generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts =\",\n \"int): raise Exception('solver option {} must be of type int. You have {}.'.format(field_,\",\n \"is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims =\",\n \"dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) #\",\n \"acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function\",\n \"of the solver: :param stage: integer corresponding to shooting node :param field_: string\",\n \"c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat\",\n \"OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER\",\n \"## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0\",\n \"out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7:\",\n \"this function should not be used anymore, better use cost_set, constraints_set def set(self,\",\n \"elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for\",\n \"Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts\",\n \"binary forms, with or without # modification, are permitted provided that the following\",\n \"W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e\",\n \"and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr)\",\n \"['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t:\",\n \"argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m',\",\n \"columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu\",\n \"OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #\",\n \"variables corresponding to evaluation of all inequalities (at the solution) \\\\n sl: slack\",\n \"POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape)\",\n \"with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx + nsbu\",\n \"is not a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if\",\n \"self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in\",\n \"columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e =\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"\\\"\\\"\\\" set numerical data in the cost module of the solver: :param stage_:\",\n \"if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,\",\n \"nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e\",\n \"import numpy as np from ctypes import * from casadi import CasadiMeta, Function,\",\n \"set numerical data in the cost module of the solver: :param stage_: integer\",\n \"nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu +\",\n \"maybe make one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip\",\n \"C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C\",\n \"dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or\",\n \"binary form must reproduce the above copyright notice, # this list of conditions\",\n \"= acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx\",\n \"print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information of the last solver\",\n \"!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush =\",\n \"nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.')\",\n \"CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,\",\n \"= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config,\",\n \"cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have\",\n \"PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\\",\n \"casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if\",\n \"<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> #\",\n \"constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e\",\n \"import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import\",\n \"= 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0\",\n \"\\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p']))\",\n \"raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS':\",\n \"Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny\",\n \"acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file,\",\n \"c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {}\",\n \"stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp')\",\n \"self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\"\",\n \"dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] !=\",\n \"elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True)\",\n \"!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg =\",\n \"self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes =\",\n \":param stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param\",\n \"= 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on\",\n \"nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' +\",\n \"{} must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file,\",\n \"'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj])))\",\n \"Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0]\",\n \"## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0]\",\n \"== 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0\",\n \"ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e\",\n \"lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension\",\n \"field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if\",\n \"out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter')\",\n \"dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with\",\n \"idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent\",\n \"double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_,\",\n \"# The 2-Clause BSD License # # Redistribution and use in source and\",\n \"the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad',\",\n \"if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,\",\n \"time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump\",\n \"field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config,\",\n \"= format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None)\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims =\",\n \"dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi):\",\n \"cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have\",\n \"AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict,\",\n \"= \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes\",\n \"in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model\",\n \"and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0]\",\n \"if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx' :param value_:\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,))\",\n \"field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter']\",\n \"2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2\",\n \":param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\"\",\n \".acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length,\",\n \"dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0]\",\n \"field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data,\",\n \"field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes\",\n \"constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,\",\n \"to shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate size\",\n \"Exception('solver option {} must be of type float. You have {}.'.format(field_, type(value_))) else:\",\n \"have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately\",\n \"in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\\",\n \"acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file)\",\n \"stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj\",\n \"external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg\",\n \"if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data =\",\n \"and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #\",\n \"if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,\",\n \"constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e >\",\n \"ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field =\",\n \"make one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non\",\n \"False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model,\",\n \"return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\\",\n \"stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options of the\",\n \"self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_\",\n \"constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension\",\n \"object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod\",\n \"get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe\",\n \"acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost\",\n \"integer corresponding to shooting node :param field_: string in ['x', 'u', 'z', 'pi',\",\n \"def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model =\",\n \"ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e:\",\n \"value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p,\",\n \"only value 0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if\",\n \"copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf\",\n \"regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0\",\n \"template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir =\",\n \"# external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension\",\n \"function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"= nsbx + nsbu + nsh + nsg + nsphi wrong_field = \\\"\\\"\",\n \"= acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should\",\n \"== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file,\",\n \"ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) !=\",\n \"consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim\",\n \"c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the QP) 'time_qp_xcond',\",\n \"raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns\",\n \"ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) !=\",\n \"True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name\",\n \"{} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data),\",\n \"W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')\",\n \"out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp\",\n \"is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding\",\n \"BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\",\n \"ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p,\",\n \"if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e\",\n \"in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam,\",\n \"min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data\",\n \"raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif\",\n \"not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object\",\n \"isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute)\",\n \"solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', #\",\n \"if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name)\",\n \"# external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h'\",\n \"= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e\",\n \"nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg:\",\n \"usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif\",\n \"cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e:\",\n \"ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif\",\n \"regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg =\",\n \"integrator contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', #\",\n \"# # 1. Redistributions of source code must retain the above copyright notice,\",\n \"Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and\",\n \"disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright\",\n \"out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type\",\n \"nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal\",\n \"dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,\",\n \"> 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1,\",\n \"stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj],\",\n \"nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e =\",\n \"cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W,\",\n \"in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in'\",\n \"if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if\",\n \"dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx + nsbu +\",\n \"dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct, v in\",\n \"OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO\",\n \"field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note::\",\n \"NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\",\n \"constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if\",\n \"solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp',\",\n \"c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data,\",\n \"is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self,\",\n \"\\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =\",\n \"= self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0]\",\n \"make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model\",\n \"nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0]\",\n \".generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint\",\n \"function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file =\",\n \"{nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if\",\n \"in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost\",\n \"interact with the acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'):\",\n \"msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data =\",\n \"= nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] !=\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates\",\n \"'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file =\",\n \"- terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file\",\n \"= \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim\",\n \"else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e =\",\n \"cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file\",\n \"def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp')\",\n \"os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' #\",\n \"'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to\",\n \"nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e:\",\n \"in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out =\",\n \"= [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0])\",\n \"# import sys, os, json import numpy as np from ctypes import *\",\n \"ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1]\",\n \"> 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): #\",\n \"Redistributions of source code must retain the above copyright notice, # this list\",\n \"W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == []\",\n \"self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape =\",\n \"raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\",\n \"nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise\",\n \"'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp\",\n \"dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_,\",\n \"# Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ =\",\n \"cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0]\",\n \"IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY\",\n \"nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np if\",\n \"\\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib #\",\n \"acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\\",\n \"value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape\",\n \"casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh:\",\n \"not be used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_):\",\n \"not a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_\",\n \":param stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W',\",\n \"nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent\",\n \"dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0]\",\n \"nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e\",\n \"'{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e ==\",\n \"constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension\",\n \"= casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u):\",\n \"of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time\",\n \"NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF\",\n \"met: # # 1. Redistributions of source code must retain the above copyright\",\n \"ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension\",\n \"if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] !=\",\n \"lower inequality constraints \\\\n su: slack variables of soft upper inequality constraints \\\\n\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_,\",\n \"Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if\",\n \"def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r')\",\n \"cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0]\",\n \"Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0]\",\n \"soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam',\",\n \"self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self): stat =\",\n \"self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in()\",\n \"nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise\",\n \"or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else:\",\n \"field, out_data) return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP':\",\n \"nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e\",\n \"clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get\",\n \"constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else:\",\n \"if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e,\",\n \"outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados\",\n \"format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict\",\n \"dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts)\",\n \"np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx\",\n \"dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear\",\n \"raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0]\",\n \"= cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7:\",\n \"field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values are {}.\",\n \"size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns}\",\n \"= \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim\",\n \"(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for\",\n \"wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\"\",\n \"elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field !=\",\n \"terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise\",\n \"'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file,\",\n \"'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or\",\n \"type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0\",\n \"AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR\",\n \"if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can\",\n \"ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE #\",\n \"'sqp_iter', # number of SQP iterations 'statistics', # table with info about last\",\n \"< 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take\",\n \"'t', 'sl', 'su',] .. note:: regarding lam, t: \\\\n the inequalities are internally\",\n \"current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\"\",\n \"[ lbu lbx lg lh lphi ubu ubx ug uh uphi; \\\\n lsbu\",\n \"time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps\",\n \"!= tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension\",\n \"+ f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\\",\n \"= 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex\",\n \"elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e):\",\n \"import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from\",\n \"pi: multipliers for dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n t:\",\n \"list of conditions and the following disclaimer in the documentation # and/or other\",\n \"acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file,\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif\",\n \"self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return\",\n \"idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent\",\n \"Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue acados_attribute =\",\n \"!= dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i\",\n \"and use in source and binary forms, with or without # modification, are\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp\",\n \"lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension\",\n \"# generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) #\",\n \"node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',]\",\n \"# this list of conditions and the following disclaimer in the documentation #\",\n \"dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny:\",\n \"sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1])\",\n \"for field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0])\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"\\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the\",\n \"in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\\",\n \"in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\\",\n \"out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e\",\n \"have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes\",\n \"nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.')\",\n \"self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\"\",\n \"if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if\",\n \"constraints \\\\n su: slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields\",\n \"field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise\",\n \"# set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp,\",\n \"field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p,\",\n \"dimension', \\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\\",\n \"raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif\",\n \"constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the constraint module of\",\n \"object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting\",\n \"ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise\",\n \"Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if\",\n \"print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get\",\n \"'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function\",\n \"'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8')\",\n \"EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,\",\n \"copyright notice, # this list of conditions and the following disclaimer. # #\",\n \"import * from casadi import CasadiMeta, Function, SX from copy import deepcopy from\",\n \"constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x):\",\n \"template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file =\",\n \"value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\\",\n \"if cost.Vu.shape[1] != dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should\",\n \"tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension {}\",\n \"solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes =\",\n \"make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints\",\n \"= 0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0\",\n \"not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct,\",\n \"dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0]\",\n \"'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level',\",\n \"is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding\",\n \"Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent\",\n \"Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import\",\n \"sys, os, json import numpy as np from ctypes import * from casadi\",\n \".generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost\",\n \"regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e =\",\n \"ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file)\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_\",\n \"self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out\",\n \"def get_stats(self, field_): \\\"\\\"\\\" get the information of the last solver call: :param\",\n \"dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or\",\n \"Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension {} (you have\",\n \"self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE:\",\n \"# setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__)\",\n \"or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not\",\n \"lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu\",\n \"# Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip\",\n \"acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes\",\n \"if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,\",\n \"nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e\",\n \"con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':\",\n \"not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh\",\n \"0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file\",\n \"Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension:\",\n \"INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,\",\n \"dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear\",\n \"== 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching\",\n \"out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion\",\n \"terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh =\",\n \"# This file is part of acados. # # The 2-Clause BSD License\",\n \"should be column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu\",\n \"for integrator contribution of external function calls 'time_sim_la', # cpu time for integrator\",\n \"if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z):\",\n \"value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_ > 2:\",\n \"Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if\",\n \"= field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_ is 'p':\",\n \"= ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model)\",\n \"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED\",\n \"is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr\",\n \"raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0]\",\n \"1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension'\",\n \"Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,\",\n \"IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR\",\n \"np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL':\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear\",\n \"lam: multipliers for inequalities \\\\n t: slack variables corresponding to evaluation of all\",\n \"'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"<NAME> # # This file is part of acados. # # The 2-Clause\",\n \"OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.;\",\n \"PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\",\n \"['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims\",\n \"self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims\",\n \"idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,))\",\n \"get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims'])\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0]\",\n \"'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time previous call 'time_lin', #\",\n \"c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int))\",\n \"dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.')\",\n \"stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj\",\n \"\\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims\",\n \"!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh\",\n \"\\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options #\",\n \"c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data\",\n \"mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format(\",\n \"opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name,\",\n \"load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints,\",\n \"is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise\",\n \"out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype =\",\n \"= nsg ns = nsbx + nsbu + nsh + nsg + nsphi\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type !=\",\n \"\\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']:\",\n \"time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not\",\n \"is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps)\",\n \"POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p,\",\n \"field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]\",\n \"<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part\",\n \"over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal\",\n \"constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx\",\n \"field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an\",\n \"0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over\",\n \"return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the cost\",\n \"numpy as np from ctypes import * from casadi import CasadiMeta, Function, SX\",\n \"set options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks',\",\n \"if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_)\",\n \"!= ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field\",\n \"!= nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh =\",\n \"casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.')\",\n \"stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data\",\n \"json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims\",\n \"np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if\",\n \"acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False\",\n \"out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name)\",\n \"cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field\",\n \"else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or\",\n \"is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir,\",\n \"Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension:\",\n \"!= nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi\",\n \"dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"not isinstance(value_, str): raise Exception('solver option {} must be of type str. You\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part of acados.\",\n \"# nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz\",\n \"corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_:\",\n \"idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,))\",\n \"= np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.')\",\n \"file is part of acados. # # The 2-Clause BSD License # #\",\n \"is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0]\",\n \"POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config,\",\n \"dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and\",\n \"jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj])))\",\n \"The 2-Clause BSD License # # Redistribution and use in source and binary\",\n \"SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode\",\n \"(not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf =\",\n \"out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over\",\n \"above copyright notice, # this list of conditions and the following disclaimer in\",\n \"found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name)\",\n \"acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type\",\n \"value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver\",\n \"== 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up\",\n \"if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise\",\n \"time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with info\",\n \"out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name)\",\n \"sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout =\",\n \"dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts\",\n \"in source and binary forms, with or without # modification, are permitted provided\",\n \"# # 2. Redistributions in binary form must reproduce the above copyright notice,\",\n \"value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p,\",\n \"regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh\",\n \"c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p\",\n \"if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False)\",\n \"template_dir, json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e ==\",\n \"!= ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field\",\n \"'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ ==\",\n \"nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx\",\n \"'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type\",\n \"= c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int,\",\n \"out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir\",\n \"'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not in\",\n \"= 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex\",\n \"\\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj]))\",\n \"ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg, ug,\",\n \"description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict\",\n \"corresponding to shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam',\",\n \"raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif\",\n \"solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\'\",\n \"elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx\",\n \"json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0:\",\n \"!= ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng\",\n \"# Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object\",\n \"linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside\",\n \"\\\"\\\"\\\" # cast value_ to avoid conversion issues value_ = value_.astype(float) field =\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in\",\n \"out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m'\",\n \"ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.')\",\n \"COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\",\n \"ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e !=\",\n \"Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0]\",\n \"and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif\",\n \"is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding\",\n \"the inequalities are internally organized in the following order: \\\\n [ lbu lbx\",\n \"mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims,\",\n \"ng, regarding lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr):\",\n \"!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e =\",\n \"cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields =\",\n \"constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have\",\n \"Exception('solver option {} must be of type str. You have {}.'.format(field_, type(value_))) else:\",\n \"dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi\",\n \"'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file,\",\n \"= {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e =\",\n \"C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else:\",\n \"ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if\",\n \"field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data\",\n \"if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp\",\n \"== 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]),\",\n \"int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self,\",\n \"field_: string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast value_\",\n \"= nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] !=\",\n \"= get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json,\",\n \"c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data\",\n \"self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current input\",\n \"constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e\",\n \"ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__\",\n \"is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or\",\n \"{} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p =\",\n \"nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh\",\n \"value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p)\",\n \"elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e):\",\n \"Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype\",\n \"acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) #\",\n \"treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype =\",\n \"False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True,\",\n \"# <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,\",\n \"'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir =\",\n \"= acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) #\",\n \"idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent\",\n \"= self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in =\",\n \"# strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict =\",\n \"+ mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values are\",\n \"dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] !=\",\n \"render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function if\",\n \"constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu,\",\n \"folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file,\",\n \"acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir\",\n \"function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file =\",\n \"render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir\",\n \"stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\",\n \"default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()\",\n \"Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0:\",\n \"out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m =\",\n \"usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\\\n lam:\",\n \"regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise\",\n \"for integrator contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call',\",\n \"raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent\",\n \"\\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape))\",\n \"must reproduce the above copyright notice, # this list of conditions and the\",\n \"con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0]\",\n \"lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension\",\n \"nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if\",\n \"def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the constraint module\",\n \"nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] !=\",\n \"'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file,\",\n \"+ ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int\",\n \"raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0]\",\n \"1. Redistributions of source code must retain the above copyright notice, # this\",\n \"disclaimer in the documentation # and/or other materials provided with the distribution. #\",\n \"/ dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent\",\n \"the last solution of the solver: :param stage: integer corresponding to shooting node\",\n \"value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can '\",\n \"dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] !=\",\n \"yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e)\",\n \"Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json\",\n \"solver: :param stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref',\",\n \"dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] !=\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \"dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np =\",\n \"opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions:\",\n \"'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot',\",\n \"W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if\",\n \"for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj],\",\n \"mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values are {}.\",\n \"dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically\",\n \"return out def print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if\",\n \"cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu\",\n \"!= nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e,\",\n \"regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e =\",\n \"'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to\",\n \"must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_)\",\n \"get the last solution of the solver: :param stage: integer corresponding to shooting\",\n \"nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg\",\n \"nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,))\",\n \"elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must\",\n \"model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name =\",\n \"'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields =\",\n \"isinstance(value_, str): raise Exception('solver option {} must be of type str. You have\",\n \"generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column,\",\n \"# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #\",\n \"acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type\",\n \"generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and\",\n \"parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if\",\n \"the above copyright notice, # this list of conditions and the following disclaimer\",\n \"N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1]\",\n \"else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes))\",\n \"# terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and\",\n \"'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex\",\n \"stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0],\",\n \"['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_\",\n \"if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if\",\n \"lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or\",\n \"is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f:\",\n \"dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e\",\n \"if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi\",\n \"+ nsbu + nsh + nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0]\",\n \"(stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64)\",\n \"constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension\",\n \"c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field)\",\n \"out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh >\",\n \"POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields +\",\n \":param field_: string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast\",\n \"render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir =\",\n \"if not isinstance(value_, float): raise Exception('solver option {} must be of type float.\",\n \"= nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] !=\",\n \"ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\",\n \"in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue acados_attribute\",\n \"f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure\",\n \"dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options\",\n \"field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is\",\n \"uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if\",\n \"lg lh lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh\",\n \"cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n\",\n \"# dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile\",\n \"jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj],\",\n \"{nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization\",\n \"'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_ to avoid\",\n \"model.p and parameter_values.') ## cost # path if cost.cost_type == 'LINEAR_LS': ny =\",\n \"regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W,\",\n \"other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0\",\n \"ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0]\",\n \"raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng\",\n \"cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\\",\n \"constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields =\",\n \"ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e,\",\n \"= nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0]\",\n \"= self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out =\",\n \"if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\"\",\n \"cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if\",\n \"dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ =\",\n \"con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e =\",\n \"but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx =\",\n \"con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0\",\n \"= np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.')\",\n \"make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim =\",\n \"{nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes):\",\n \"valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields\",\n \"as np from ctypes import * from casadi import CasadiMeta, Function, SX from\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not\",\n \"= cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config,\",\n \"acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"option {} must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e =\",\n \"json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not\",\n \"= nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e\",\n \"from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from\",\n \"SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND\",\n \"raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and\",\n \"!= 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] !=\",\n \"+ nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx},\",\n \"+ nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu =\",\n \"if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function\",\n \"= False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type ==\",\n \"self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical\",\n \"SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,\",\n \"= 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape ==\",\n \"ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes',\",\n \"regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise\",\n \"idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent\",\n \"range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and\",\n \"if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format(\",\n \"# explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': #\",\n \"external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file\",\n \"ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field,\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\"\",\n \"!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e:\",\n \"terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e\",\n \"self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\\",\n \"def options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver: Parameters: :param field_:\",\n \"elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0]\",\n \"'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage,\",\n \"in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be of\",\n \"open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp,\",\n \"!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e\",\n \"out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir =\",\n \"acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name,\",\n \"all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0]\",\n \"f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =\",\n \"size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e}\",\n \"OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\",\n \"'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver\",\n \"# terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e:\",\n \"idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu + nsh +\",\n \"np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self,\",\n \"LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\",\n \"t: \\\\n the inequalities are internally organized in the following order: \\\\n [\",\n \"field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config,\",\n \"(field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config,\",\n \"# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES\",\n \"['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast\",\n \"acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f)\",\n \"stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj],\",\n \"self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config()\",\n \"0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e\",\n \"in string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be of\",\n \"<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, #\",\n \"EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys,\",\n \"valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter',\",\n \"not isinstance(value_, int): raise Exception('solver option {} must be of type int. You\",\n \"usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics equality constraints\",\n \"'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time previous call 'time_lin',\",\n \"dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise\",\n \"with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_):\",\n \"nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent\",\n \"os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib\",\n \"'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj],\",\n \"if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\"\",\n \"is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e\",\n \"stage_, field_, value_): \\\"\\\"\\\" set numerical data in the constraint module of the\",\n \"POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if\",\n \"for field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data =\",\n \"idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] !=\",\n \"that the following conditions are met: # # 1. Redistributions of source code\",\n \"regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise\",\n \"function calls 'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp',\",\n \"ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0\",\n \"= 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file,\",\n \"self.nlp_solver, field, out_data) return out # Note: this function should not be used\",\n \"if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should\",\n \"!= ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e =\",\n \"raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e\",\n \"and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value\",\n \"import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template,\",\n \"\\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected\",\n \"if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding\",\n \"acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file,\",\n \"= field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is\",\n \"dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0]\",\n \"THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #\",\n \"dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'):\",\n \"OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS\",\n \"THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE\",\n \"= 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension\",\n \"'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file,\",\n \"c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims:\",\n \"field_, value_): \\\"\\\"\\\" set options of the solver: Parameters: :param field_: string, e.g.\",\n \"if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr) if\",\n \"= c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \"the solver: :param stage_: integer corresponding to shooting node :param field_: string, e.g.\",\n \"out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e\",\n \"# implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif\",\n \"cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"SQP iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n', ]\",\n \"template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise\",\n \"opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either\",\n \"= cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension\",\n \"elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format(\",\n \"= 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints\",\n \"else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise\",\n \"casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims\",\n \"'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total\",\n \"casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty')\",\n \"def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment json_path\",\n \"dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e):\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny:\",\n \"acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts = dict(generate_hess=1)\",\n \"elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0:\",\n \"is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding\",\n \"\\\\n the inequalities are internally organized in the following order: \\\\n [ lbu\",\n \"This file is part of acados. # # The 2-Clause BSD License #\",\n \"= 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_\",\n \"c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if\",\n \"!= nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e =\",\n \"else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0]\",\n \"False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF':\",\n \"dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) #\",\n \"nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,))\",\n \"self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p,\",\n \"= nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] !=\",\n \"Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e)\",\n \"else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else: dims.np\",\n \"1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension',\",\n \"== constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] !=\",\n \"for field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape))\",\n \"dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list,\",\n \"lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config,\",\n \"constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"= '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e ==\",\n \"'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam', 't'] # cast value_\",\n \"out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"<NAME>, <NAME> # # This file is part of acados. # # The\",\n \"other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY\",\n \"\\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0]\",\n \"nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field\",\n \"elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu):\",\n \"THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED\",\n \"last solution of the solver: :param stage: integer corresponding to shooting node :param\",\n \"{dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx + nsbu + nsg\",\n \"lbx lg lh lphi ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg\",\n \"iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not\",\n \"render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh\",\n \"raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise\",\n \"# load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims,\",\n \"# # The 2-Clause BSD License # # Redistribution and use in source\",\n \"terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir =\",\n \"field_): \\\"\\\"\\\" get the last solution of the solver: :param stage: integer corresponding\",\n \"dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0]\",\n \"field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get():\",\n \"USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny !=\",\n \"dimension' \\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims),\",\n \"self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see\",\n \"type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if\",\n \"self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields:\",\n \"# Redistribution and use in source and binary forms, with or without #\",\n \"import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import\",\n \"dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] !=\",\n \"ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] !=\",\n \"acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type ==\",\n \"stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu']\",\n \"BSD License # # Redistribution and use in source and binary forms, with\",\n \"\\\\n su: slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields =\",\n \"2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise\",\n \":param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] ..\",\n \"note:: pi: multipliers for dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n\",\n \"nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints\",\n \"def get(self, stage_, field_): \\\"\\\"\\\" get the last solution of the solver: :param\",\n \"print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n')\",\n \"argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields +\",\n \".generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp\",\n \"def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model\",\n \"json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and\",\n \"if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name)\",\n \"soft lower inequality constraints \\\\n su: slack variables of soft upper inequality constraints\",\n \"value_): \\\"\\\"\\\" set numerical data in the constraint module of the solver: Parameters:\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,\",\n \"cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent\",\n \"field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} = nsbx\",\n \"!= nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e =\",\n \"not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file =\",\n \"c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {}\",\n \"solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return\",\n \"# Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0]\",\n \"np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e\",\n \"= ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if not isinstance(value_, int):\",\n \"constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,\",\n \"generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint\",\n \"string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast\",\n \"> 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0 for\",\n \"dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0]\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\\",\n \"== 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e\",\n \"= ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 ==\",\n \"elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi):\",\n \"> 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices\",\n \"distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\",\n \"NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY\",\n \"function should not be used anymore, better use cost_set, constraints_set def set(self, stage_,\",\n \"> 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type ==\",\n \"return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the constraint\",\n \"nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e =\",\n \"= cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif\",\n \"mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not\",\n \"dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] !=\",\n \"model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype\",\n \"type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str):\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in\",\n \"or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else:\",\n \"'{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS':\",\n \"yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS':\",\n \"constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,\",\n \"float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in string_fields:\",\n \"USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH\",\n \"!= ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field\",\n \"time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time\",\n \"== 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir\",\n \"FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE\",\n \"form must reproduce the above copyright notice, # this list of conditions and\",\n \"= casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] !=\",\n \"def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r')\",\n \"dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0]\",\n \"\\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field\",\n \"structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json = json.load(f)\",\n \"{} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension\",\n \"Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file\",\n \"avoid conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage\",\n \"constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension\",\n \"= acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx\",\n \"Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny,\",\n \"= '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e\",\n \"funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes\",\n \"= cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes\",\n \"= nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] !=\",\n \"\\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make\",\n \"self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p\",\n \"is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding\",\n \"'{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/'\",\n \"in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear\",\n \"= 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension {}\",\n \"# modification, are permitted provided that the following conditions are met: # #\",\n \"regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise\",\n \"# get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts\",\n \"dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] !=\",\n \"getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model):\",\n \"= c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not\",\n \"elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0]\",\n \"terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\"\",\n \"OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF\",\n \"= os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return\",\n \"nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim =\",\n \"Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if\",\n \"if not isinstance(value_, int): raise Exception('solver option {} must be of type int.\",\n \"= nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e},\",\n \"= nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e =\",\n \"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES\",\n \"in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\"\",\n \"= constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent\",\n \"self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out =\",\n \"import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import\",\n \"is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e):\",\n \"= {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path +\",\n \"= np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization:\",\n \"be column vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu =\",\n \"= nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] !=\",\n \"ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise\",\n \"lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or\",\n \"retain the above copyright notice, # this list of conditions and the following\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),\",\n \"np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data)\",\n \"PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS\",\n \"are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"get_stats(self, field_): \\\"\\\"\\\" get the information of the last solver call: :param field_:\",\n \"= field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p,\",\n \"(tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' =\",\n \"constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e,\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set\",\n \"acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost =\",\n \"json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder\",\n \"acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status =\",\n \"> 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h'\",\n \"'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost - terminal\",\n \"os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name)\",\n \"= \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for\",\n \"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #\",\n \"= '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function\",\n \"\\\\n t: slack variables corresponding to evaluation of all inequalities (at the solution)\",\n \"lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension\",\n \"DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\",\n \"return def get_stats(self, field_): \\\"\\\"\\\" get the information of the last solver call:\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver,\",\n \"= np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.')\",\n \"'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file =\",\n \"for dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n t: slack variables\",\n \"['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {}\",\n \"field = field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats():\",\n \"on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0:\",\n \"path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise\",\n \"C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model\",\n \"are met: # # 1. Redistributions of source code must retain the above\",\n \"isinstance(value_, float): raise Exception('solver option {} must be of type float. You have\",\n \"# <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is part of\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return\",\n \"= 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes\",\n \"= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or\",\n \"f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}')\",\n \"else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1)\",\n \"'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time\",\n \"constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,\",\n \"dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx + nsbu\",\n \"{nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e\",\n \"= cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with\",\n \"\\\"\\\"\\\" get the last solution of the solver: :param stage: integer corresponding to\",\n \"field_, value_): \\\"\\\"\\\" set numerical data in the cost module of the solver:\",\n \"np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if\",\n \"regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e =\",\n \"nsbx + nsbu + nsh + nsg + nsphi wrong_field = \\\"\\\" if\",\n \"LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\",\n \"explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit\",\n \"opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x\",\n \"OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import\",\n \"cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise\",\n \"+ nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field =\",\n \"regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.')\",\n \"if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not\",\n \"\\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim =\",\n \"dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0:\",\n \"constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu\",\n \"opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts)\",\n \"= 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file\",\n \"= cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if\",\n \"raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e,\",\n \"self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes =\",\n \"cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e =\",\n \"cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values\",\n \"c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in\",\n \"cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\\",\n \"Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e =\",\n \"IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING\",\n \"!= 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] !=\",\n \"dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny #\",\n \"iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n', ] field\",\n \"in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\\",\n \"with or without # modification, are permitted provided that the following conditions are\",\n \"0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e:\",\n \"field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\\",\n \"QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations\",\n \"cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have\",\n \"of external function calls 'time_sim_la', # cpu time for integrator contribution of linear\",\n \"raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost # path if\",\n \"\\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,\",\n \"idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,))\",\n \"self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"'statistics', # table with info about last iteration 'stat_m', 'stat_n', ] field =\",\n \"# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS\",\n \"nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\'\",\n \"yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr)\",\n \"= np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.')\",\n \"else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu =\",\n \"'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external\",\n \"time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu time\",\n \"set numerical data in the constraint module of the solver: Parameters: :param stage_:\",\n \"acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set\",\n \"parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int\",\n \"= np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.')\",\n \"is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding\",\n \"size \\\"\\\"\\\" # cast value_ to avoid conversion issues value_ = value_.astype(float) field\",\n \"constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg,\",\n \"0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type\",\n \"'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_ field\",\n \"!= ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e\",\n \"ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e ==\",\n \"'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m,\",\n \"constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh\",\n \"np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for\",\n \"Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module\",\n \"import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import\",\n \"\\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if\",\n \"be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif\",\n \"## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' +\",\n \"OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\",\n \"of soft lower inequality constraints \\\\n su: slack variables of soft upper inequality\",\n \"self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return\",\n \"elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh =\",\n \"nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.')\",\n \"with formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes if\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes =\",\n \"c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_):\",\n \"), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data =\",\n \"value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"ns = nsbx + nsbu + nsh + nsg + nsphi wrong_field =\",\n \"= constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension\",\n \"nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu:\",\n \"OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY\",\n \"Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data\",\n \"cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if\",\n \"nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps)\",\n \"provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT\",\n \"= [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note:\",\n \"nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e #\",\n \"DAMAGE.; # import sys, os, json import numpy as np from ctypes import\",\n \"if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding\",\n \"render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost\",\n \"over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints\",\n \"casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] !=\",\n \"and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_:\",\n \"= constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent\",\n \"self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config\",\n \"options of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length'\",\n \"+ cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"str): raise Exception('solver option {} must be of type str. You have {}.'.format(field_,\",\n \"can ' 'take only value 0 for SQP-type solvers') field = field_ field\",\n \"setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type\",\n \"value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver: Parameters:\",\n \"PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS\",\n \"Note: this function should not be used anymore, better use cost_set, constraints_set def\",\n \"dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.')\",\n \"= 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if\",\n \"dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny !=\",\n \"of source code must retain the above copyright notice, # this list of\",\n \"# <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # #\",\n \"dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] !=\",\n \"initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else:\",\n \"self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set\",\n \"TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\",\n \"a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields +\",\n \"from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty,\",\n \"in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field,\",\n \"+ nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"== 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny:\",\n \"idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent\",\n \"field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields):\",\n \"stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0])\",\n \"c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"== 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent\",\n \"if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS':\",\n \"'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file,\",\n \"if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is\",\n \"etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not\",\n \"dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e +\",\n \"+ nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim\",\n \"= constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent\",\n \"for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]),\",\n \"ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json\",\n \"'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\\\n the\",\n \"cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif\",\n \"== 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False)\",\n \"value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\\",\n \"notice, # this list of conditions and the following disclaimer. # # 2.\",\n \"INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\",\n \"!= nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx =\",\n \"raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu\",\n \"ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0]\",\n \"'su',] .. note:: regarding lam, t: \\\\n the inequalities are internally organized in\",\n \"True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader and environment\",\n \"'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file =\",\n \"of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_\",\n \"nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise\",\n \"f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp\",\n \"copyright notice, # this list of conditions and the following disclaimer in the\",\n \"HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\",\n \"of the solver: Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param\",\n \"nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu\",\n \"WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN\",\n \"elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.')\",\n \"'{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e ==\",\n \"string_fields: if not isinstance(value_, str): raise Exception('solver option {} must be of type\",\n \"dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu):\",\n \"sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path\",\n \"call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call',\",\n \"= '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if acados_ocp.cost.cost_type_e\",\n \"dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0]\",\n \"dimension ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}],\",\n \"[c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this\",\n \"must be of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_)\",\n \"generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx\",\n \"multipliers for inequalities \\\\n t: slack variables corresponding to evaluation of all inequalities\",\n \"constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e\",\n \"acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be\",\n \"= c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)),\",\n \"tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension {}\",\n \"evaluation of all inequalities (at the solution) \\\\n sl: slack variables of soft\",\n \"nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi:\",\n \"{dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e\",\n \"'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK':\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m'\",\n \"cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot\",\n \"np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh\",\n \"# skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct,\",\n \"external function calls 'time_sim_la', # cpu time for integrator contribution of linear algebra\",\n \"function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}],\",\n \"nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim =\",\n \"data in the cost module of the solver: :param stage_: integer corresponding to\",\n \"raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with dimension {} (you\",\n \"argument \\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type solvers') field =\",\n \"# -*- coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, #\",\n \"import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp):\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int\",\n \"nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e +\",\n \"module of the solver: :param stage_: integer corresponding to shooting node :param field_:\",\n \"POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config,\",\n \"ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints #\",\n \"nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu:\",\n \"previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu time for\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif\",\n \"acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options\",\n \"slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u',\",\n \"self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype\",\n \"= acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status\",\n \"yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e\",\n \"os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file =\",\n \"contribution of external function calls 'time_sim_la', # cpu time for integrator contribution of\",\n \"raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif\",\n \"raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx\",\n \"if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,\",\n \"acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy\",\n \"wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\"\",\n \"== 'BGP' and acados_ocp.dims.nphi > 0: # constraints on outer function template_dir =\",\n \"string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes)\",\n \"non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__\",\n \"generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics\",\n \"slack variables corresponding to evaluation of all inequalities (at the solution) \\\\n sl:\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if\",\n \"or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values\",\n \"= \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim\",\n \"tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) =\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)\",\n \"== other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0,\",\n \"c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should not\",\n \"attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy\",\n \"get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts =\",\n \"W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise\",\n \"name = acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(),\",\n \"0 else: dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr\",\n \"!= ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e,\",\n \"nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name)\",\n \"cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0]\",\n \"constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT\",\n \"idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,))\",\n \"0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape:\",\n \"generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate\",\n \"elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts =\",\n \":param value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion issues\",\n \"c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function\",\n \"the information of the last solver call: :param field_: string in ['statistics', 'time_tot',\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding\",\n \"+ nsh_e + nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e:\",\n \"setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict\",\n \"ubu ubx ug uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx\",\n \"else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e\",\n \"lsg.') if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi\",\n \"Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if\",\n \"idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] !=\",\n \"if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,))\",\n \"if not isinstance(value_, str): raise Exception('solver option {} must be of type str.\",\n \"{}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64)\",\n \"else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise\",\n \"dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] !=\",\n \"FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\",\n \"NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\",\n \"json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"!= nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh, uh,\",\n \"nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh:\",\n \"0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot\",\n \"= \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif\",\n \"# Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make\",\n \"field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes =\",\n \"generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL':\",\n \"dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding\",\n \"out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"'lam', 't'] # cast value_ to avoid conversion issues value_ = value_.astype(float) field\",\n \"cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p]\",\n \"elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu =\",\n \"is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding\",\n \"c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created:\",\n \"module of the solver: Parameters: :param stage_: integer corresponding to shooting node :param\",\n \"Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if\",\n \"not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps\",\n \"acados_struct, v in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict):\",\n \"sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file,\",\n \"= casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0]\",\n \"Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0]\",\n \"np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out =\",\n \"c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del\",\n \"of all inequalities (at the solution) \\\\n sl: slack variables of soft lower\",\n \"regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise\",\n \"nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi =\",\n \"are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =\",\n \"= 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file\",\n \"= acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with\",\n \"Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0):\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out\",\n \"= cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if\",\n \"= cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.'\",\n \"ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny\",\n \"field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int,\",\n \"COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,\",\n \"= {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e):\",\n \"field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in\",\n \"if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code generate_c_code_explicit_ode(model) elif\",\n \"ns = {ns} = nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1]\",\n \"# constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi\",\n \"== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file,\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set():\",\n \"dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape =\",\n \"import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import\",\n \"get the information of the last solver call: :param field_: string in ['statistics',\",\n \"the cost module of the solver: :param stage_: integer corresponding to shooting node\",\n \"'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif\",\n \"stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in\",\n \"in the constraint module of the solver: Parameters: :param stage_: integer corresponding to\",\n \"= nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e:\",\n \"constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension\",\n \"dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if\",\n \"Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\\",\n \"in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints\",\n \"forms, with or without # modification, are permitted provided that the following conditions\",\n \"# number of SQP iterations 'statistics', # table with info about last iteration\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if field_\",\n \"of SQP iterations 'statistics', # table with info about last iteration 'stat_m', 'stat_n',\",\n \"int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields =\",\n \"= acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else:\",\n \"dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx\",\n \"Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu = 0 else:\",\n \"info about last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8')\",\n \"THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.; #\",\n \"value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0\",\n \"constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu,\",\n \"constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx,\",\n \"nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise\",\n \"= 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file,\",\n \"= 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost\",\n \"stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information of the last\",\n \"'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\\\n\",\n \"[c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else:\",\n \"int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type\",\n \"- opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide\",\n \"!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e\",\n \"if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\\",\n \"vector!') # nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) #\",\n \"ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\",\n \"Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if\",\n \"nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h'\",\n \"f'Detected ns = {ns} = nsbx + nsbu + nsg + nsh +\",\n \"field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" #\",\n \"\\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim =\",\n \"int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def\",\n \"converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of\",\n \"= 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex\",\n \"set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions\",\n \"dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0]\",\n \"= dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False,\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return\",\n \"of the solver: Parameters: :param stage_: integer corresponding to shooting node :param field_:\",\n \"acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit\",\n \"be used anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields\",\n \"the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP\",\n \"inequalities are internally organized in the following order: \\\\n [ lbu lbx lg\",\n \"\\\"\\\"\\\" set numerical data in the constraint module of the solver: Parameters: :param\",\n \"= c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims,\",\n \"= dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown\",\n \"= ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if\",\n \"ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp,\",\n \"Exception('inconsistent dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"TODO: maybe make one funciton with formatting for acados_struct, v in ocp_layout.items(): #\",\n \"= constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx =\",\n \"= 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created\",\n \"!= nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns\",\n \"# # This file is part of acados. # # The 2-Clause BSD\",\n \"= casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z)\",\n \"dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and\",\n \"dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator\",\n \"W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')\",\n \"!= nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e =\",\n \"= np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p]\",\n \"= make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C code\",\n \"= self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config =\",\n \"!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e =\",\n \"integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT': opts = dict(generate_hess=1) else: opts = dict(generate_hess=0)\",\n \"dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0\",\n \"ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def\",\n \"template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information of the\",\n \"import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import\",\n \"!= ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}],\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not\",\n \"IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.\",\n \"0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu,\",\n \"POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape)\",\n \"OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\",\n \"e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields\",\n \"be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if\",\n \"field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p,\",\n \"return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout()\",\n \"isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes\",\n \"return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': #\",\n \"' 'take only value 0 for SQP-type solvers') field = field_ field =\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)),\",\n \"dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0]\",\n \"ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny: raise Exception('inconsistent dimension ny: regarding\",\n \"== []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if\",\n \"== 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file,\",\n \"OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE\",\n \"acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting\",\n \"= 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file,\",\n \"np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if\",\n \"= time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or\",\n \"out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims,\",\n \"dimension np, regarding model.p and parameter_values.') ## cost # path if cost.cost_type ==\",\n \"if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,\",\n \"dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e\",\n \"Possible values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out\",\n \"print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format(\",\n \"is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or\",\n \"if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name)\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters separately if\",\n \"shooting node :param field_: string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\"\",\n \"information of the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin',\",\n \"ny, regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n')\",\n \"if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\\",\n \"class to interact with the acados ocp solver C object \\\"\\\"\\\" def __init__(self,\",\n \"(not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform\",\n \"# this list of conditions and the following disclaimer. # # 2. Redistributions\",\n \"= dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0]\",\n \"\\\\n [ lbu lbx lg lh lphi ubu ubx ug uh uphi; \\\\n\",\n \"set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints = acados_ocp.constraints model\",\n \"'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\\\n the inequalities are\",\n \"out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values\",\n \"value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion issues value_\",\n \"'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi', 'lam',\",\n \"!= tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension\",\n \"0 for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in\",\n \"is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding\",\n \"dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] =\",\n \"acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as f: ocp_nlp_json =\",\n \"elif constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg):\",\n \"# total cpu time previous call 'time_lin', # cpu time for linearization 'time_sim',\",\n \"render_template(in_file, out_file, template_dir, json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL':\",\n \"ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny\",\n \"out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name)\",\n \"regarding idxbx, ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0]\",\n \"json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp\",\n \"stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in\",\n \"Parameters: :param stage_: integer corresponding to shooting node :param field_: string, e.g. 'lbx'\",\n \"constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx,\",\n \"= 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension\",\n \"elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx):\",\n \"import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils import\",\n \"type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_ in\",\n \"string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding\",\n \"or constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else:\",\n \"raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N):\",\n \"if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\\",\n \"acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json\",\n \"+ mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int\",\n \"to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code')\",\n \"as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp\",\n \"is an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields))\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"= ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct, v\",\n \"ush usphi] .. note:: pi: multipliers for dynamics equality constraints \\\\n lam: multipliers\",\n \"raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng\",\n \"'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over\",\n \"in the documentation # and/or other materials provided with the distribution. # #\",\n \"data in the constraint module of the solver: Parameters: :param stage_: integer corresponding\",\n \"constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e,\",\n \"class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip\",\n \"model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x)\",\n \"usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif\",\n \"AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT\",\n \"be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0\",\n \"usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif\",\n \"__init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent\",\n \"are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)),\",\n \"dimension nsphi, regarding idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg):\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding\",\n \"else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else:\",\n \"indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with\",\n \"> 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create()\",\n \"\\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data =\",\n \"\\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"# terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file\",\n \"casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu\",\n \"external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates\",\n \"idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent\",\n \"'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion\",\n \"dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng if not\",\n \"= nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] !=\",\n \"opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please\",\n \"get(self, stage_, field_): \\\"\\\"\\\" get the last solution of the solver: :param stage:\",\n \"Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary\",\n \"of conditions and the following disclaimer. # # 2. Redistributions in binary form\",\n \"0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"regarding lam, t: \\\\n the inequalities are internally organized in the following order:\",\n \"constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e\",\n \"raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0]\",\n \"= {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e = ns_e # discretization\",\n \"'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and\",\n \"'c_generated_code/' ## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir,\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ '\",\n \"!= dims.nu and ny != 0: raise Exception('inconsistent dimension: Vu should have nu\",\n \"constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"= getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp,\",\n \"= \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim\",\n \"input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton\",\n \"raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2 for\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage,\",\n \"dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e):\",\n \"render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type\",\n \"= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes =\",\n \"one funciton with formatting for acados_struct, v in ocp_layout.items(): # skip non dict\",\n \"(value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for\",\n \"'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to\",\n \"self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_):\",\n \"if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ##\",\n \"with the acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created\",\n \"c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p,\",\n \"with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS\",\n \"out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter\",\n \"POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"and binary forms, with or without # modification, are permitted provided that the\",\n \"nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_\",\n \"== 'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file\",\n \"!= dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have nx\",\n \"c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"= '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear\",\n \"!= sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with\",\n \"value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def\",\n \"if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else:\",\n \"!= ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field\",\n \"return def options_set(self, field_, value_): \\\"\\\"\\\" set options of the solver: Parameters: :param\",\n \"out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path)\",\n \"code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code\",\n \"acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file,\",\n \"stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype\",\n \"if is_empty(constraints.usbu): constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,\",\n \".. note:: regarding lam, t: \\\\n the inequalities are internally organized in the\",\n \"if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N *\",\n \"POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n =\",\n \"field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64))\",\n \"0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over\",\n \"stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\" get the information of\",\n \"THE # POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy\",\n \"casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode\",\n \"= 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension\",\n \"raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must\",\n \"field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int, float\",\n \"!= nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e =\",\n \"regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi =\",\n \"have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not\",\n \"self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]),\",\n \"# nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column\",\n \"= c_int(value_) elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option\",\n \"c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p,\",\n \"json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout =\",\n \"Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in constraints_fields:\",\n \"== [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape\",\n \"= field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a\",\n \"model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE\",\n \"OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\",\n \"materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE\",\n \"Parameters: :param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type\",\n \"json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if\",\n \"Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type solvers') field\",\n \"if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e,\",\n \"ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0]\",\n \"'u', 'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_ =\",\n \"c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif\",\n \"cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad', # cpu\",\n \"'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', # total cpu time previous\",\n \"elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e =\",\n \"POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"and the following disclaimer. # # 2. Redistributions in binary form must reproduce\",\n \"acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e,\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should\",\n \"!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu\",\n \"if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng:\",\n \"'{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints on convex over nonlinear function if\",\n \"Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name\",\n \"dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx =\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data)\",\n \"acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise\",\n \"cpu time for integrator contribution of external function calls 'time_sim_la', # cpu time\",\n \"LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND\",\n \"dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not\",\n \"dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0\",\n \"cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\\",\n \"= cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\")\",\n \"<NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>,\",\n \"ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict)\",\n \"be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_,\",\n \":param stage: integer corresponding to shooting node :param field_: string in ['x', 'u',\",\n \"acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx ==\",\n \"nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}') dims.ns_e\",\n \"else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0]\",\n \"out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact\",\n \"Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if\",\n \"== 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file\",\n \"self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver()\",\n \"type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields\",\n \"acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file,\",\n \"issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_)\",\n \"= json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp()\",\n \"2-Clause BSD License # # Redistribution and use in source and binary forms,\",\n \"<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME>\",\n \"0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] != dims.nu\",\n \"automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to\",\n \"self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve\",\n \"+ '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype =\",\n \"are internally organized in the following order: \\\\n [ lbu lbx lg lh\",\n \"part of acados. # # The 2-Clause BSD License # # Redistribution and\",\n \"values are {}. Exiting.'.format(fields, fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out =\",\n \"format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost\",\n \"dims.nsg = nsg ns = nsbx + nsbu + nsh + nsg +\",\n \"constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,\",\n \"cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h'\",\n \"== 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching\",\n \"constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name)\",\n \"dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape\",\n \"cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns:\",\n \"= c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver option\",\n \"nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal\",\n \"not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible\",\n \"opts = dict(generate_hess=1) else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh\",\n \"lam, t: \\\\n the inequalities are internally organized in the following order: \\\\n\",\n \"field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields\",\n \"Redistributions in binary form must reproduce the above copyright notice, # this list\",\n \"'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file =\",\n \"nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx):\",\n \"stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return def get_stats(self, field_): \\\"\\\"\\\"\",\n \"if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e,\",\n \"type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ ==\",\n \"terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file =\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def\",\n \"np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config,\",\n \"nx if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!')\",\n \"0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise\",\n \"'h_e_constraint.in.h' out_file = '{}_h_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear cost function if\",\n \"type int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in\",\n \"discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise\",\n \"better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref']\",\n \"qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the QP)\",\n \"if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size\",\n \"\\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3:\",\n \"(you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)\",\n \"OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\",\n \"!= nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e #\",\n \"# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\"\",\n \"= cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding\",\n \"is_empty(constraints.lsh): constraints.lsh = np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding\",\n \"raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_,\",\n \"You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if\",\n \"if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh,\",\n \"out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear cost function if\",\n \"nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: # constraints on\",\n \"discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N\",\n \"out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE\",\n \"c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int))\",\n \"= cast((value_data), c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument\",\n \"np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p,\",\n \"nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif constraints.lsh_e.shape[0] != nsh_e: raise\",\n \"if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) #\",\n \"contribution of linear algebra 'time_qp', # cpu time qp solution 'time_qp_solver_call', # cpu\",\n \"regarding W_e, yref_e.') dims.ny_e = ny_e ## constraints # initial if (constraints.lbx_0 ==\",\n \"= ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps =\",\n \"0: raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny:\",\n \"ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so' # get self.shared_lib =\",\n \"regarding idxsbu, usbu.') dims.nsbu = nsbu nsh = constraints.idxsh.shape[0] if is_empty(constraints.lsh): constraints.lsh =\",\n \"lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr\",\n \"type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields: if not isinstance(value_, float):\",\n \"'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', #\",\n \"terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file =\",\n \"with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): #\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out\",\n \"elif field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must\",\n \"not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model,\",\n \"is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif\",\n \"idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,))\",\n \"dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0]\",\n \"(field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n\",\n \"casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e:\",\n \"'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h' out_file =\",\n \"elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\")\",\n \"self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib\",\n \"not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a\",\n \"c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes\",\n \"self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype\",\n \"-- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model --\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field,\",\n \"\\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set\",\n \"json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file =\",\n \"lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi:\",\n \"are permitted provided that the following conditions are met: # # 1. Redistributions\",\n \"!= 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif casadi_length(model.cost_y_expr) != ny:\",\n \"= constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension\",\n \"c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =\",\n \"dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz =\",\n \"casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) #\",\n \"\\\"\\\"\\\" solve the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status\",\n \"{ns} = nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With\",\n \"acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_e_constraint.in.h'\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def print_statistics(self):\",\n \"ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx\",\n \"constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size\",\n \"acados_ocp.cost constraints = acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if\",\n \"value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) #\",\n \"self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p]\",\n \"else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise\",\n \"= 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file\",\n \"dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx:\",\n \"set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external functions ocp_generate_external_functions(acados_ocp, model)\",\n \"!= ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx,\",\n \"int. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_int(value_) elif field_ in double_fields:\",\n \"nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15:\",\n \"nonlinear cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h'\",\n \"dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu if\",\n \"casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] !=\",\n \"= value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes =\",\n \"# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS\",\n \"value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field \\\"{}\\\" with\",\n \"ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,\",\n \"cpu time for integrator 'time_sim_ad', # cpu time for integrator contribution of external\",\n \"del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python #\",\n \"'pi', 'lam', 't'] # cast value_ to avoid conversion issues value_ = value_.astype(float)\",\n \"# skip non dict attributes if not isinstance(v, dict): continue acados_attribute = getattr(acados_ocp,\",\n \"following disclaimer in the documentation # and/or other materials provided with the distribution.\",\n \"<NAME>, <NAME>, <NAME>, <NAME> # # This file is part of acados. #\",\n \"raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e =\",\n \"acados_ocp.constraints model = acados_ocp.model opts = acados_ocp.solver_options # nx if is_column(model.x): dims.nx =\",\n \"{}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase': if value_ <\",\n \"constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi,\",\n \"= c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the\",\n \"# Note: this function should not be used anymore, better use cost_set, constraints_set\",\n \"= sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout\",\n \"last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la',\",\n \"field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid\",\n \"dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] !=\",\n \"cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\\",\n \"lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for\",\n \"invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes =\",\n \"value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the\",\n \"print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI': print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]):\",\n \"if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n') elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':\",\n \"nh: raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh\",\n \"{nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0]\",\n \"ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description\",\n \"constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes if\",\n \"constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name)\",\n \"'time_qp_solver_call', # cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg',\",\n \"wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\"\",\n \"the solver: Parameters: :param stage_: integer corresponding to shooting node :param field_: string,\",\n \"= \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\ self.nlp_opts, field, byref(value_ctypes)) return def\",\n \"string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to\",\n \"nsphi = {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e =\",\n \"ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip\",\n \"{} is not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields\",\n \"json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render\",\n \"'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion\",\n \"'time_sim_ad', # cpu time for integrator contribution of external function calls 'time_sim_la', #\",\n \"constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi >\",\n \"above copyright notice, # this list of conditions and the following disclaimer. #\",\n \"from copy import deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from\",\n \"value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you\",\n \"cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\":\",\n \"cost function if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file\",\n \"nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns\",\n \"!= nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx =\",\n \"cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return\",\n \"opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf !=\",\n \"\\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim =\",\n \"f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0]\",\n \"nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e},\",\n \"else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else:\",\n \"class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp solver C object\",\n \"is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding\",\n \"generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model,\",\n \"without # modification, are permitted provided that the following conditions are met: #\",\n \"= '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type == 'EXTERNAL':\",\n \"!= \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ +\",\n \"nsg_e + nsphi_e wrong_field = \\\"\\\" if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\"\",\n \"constraints # initial if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 =\",\n \"dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh = 0\",\n \"elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e):\",\n \"= min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data =\",\n \"the solver: :param stage: integer corresponding to shooting node :param field_: string in\",\n \"# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def\",\n \"['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field = field_\",\n \"\\\"\\\"\\\" class to interact with the acados ocp solver C object \\\"\\\"\\\" def\",\n \"generate external functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render\",\n \"0 or acados_ocp.dims.nh_e > 0: generate_c_code_constraint(model, model.name, True, opts) # dummy matrices if\",\n \"node :param field_: string, e.g. 'lbx' :param value_: of appropriate size \\\"\\\"\\\" #\",\n \"dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non dict attributes\",\n \"= field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"print_statistics(self): stat = self.get_stats(\\\"statistics\\\") if self.acados_ocp.solver_options.nlp_solver_type == 'SQP': print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for\",\n \"np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu\",\n \"nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,))\",\n \"self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_):\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file = 'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"= CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype\",\n \"self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"call 'time_lin', # cpu time for linearization 'time_sim', # cpu time for integrator\",\n \"different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0\",\n \"= 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file\",\n \"provided that the following conditions are met: # # 1. Redistributions of source\",\n \"ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with\",\n \"= 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file\",\n \"dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field},\",\n \"acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model =\",\n \"template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set():\",\n \"cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e:\",\n \"'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED\",\n \"from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model\",\n \"dims.nphi = casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr\",\n \"have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p)\",\n \"dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension\",\n \"field, out_data) return out # Note: this function should not be used anymore,\",\n \"Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr)\",\n \"acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True)\",\n \"BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF\",\n \"field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in\",\n \"dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise\",\n \"W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] != dims.nx and ny_e != 0: raise Exception('inconsistent dimension:\",\n \"= 'make_sfun.in.m' out_file = 'make_sfun.m' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file\",\n \"ocp_check_against_layout, np_array_to_list, make_model_consistent,\\\\ set_up_imported_gnsf_model def make_ocp_dims_consistent(acados_ocp): dims = acados_ocp.dims cost = acados_ocp.cost constraints\",\n \"variables of soft lower inequality constraints \\\\n su: slack variables of soft upper\",\n \"this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0,\",\n \"nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng = constraints.lg.shape[0] if\",\n \"nsbx, regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx:\",\n \"permitted provided that the following conditions are met: # # 1. Redistributions of\",\n \"nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0] != nsg:\",\n \"must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8')\",\n \"constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e, regarding_e lg_e,\",\n \"ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) #\",\n \"POSSIBILITY OF SUCH DAMAGE.; # import sys, os, json import numpy as np\",\n \"print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj],\",\n \"dims.nsh_e = nsh_e nsg_e = constraints.idxsg_e.shape[0] if is_empty(constraints.lsg_e): constraints.lsg_e = np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0]\",\n \"= (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ '\",\n \"= json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp = AcadosOcp() # load class\",\n \"constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx\",\n \"json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir\",\n \"ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif cost.zl.shape[0] != ns: wrong_field =\",\n \"'/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): #\",\n \"in ocp_layout.items(): # skip non dict attributes if not isinstance(v, dict): continue #\",\n \"dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1: raise Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps =\",\n \"field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p,\",\n \"elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush):\",\n \"{opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__]\",\n \"numerical data in the constraint module of the solver: Parameters: :param stage_: integer\",\n \"CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\",\n \"is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') # nu\",\n \".generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from .utils\",\n \":param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg',\",\n \"+ \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny\",\n \"np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\")\",\n \"raise Exception('inconsistent dimension: Vu should have nu columns.') if cost.yref.shape[0] != ny: raise\",\n \"'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e ==\",\n \"= c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype =\",\n \"lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers\",\n \"raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout():\",\n \"regarding idxsbx, lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise\",\n \"0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else:\",\n \"EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #\",\n \"e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate size \\\"\\\"\\\" # cast value_\",\n \"# cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table\",\n \"BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL\",\n \"conversion issues value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage =\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields\",\n \"= np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.')\",\n \"\\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field,\",\n \"dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi\",\n \"field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be\",\n \"= np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.')\",\n \"c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int\",\n \"= np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field,\",\n \"Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx\",\n \"not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e\",\n \"shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i]\",\n \"= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.')\",\n \"cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e:\",\n \"ny_e: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]')\",\n \"'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI'\",\n \"or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng,\",\n \"ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e:\",\n \"ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny:\",\n \"# Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>,\",\n \"Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi = np.zeros((nsphi,)) elif constraints.usphi.shape[0]\",\n \"if cost.Zl_e.shape[0] != ns_e: wrong_field = \\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] !=\",\n \"= constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension\",\n \"= True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims =\",\n \"nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu\",\n \"cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ##\",\n \"= cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim = cost.Zu.shape[0] elif\",\n \"= AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes\",\n \"variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z',\",\n \"field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0)\",\n \"nsg + nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu},\",\n \"template_dir, json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!! #\",\n \"import sys, os, json import numpy as np from ctypes import * from\",\n \"= field_ field = field.encode('utf-8') if (field_ not in out_fields + mem_fields): raise\",\n \"time for integrator contribution of linear algebra 'time_qp', # cpu time qp solution\",\n \"\\\\'rti_phase\\\\' can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if\",\n \"'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate external\",\n \"def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy\",\n \"'acados_sim_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file =\",\n \"+ nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e},\",\n \"nsg ns = nsbx + nsbu + nsh + nsg + nsphi wrong_field\",\n \"out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost if acados_ocp.cost.cost_type ==\",\n \"anymore, better use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref',\",\n \"dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e\",\n \"regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with info about\",\n \"= cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_ not in constraints_fields +\",\n \"stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field\",\n \"['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_ not in out_fields\",\n \"out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims,\",\n \"field_ in double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be\",\n \"description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__)\",\n \"of the solver: :param stage_: integer corresponding to shooting node :param field_: string,\",\n \"value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims):\",\n \"reproduce the above copyright notice, # this list of conditions and the following\",\n \"solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name +\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_\",\n \"make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time\",\n \"idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent\",\n \"nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\\",\n \"+ f'Detected ns = {ns} = nsbx + nsbu + nsg + nsh\",\n \"raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif\",\n \"Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension\",\n \"ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT\",\n \"!= ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent\",\n \"nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack\",\n \"SUCH DAMAGE.; # import sys, os, json import numpy as np from ctypes\",\n \"constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e,\",\n \"HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,\",\n \"else: if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {}\",\n \"should have nu columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W,\",\n \"!= ny: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') if cost.yref.shape[0] != ny:\",\n \"ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_,\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in\",\n \"'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks']\",\n \"['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields:\",\n \"acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp)\",\n \"model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type ==\",\n \"np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if\",\n \"time for integrator contribution of external function calls 'time_sim_la', # cpu time for\",\n \"f' = tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path\",\n \"raise Exception('solver option {} must be of type int. You have {}.'.format(field_, type(value_)))\",\n \"nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg =\",\n \"shooting_nodes for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf\",\n \"W, cost_y_expr.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' +\",\n \"nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise\",\n \"out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype =\",\n \"ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.'\",\n \"dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] !=\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching\",\n \"or without # modification, are permitted provided that the following conditions are met:\",\n \"acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] #\",\n \"if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3:\",\n \"cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x',\",\n \"if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np:\",\n \"with info about last iteration 'stat_m', 'stat_n', ] field = field_ field =\",\n \"generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp\",\n \"outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file,\",\n \"if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\\",\n \"nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e:\",\n \"from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf from\",\n \"status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get the last\",\n \"json_path) ## folder model template_dir = 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name)\",\n \"'t'] mem_fields = ['sl', 'su'] field = field_ field = field.encode('utf-8') if (field_\",\n \"W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0] != ny: raise Exception('inconsistent\",\n \"numerical data in the cost module of the solver: :param stage_: integer corresponding\",\n \"int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_\",\n \"nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.') dims.nsbu = nsbu nsh =\",\n \"elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx =\",\n \"and ny_e != 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e)\",\n \"= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data =\",\n \"= ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e\",\n \"print('\\\\niter\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>3: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if\",\n \"Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr):\",\n \"np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if\",\n \"casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty')\",\n \"# path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny\",\n \"self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if\",\n \"input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get\",\n \"= constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0]\",\n \"constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh,\",\n \"constraints.lbx.shape[0] != nbx: raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.') else: dims.nbx\",\n \"acados_ocp.cost.cost_type == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if\",\n \"ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() with open(json_file, 'r') as\",\n \"c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)]\",\n \"json_file) # render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make\",\n \"['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields =\",\n \"constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions\",\n \"the documentation # and/or other materials provided with the distribution. # # THIS\",\n \"PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\",\n \"render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir,\",\n \"is not a valid argument.\\\\ \\\\nPossible values are {}. Exiting.\\\".format(field, \\\\ constraints_fields +\",\n \"* from casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode\",\n \"this list of conditions and the following disclaimer in the documentation # and/or\",\n \"np.zeros((nsphi,)) elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi\",\n \"= c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype =\",\n \"template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0:\",\n \"nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi:\",\n \"raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e\",\n \"i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not\",\n \"elif constraints.usphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.') dims.nsphi =\",\n \"values are {}. Exiting.\\\".format(field, \\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes\",\n \"this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0): raise\",\n \"\\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field = \\\"Zu\\\" dim =\",\n \"model): model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate\",\n \"= 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file,\",\n \"self.shared_lib.acados_get_nlp_out.restype = c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype\",\n \"\\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self, stage_, field_, value_): \\\"\\\"\\\"\",\n \"self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64)\",\n \"= field_ field = field.encode('utf-8') if (field_ not in fields): raise Exception('AcadosOcpSolver.get_stats(): {}\",\n \"W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if\",\n \"object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict) # strip symbolics ocp_nlp_dict['model'] =\",\n \"nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e =\",\n \"= np.zeros((nsbx,)) elif constraints.lsbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.')\",\n \"elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e =\",\n \"'{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal constraints on convex over nonlinear function\",\n \"with dimension {} (you have {})'.format( \\\\ field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,\",\n \"constraint module of the solver: Parameters: :param stage_: integer corresponding to shooting node\",\n \"' for field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_, tuple(dims),\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.' + \\\\ f'\\\\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]') if cost.Vx_e.shape[1] !=\",\n \"!= nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.') if is_empty(constraints.usbu): constraints.usbu =\",\n \"laod class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): #\",\n \"table with info about last iteration 'stat_m', 'stat_n', ] field = field_ field\",\n \"casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif\",\n \"ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent\",\n \"is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding\",\n \"ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object acados_ocp =\",\n \"about last iteration 'stat_m', 'stat_n', ] field = field_ field = field.encode('utf-8') if\",\n \"'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost.in.h' out_file = '{}_external_cost.h'.format(name) render_template(in_file, out_file, template_dir,\",\n \"else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr) else: nh =\",\n \"matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny,\",\n \"cost.cost_type == 'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise\",\n \"'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\" fields = ['time_tot', #\",\n \"field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config,\",\n \"HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT\",\n \"raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty') else: dims.nr =\",\n \"ctypes import * from casadi import CasadiMeta, Function, SX from copy import deepcopy\",\n \"!= dims.nx and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx\",\n \"template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file\",\n \"= self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg =\",\n \"if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.') dims.ny_e = ny_e\",\n \"MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT\",\n \"for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns = {ns} =\",\n \"current_module = sys.modules[__name__] acados_path = os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f:\",\n \"solve the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def\",\n \"generate_c_code_nls_cost(model, model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':\",\n \"int(stat[0][jj]), stat[1][jj], stat[2][jj], \\\\ stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj],\",\n \"out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e\",\n \"nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent\",\n \"os.path.dirname(current_module.__file__) with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout\",\n \"out # Note: this function should not be used anymore, better use cost_set,\",\n \"len(value_shape) == 1: value_shape = (value_shape[0], 0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set():\",\n \"= self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get the last solution\",\n \"lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension\",\n \"wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size\",\n \"in the following order: \\\\n [ lbu lbx lg lh lphi ubu ubx\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This\",\n \"0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh,\",\n \"= {ns} = nsbx + nsbu + nsg + nsh + nsphi.\\\\n\\\\t'\\\\ +\",\n \"stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj]))) if stat.shape[0]>7: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj])) print('\\\\n')\",\n \"ng: raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng =\",\n \"idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,))\",\n \"of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length']\",\n \"f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0:\",\n \"not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!') if not is_column(constraints.lbx_0):\",\n \"source and binary forms, with or without # modification, are permitted provided that\",\n \"regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu = constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu =\",\n \"acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if\",\n \"f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh},\",\n \"uh_e, con_h_expr_e.') else: dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e =\",\n \"'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # constraints\",\n \"Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:\",\n \"dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz =\",\n \"(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE\",\n \"option {} must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes\",\n \"dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field},\",\n \"as f: ocp_nlp_json = json.load(f) ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims']) # Instantiate AcadosOcp object\",\n \"= {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi =\",\n \"shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of appropriate\",\n \"field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config,\",\n \"field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\"\",\n \"raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only value 0 for SQP-type solvers')\",\n \"model -- generate C code generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields:\",\n \"min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data,\",\n \"self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p,\",\n \"an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields)) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes\",\n \"nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent\",\n \"dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,)) elif constraints.lsg.shape[0]\",\n \"raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension {} (you\",\n \"= c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype =\",\n \"print('\\\\niter\\\\tres_stat\\\\tres_eq\\\\t\\\\tres_ineq\\\\tres_comp\\\\tqp_stat\\\\tqp_iter') if stat.shape[0]>7: print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:d}\\\\t{:d}'.format( \\\\ int(stat[0][jj]), stat[1][jj], stat[2][jj],\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\\",\n \"con_phi_expr is nonempty') else: dims.nr = casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if\",\n \"= np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if\",\n \"= np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.')\",\n \"AcadosOcp object acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict #\",\n \"NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): #\",\n \"{nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns\",\n \"1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0:\",\n \"value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes\",\n \"nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise\",\n \"regarding W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if\",\n \"with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p\",\n \"if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi,\",\n \"stage = c_int(stage_) # treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes =\",\n \"acados_attribute = getattr(acados_ocp, acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def\",\n \"acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def\",\n \"(field_ not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\\",\n \"def cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the cost module\",\n \"str. You have {}.'.format(field_, type(value_))) else: value_ctypes = value_.encode('utf-8') if field_ == 'rti_phase':\",\n \"string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter'] \\\"\\\"\\\"\",\n \"json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path)) template_dir\",\n \"cpu time previous call 'time_lin', # cpu time for linearization 'time_sim', # cpu\",\n \"!= ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n')\",\n \"= cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with\",\n \"functions ocp_generate_external_functions(acados_ocp, model) # dump to json ocp_formulation_json_dump(acados_ocp, json_file) # render templates ocp_render_templates(acados_ocp,\",\n \"acados_ocp.dims.nphi_e > 0: # terminal constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file\",\n \"of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi',\",\n \"= np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.')\",\n \"c_int dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64)\",\n \"field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e\",\n \"os, json import numpy as np from ctypes import * from casadi import\",\n \"this list of conditions and the following disclaimer. # # 2. Redistributions in\",\n \"value_ to avoid conversion issues value_ = value_.astype(float) field = field_ field =\",\n \"= dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one funciton with formatting for acados_struct, v\",\n \"raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}],\",\n \"elif cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] !=\",\n \"== 'rti_phase': if value_ < 0 or value_ > 2: raise Exception('AcadosOcpSolver.solve(): argument\",\n \"0 else: dims.nz = casadi_length(model.z) # np if is_empty(model.p): dims.np = 0 else:\",\n \"== 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file,\",\n \"template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp solver\",\n \"# terminal ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e wrong_field =\",\n \"<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,\",\n \"] field = field_ field = field.encode('utf-8') if (field_ not in fields): raise\",\n \"W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e =\",\n \"json_path) # terminal constraints on convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP'\",\n \"dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) if (field_ in out_fields): self.shared_lib.ocp_nlp_out_get.argtypes = \\\\ [c_void_p,\",\n \"\\\\ ' for field \\\"{}\\\" with dimension {} (you have {})'.format( \\\\ field_,\",\n \"self.nlp_dims, self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension\",\n \"= constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different shapes!')\",\n \"elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e):\",\n \"ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model = make_model_consistent(model) if\",\n \"= np.zeros((nsh,)) elif constraints.lsh.shape[0] != nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.')\",\n \"raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\\\\ \\\\n Possible values are {}.\",\n \"with open(acados_path + '/acados_layout.json', 'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def\",\n \"cost.Vz.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' +\",\n \"raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e\",\n \"shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl',\",\n \"## Render templates in_file = 'main.in.c' out_file = 'main_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"int_fields: if not isinstance(value_, int): raise Exception('solver option {} must be of type\",\n \"for SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields:\",\n \"'stat_n', ] field = field_ field = field.encode('utf-8') if (field_ not in fields):\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in\",\n \"<NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file\",\n \"columns.') if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\\",\n \"Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz != 0 and cost.Vz.shape[0]\",\n \"<NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # # This file is\",\n \"nsbx: raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.') dims.nsbx = nsbx nsbu =\",\n \"time_steps elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes\",\n \"self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) ==\",\n \"dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_,\",\n \"dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"template_dir = 'c_generated_code/{}_cost/'.format(name) in_file = 'cost_y_fun.in.h' out_file = '{}_cost_y_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension nsbx,\",\n \"value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set():\",\n \"ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif\",\n \"and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape =\",\n \"the following disclaimer in the documentation # and/or other materials provided with the\",\n \"# np if is_empty(model.p): dims.np = 0 else: dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0]\",\n \"'{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class AcadosOcpSolver: \\\"\\\"\\\" class to interact with the\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \\\\ self.nlp_dims, self.nlp_solver, stage_, field, out_data) return out def\",\n \"# # Redistribution and use in source and binary forms, with or without\",\n \"and ny_e != 0: raise Exception('inconsistent dimension: Vx_e should have nx columns.') if\",\n \"or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else:\",\n \"json_file): name = acados_ocp.model.name # setting up loader and environment json_path = '{cwd}/{json_file}'.format(\",\n \"internally organized in the following order: \\\\n [ lbu lbx lg lh lphi\",\n \".. note:: pi: multipliers for dynamics equality constraints \\\\n lam: multipliers for inequalities\",\n \"'pi', 'lam', 't', 'sl', 'su',] .. note:: regarding lam, t: \\\\n the inequalities\",\n \"or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' +\",\n \"acados_ocp = AcadosOcp() # load class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class\",\n \"{} is not a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields))\",\n \"!= 0: raise Exception('inconsistent dimension: Vx should have nx columns.') if cost.Vu.shape[1] !=\",\n \"for inequalities \\\\n t: slack variables corresponding to evaluation of all inequalities (at\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage,\",\n \"= cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field = \\\"zl_e\\\" dim = cost.zl_e.shape[0] elif\",\n \"'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double))\",\n \"+ model.name + '.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True\",\n \"model = acados_ocp.model # make dims consistent make_ocp_dims_consistent(acados_ocp) if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp)\",\n \"constraints.lsh_e.shape[0] != nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e\",\n \"'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'make_sfun.in.m' out_file =\",\n \"value_): \\\"\\\"\\\" set options of the solver: Parameters: :param field_: string, e.g. 'print_level',\",\n \"constraints.usbu = np.zeros((nsbu,)) elif constraints.usbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu, regarding idxsbu,\",\n \"model.name, True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx =\",\n \"json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_'\",\n \"self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data,\",\n \"# NOTE: DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name):\",\n \"dims.np = casadi_length(model.p) if acados_ocp.parameter_values.shape[0] != dims.np: raise Exception('inconsistent dimension np, regarding model.p\",\n \"= cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif\",\n \"in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)\",\n \"in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps = time_steps elif (not is_empty(opts.time_steps))\",\n \"= ['globalization'] if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option\",\n \"OF SUCH DAMAGE.; # import sys, os, json import numpy as np from\",\n \"return status def get(self, stage_, field_): \\\"\\\"\\\" get the last solution of the\",\n \"for integrator 'time_sim_ad', # cpu time for integrator contribution of external function calls\",\n \"= ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0]\",\n \"# laod class attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items():\",\n \"!= dims.np: raise Exception('inconsistent dimension np, regarding model.p and parameter_values.') ## cost #\",\n \"and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name)\",\n \"# treat parameters separately if field_ is 'p': self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)] self.shared_lib.acados_update_params.restype\",\n \"if is_column(model.x): dims.nx = casadi_length(model.x) else: raise Exception('model.x should be column vector!') #\",\n \"c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype = c_void_p self.nlp_config = self.shared_lib.acados_get_nlp_config() self.shared_lib.acados_get_nlp_out.restype = c_void_p\",\n \"\\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi] .. note::\",\n \"usbx_e.') dims.nsbx_e = nsbx_e nsh_e = constraints.idxsh_e.shape[0] if is_empty(constraints.lsh_e): constraints.lsh_e = np.zeros((nsh_e,)) elif\",\n \"stage_: integer corresponding to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess'\",\n \"convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0: #\",\n \"dimension: regarding W_e, yref_e.') dims.ny_e = ny_e elif cost.cost_type_e == 'NONLINEAR_LS': ny_e =\",\n \"\\\"Zl_e\\\" dim = cost.Zl_e.shape[0] elif cost.Zu_e.shape[0] != ns_e: wrong_field = \\\"Zu_e\\\" dim =\",\n \"0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ >\",\n \"def __del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily\",\n \"nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.') if is_empty(constraints.usg_e): constraints.usg_e = np.zeros((nsg_e,))\",\n \"= self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver =\",\n \"\\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg)\",\n \"nu if is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if\",\n \"constraints \\\\n lam: multipliers for inequalities \\\\n t: slack variables corresponding to evaluation\",\n \"number of SQP iterations 'statistics', # table with info about last iteration 'stat_m',\",\n \"convex over nonlinear function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: #\",\n \"constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e,\",\n \"value_data_p) elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"from .generate_c_code_external_cost import generate_c_code_external_cost from .acados_ocp import AcadosOcp from .acados_model import acados_model_strip_casadi_symbolics from\",\n \"+ nsh + nsphi.\\\\n\\\\t'\\\\ + f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg\",\n \"constraints.lsg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.') if is_empty(constraints.usg): constraints.usg\",\n \"self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out #\",\n \"constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape\",\n \"environment json_path = '{cwd}/{json_file}'.format( cwd=os.getcwd(), json_file=json_file) if not os.path.exists(json_path): raise Exception('{} not found!'.format(json_path))\",\n \"integrator contribution of external function calls 'time_sim_la', # cpu time for integrator contribution\",\n \"[]): dims.nbx_0 = 0 else: this_shape = constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not\",\n \"idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e = constraints.lg_e.shape[0] if constraints.ug_e.shape[0] !=\",\n \"Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and ny != 0: raise Exception('inconsistent\",\n \"opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if\",\n \"json_path) # nonlinear cost function if acados_ocp.cost.cost_type == 'NONLINEAR_LS': template_dir = 'c_generated_code/{}_cost/'.format(name) in_file\",\n \"0) if value_shape != tuple(dims): raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \\\\ ' for field\",\n \"raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e = nsg_e nsphi_e = constraints.idxsphi_e.shape[0]\",\n \"'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'Makefile.in' out_file =\",\n \"set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu',\",\n \"OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\",\n \"cpu time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting\",\n \"should have nx columns.') if cost.Vu.shape[1] != dims.nu and ny != 0: raise\",\n \"\\\\ constraints_fields + cost_fields + out_fields + ['p'])) self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"list of conditions and the following disclaimer. # # 2. Redistributions in binary\",\n \"dynamics equality constraints \\\\n lam: multipliers for inequalities \\\\n t: slack variables corresponding\",\n \"function if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0: # terminal constraints on\",\n \"nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\"\",\n \"else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz = 0 else: dims.nz\",\n \"tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\\",\n \"not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if\",\n \"attributes dict, dims, constraints, etc for acados_struct, v in ocp_layout.items(): # skip non\",\n \"= field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"a valid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(fields, fields)) if field_ in\",\n \"cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics', # table with\",\n \"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" #\",\n \"constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx = constraints.idxbx.shape[0]\",\n \"self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p] self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int dims =\",\n \"c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes\",\n \"c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_,\",\n \"if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e: raise Exception('inconsistent dimension nh_e, regarding\",\n \"cost.zu.shape[0] if wrong_field != \\\"\\\": raise Exception(f'Inconsistent size for field {wrong_field}, with dimension\",\n \"'SQP_RTI' and value_ > 0: raise Exception('AcadosOcpSolver.solve(): argument \\\\'rti_phase\\\\' can ' 'take only\",\n \"have different shapes!') if not is_column(constraints.lbx_0): raise Exception('lbx_0, ubx_0 must be column vectors!')\",\n \"raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.') if is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif\",\n \"is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not\",\n \"dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field) out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64) out_data\",\n \"casadi_length(model.con_r_expr) # terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] !=\",\n \"np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu)) if not acados_ocp.cost.cost_type_e == 'LINEAR_LS': acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx)) if acados_ocp.cost.cost_type\",\n \"have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding W_e, yref_e.')\",\n \"C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else:\",\n \"dim = cost.zl_e.shape[0] elif cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0]\",\n \"skip non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp,\",\n \"= 'c_generated_code/{}_model/'.format(name) in_file = 'model.in.h' out_file = '{}_model.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"{wrong_field}, with dimension {dim}, \\\\n\\\\t'\\\\ + f'Detected ns_e = {ns_e} = nsbx_e +\",\n \"= np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i] opts.time_steps =\",\n \"raise Exception('model.x should be column vector!') # nu if is_empty(model.u): dims.nu = 0\",\n \"dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape\",\n \"nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e\",\n \"== 'NONLINEAR_LS': ny_e = cost.W_e.shape[0] if is_empty(model.cost_y_expr_e) and ny_e != 0: raise Exception('inconsistent\",\n \"double_fields: if not isinstance(value_, float): raise Exception('solver option {} must be of type\",\n \"'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent dimension ny_e: regarding\",\n \"su: slack variables of soft upper inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x',\",\n \"# cpu time inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', #\",\n \"# 2. Redistributions in binary form must reproduce the above copyright notice, #\",\n \"uh uphi; \\\\n lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]\",\n \"value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for field \\\"{}\\\" '.format(field_) msg\",\n \"raise Exception('solver option {} must be of type float. You have {}.'.format(field_, type(value_)))\",\n \"elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field !=\",\n \"W, Vx, Vu, Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1]\",\n \"raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif\",\n \"= nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu:\",\n \"ng_e, regarding_e lg_e, ug_e, C_e.') else: dims.ng_e = ng_e if not is_empty(model.con_h_expr_e): nh_e\",\n \"np.zeros((nsg_e,)) elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e\",\n \"'NONLINEAR_LS': generate_c_code_nls_cost(model, model.name, True) elif acados_ocp.cost.cost_type_e == 'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file):\",\n \"'.so' # get self.shared_lib = CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p\",\n \"if is_empty(constraints.usg): constraints.usg = np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg,\",\n \"dims.nx and ny != 0: raise Exception('inconsistent dimension: Vx should have nx columns.')\",\n \"0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0:\",\n \"idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise Exception('inconsistent\",\n \"nh if is_empty(model.con_phi_expr): dims.nphi = 0 dims.nr = 0 else: dims.nphi = casadi_length(model.con_phi_expr)\",\n \"out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path)\",\n \"0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0\",\n \"value_shape != tuple(dims): raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with\",\n \"lsh lsphi usbu usbx usg ush usphi] .. note:: pi: multipliers for dynamics\",\n \"True, opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny,\",\n \"dims.nh_e = nh_e if is_empty(model.con_phi_expr_e): dims.nphi_e = 0 dims.nr_e = 0 else: dims.nphi_e\",\n \"SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\",\n \"Exception('inconsistent dimension N, regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i]\",\n \"ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input\",\n \"!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot\",\n \"nsh + nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field\",\n \"class dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc\",\n \"raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny =\",\n \"Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else: dims.nbu = nbu ng =\",\n \"CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY\",\n \"qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter',\",\n \"> 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e >\",\n \"A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER\",\n \"== 'statistics': sqp_iter = self.get_stats(\\\"sqp_iter\\\") stat_m = self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size =\",\n \"all inequalities (at the solution) \\\\n sl: slack variables of soft lower inequality\",\n \"OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF\",\n \"# path nbx = constraints.idxbx.shape[0] if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx:\",\n \"if not is_empty(model.con_h_expr_e): nh_e = casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] !=\",\n \"= casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is\",\n \"# and/or other materials provided with the distribution. # # THIS SOFTWARE IS\",\n \"# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\",\n \"sl: slack variables of soft lower inequality constraints \\\\n su: slack variables of\",\n \"= opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] != dims.N+1:\",\n \"nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e\",\n \"{ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e =\",\n \"IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\",\n \"c_void_p) if field_ in constraints_fields: self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p,\",\n \"= 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file,\",\n \"is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) # Slack dimensions nsbx = constraints.idxsbx.shape[0] if\",\n \"-- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF':\",\n \"in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\\",\n \"DLL cannot be easily unloaded!!! # see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python # while isLoaded(self.shared_lib_name): # dlclose(handle)\",\n \"IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY\",\n \"\\\\n lam: multipliers for inequalities \\\\n t: slack variables corresponding to evaluation of\",\n \"is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf / dims.N * np.ones((dims.N,))\",\n \"print('\\\\tqp_res_stat\\\\tqp_res_eq\\\\tqp_res_ineq\\\\tqp_res_comp') for jj in range(stat.shape[1]): print('{:d}\\\\t{:d}\\\\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\\",\n \"self.nlp_in, stage, field, value_data_p) return def options_set(self, field_, value_): \\\"\\\"\\\" set options of\",\n \"c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p\",\n \"not in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field,\",\n \"c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data =\",\n \"AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL\",\n \"in_file = 'cost_y_e_fun.in.h' out_file = '{}_cost_y_e_fun.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # external cost\",\n \"cost.zu_e.shape[0] != ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\":\",\n \"def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'): self.solver_created = False model = acados_ocp.model # make dims\",\n \"ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0] elif cost.Zu.shape[0] != ns: wrong_field =\",\n \"json_path) in_file = 'acados_solver_sfun.in.c' out_file = 'acados_solver_sfunction_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out\",\n \"constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.') dims.nsphi_e = nsphi_e\",\n \"{} must be of type int. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"acados_ocp_nlp structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict\",\n \"+ nsh + nsg + nsphi wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns:\",\n \"usphi_e.') dims.nsphi_e = nsphi_e # terminal ns_e = nsbx_e + nsh_e + nsg_e\",\n \":param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length' :param value_: of type int,\",\n \"Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.') else: dims.nbx_e = nbx_e ng_e =\",\n \"Exception('inconsistent dimension ng, regarding lg, ug, C, D.') else: dims.ng = ng if\",\n \"lsbx.') if is_empty(constraints.usbx): constraints.usbx = np.zeros((nsbx,)) elif constraints.usbx.shape[0] != nsbx: raise Exception('inconsistent dimension\",\n \"conditions and the following disclaimer in the documentation # and/or other materials provided\",\n \"= 0 else: dims.nphi_e = casadi_length(model.con_phi_expr_e) if is_empty(model.con_r_expr_e): raise Exception('convex over nonlinear constraints:\",\n \"nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise\",\n \"np.zeros((nsg,)) elif constraints.usg.shape[0] != nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg\",\n \"!= ns_e: wrong_field = \\\"Zu_e\\\" dim = cost.Zu_e.shape[0] elif cost.zl_e.shape[0] != ns_e: wrong_field\",\n \"fields)) if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data =\",\n \"'BGH' and acados_ocp.dims.nh > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file =\",\n \"self.nlp_in, stage, field, value_data_p) return def constraints_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical\",\n \"\\\"\\\"\\\" fields = ['time_tot', # total cpu time previous call 'time_lin', # cpu\",\n \"= np.zeros((nsg_e,)) elif constraints.lsg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.')\",\n \"= casadi_length(model.con_phi_expr) if is_empty(model.con_r_expr): raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is\",\n \"over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty') else: dims.nr_e = casadi_length(model.con_r_expr_e) #\",\n \"on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_e_constraint.in.h' out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file,\",\n \"= 'c_generated_code/{}_cost/'.format(name) in_file = 'external_cost_e.in.h' out_file = '{}_external_cost_e.h'.format(name) render_template(in_file, out_file, template_dir, json_path) class\",\n \"generate_c_code_explicit_ode(model) elif acados_ocp.solver_options.integrator_type == 'IRK': # implicit model -- generate C code opts\",\n \".acados_model import acados_model_strip_casadi_symbolics from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\\\\ format_class_dict, ocp_check_against_layout,\",\n \"the solution) \\\\n sl: slack variables of soft lower inequality constraints \\\\n su:\",\n \"value_ = value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes\",\n \"'NONLINEAR_LS': ny = cost.W.shape[0] if is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension\",\n \"not isinstance(value_, float): raise Exception('solver option {} must be of type float. You\",\n \"np.sum(opts.time_steps) if (tf - opts.tf) / tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\\",\n \"constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \\\\ or constraints.D.shape[0] !=\",\n \"c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) return def\",\n \"/ tf > 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps)\",\n \"> 1e-15: raise Exception(f'Inconsistent discretization: {opts.tf}'\\\\ f' = tf != sum(opts.time_steps) = {tf}.')\",\n \"model = make_model_consistent(model) if acados_ocp.solver_options.integrator_type == 'ERK': # explicit model -- generate C\",\n \"and ny != 0: raise Exception('inconsistent dimension: Vu should have nu columns.') if\",\n \"self.get_stats(\\\"stat_m\\\") stat_n = self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1,\",\n \"= 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file = '{}_phi_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) #\",\n \"'c_generated_code/{}_constraints/'.format(name) in_file = 'h_constraint.in.h' out_file = '{}_h_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # terminal\",\n \"None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as f: json.dump(ocp_nlp_dict, f,\",\n \"if cost.yref.shape[0] != ny: raise Exception('inconsistent dimension: regarding W, yref.' + \\\\ f'\\\\nGot\",\n \"!= ns: wrong_field = \\\"zu\\\" dim = cost.zu.shape[0] if wrong_field != \\\"\\\": raise\",\n \"is_empty(model.u): dims.nu = 0 else: dims.nu = casadi_length(model.u) # nz if is_empty(model.z): dims.nz\",\n \"value_data, value_.shape[0]) else: if field_ not in constraints_fields + cost_fields + out_fields: raise\",\n \"dimension: Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension:\",\n \"of the last solver call: :param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim',\",\n \"__del__(self): if self.solver_created: self.shared_lib.acados_free() del self.shared_lib # NOTE: DLL cannot be easily unloaded!!!\",\n \"dict acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for\",\n \"# constraints on outer function template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file = 'phi_constraint.in.h' out_file =\",\n \"!= ng \\\\ or constraints.D.shape[0] != ng: raise Exception('inconsistent dimension ng, regarding lg,\",\n \"= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape\",\n \"acados_struct) acados_attribute.__dict__ = ocp_nlp_dict[acados_struct] setattr(acados_ocp, acados_struct, acados_attribute) return acados_ocp def ocp_generate_external_functions(acados_ocp, model): model\",\n \"solver: Parameters: :param stage_: integer corresponding to shooting node :param field_: string, e.g.\",\n \"'r') as f: ocp_nlp_layout = json.load(f) return ocp_nlp_layout def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'): # Load\",\n \"Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns = nsbx +\",\n \"in out_fields + mem_fields): raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible\",\n \"dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__) ocp_nlp_dict = format_class_dict(ocp_nlp_dict)\",\n \"as f: json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True) def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'): # Load acados_ocp_nlp structure\",\n \"field_, value_): \\\"\\\"\\\" set numerical data in the constraint module of the solver:\",\n \"OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON\",\n \"nsh: raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,))\",\n \"dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape = (value_shape[0], 0) if\",\n \"= ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields = ['sl', 'su'] field =\",\n \"nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent\",\n \"constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise Exception('inconsistent dimension nbx_e, regarding idxbx_e,\",\n \"in constraints_fields + cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid\",\n \"self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data) return out # Note: this function should not be\",\n \"acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e\",\n \"out_data) return out # Note: this function should not be used anymore, better\",\n \"appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion issues value_ = value_.astype(float)\",\n \"constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh: raise Exception('inconsistent dimension nh, regarding lh,\",\n \"self.nlp_opts, field, value_ctypes) else: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_void_p] self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \\\\\",\n \"regarding lg, ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh\",\n \"of type float. You have {}.'.format(field_, type(value_))) else: value_ctypes = c_double(value_) elif field_\",\n \"raise Exception('lbx_0, ubx_0 must be column vectors!') dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 ==\",\n \"LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,\",\n \"stage = c_int(stage_) self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)] self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype\",\n \"implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type\",\n \"the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\",\n \"= constraints.lbx_0.shape other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0\",\n \"if acados_ocp.solver_options.integrator_type == 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0]\",\n \"model.name, False) elif acados_ocp.cost.cost_type == 'EXTERNAL': generate_c_code_external_cost(model, False) if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS': generate_c_code_nls_cost(model,\",\n \"nsg: raise Exception('inconsistent dimension nsg, regarding idxsg, usg.') dims.nsg = nsg ns =\",\n \"nsg_e nsphi_e = constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e:\",\n \"# nonlinear constraints if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0: template_dir =\",\n \"Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.') if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0]\",\n \"organized in the following order: \\\\n [ lbu lbx lg lh lphi ubu\",\n \"'initialize_t_slacks', 'step_length' :param value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase',\",\n \"Slack dimensions nsbx = constraints.idxsbx.shape[0] if is_empty(constraints.lsbx): constraints.lsbx = np.zeros((nsbx,)) elif constraints.lsbx.shape[0] !=\",\n \"else: dims.nbu = nbu ng = constraints.lg.shape[0] if constraints.ug.shape[0] != ng or constraints.C.shape[0]\",\n \"generate_c_code_gnsf from .generate_c_code_constraint import generate_c_code_constraint from .generate_c_code_nls_cost import generate_c_code_nls_cost from .generate_c_code_external_cost import generate_c_code_external_cost\",\n \"= constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension\",\n \"acados_ocp.__dict__ = ocp_nlp_dict # laod class attributes dict, dims, constraints, etc for acados_struct,\",\n \"<NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>,\",\n \"['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields = ['x', 'u', 'pi',\",\n \"self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) ==\",\n \"to shooting node :param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess' :param value_: of\",\n \"'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in int_fields: if\",\n \"= 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file = 'acados_solver_{}.h'.format(name) render_template(in_file,\",\n \"fields = ['time_tot', # total cpu time previous call 'time_lin', # cpu time\",\n \"total cpu time previous call 'time_lin', # cpu time for linearization 'time_sim', #\",\n \"wrong_field = \\\"\\\" if cost.Zl.shape[0] != ns: wrong_field = \\\"Zl\\\" dim = cost.Zl.shape[0]\",\n \"'sl', 'su',] .. note:: regarding lam, t: \\\\n the inequalities are internally organized\",\n \"= \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage,\",\n \"cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny: raise Exception('inconsistent dimension ny, regarding W,\",\n \"template_dir, json_path) # constraints on convex over nonlinear function if acados_ocp.constraints.constr_type == 'BGP'\",\n \"= casadi_length(model.con_h_expr_e) else: nh_e = 0 if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] !=\",\n \"idxsphi, usphi.') dims.nsphi = nsphi nsg = constraints.idxsg.shape[0] if is_empty(constraints.lsg): constraints.lsg = np.zeros((nsg,))\",\n \"self.shared_lib.acados_update_params.restype = c_int value_data = cast(value_.ctypes.data, POINTER(c_double)) self.shared_lib.acados_update_params(stage, value_data, value_.shape[0]) else: if field_\",\n \"formatting for acados_struct, v in ocp_layout.items(): # skip non dict attributes if not\",\n \"json_path) in_file = 'Makefile.in' out_file = 'Makefile' render_template(in_file, out_file, template_dir, json_path) in_file =\",\n \"Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n')\",\n \"= self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self):\",\n \"# table with info about last iteration 'stat_m', 'stat_n', ] field = field_\",\n \"mismatching dimension for field \\\"{}\\\" '.format(field_) msg += 'with dimension {} (you have\",\n \"is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization')\",\n \"!= ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \\\\\",\n \"'time_lin', # cpu time for linearization 'time_sim', # cpu time for integrator 'time_sim_ad',\",\n \"== 'GNSF': set_up_imported_gnsf_model(acados_ocp) # set integrator time automatically acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0] # generate\",\n \"nh = casadi_length(model.con_h_expr) else: nh = 0 if constraints.uh.shape[0] != nh or constraints.lh.shape[0]\",\n \"cost_set(self, stage_, field_, value_): \\\"\\\"\\\" set numerical data in the cost module of\",\n \"{} must be of type str. You have {}.'.format(field_, type(value_))) else: value_ctypes =\",\n \"{})'.format(dims, value_.shape[0]) raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if\",\n \"!= ns_e: wrong_field = \\\"zu_e\\\" dim = cost.zu_e.shape[0] if wrong_field != \\\"\\\": raise\",\n \"# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\",\n \"'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_ == 'statistics':\",\n \"= {nsphi_e}') dims.ns_e = ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform\",\n \"CDLL(self.shared_lib_name) self.shared_lib.acados_create() self.solver_created = True self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype =\",\n \"raise Exception(msg) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) if field_ in\",\n \"continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__)) # Copy ocp object attributes dictionaries ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp,\",\n \"== 'GNSF': generate_c_code_gnsf(model) else: raise Exception(\\\"ocp_generate_external_functions: unknown integrator type.\\\") if acados_ocp.solver_options.hessian_approx == 'EXACT':\",\n \"other_shape = constraints.ubx_0.shape if not this_shape == other_shape: raise Exception('lbx_0, ubx_0 have different\",\n \"[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p)\",\n \"if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension nsbu,\",\n \"out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else:\",\n \"+ f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh =\",\n \"integrator 'time_sim_ad', # cpu time for integrator contribution of external function calls 'time_sim_la',\",\n \"coding: future_fstrings -*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>,\",\n \"code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model, opts) elif acados_ocp.solver_options.integrator_type == 'GNSF': generate_c_code_gnsf(model) else: raise\",\n \"AcadosOcpSolver: \\\"\\\"\\\" class to interact with the acados ocp solver C object \\\"\\\"\\\"\",\n \"for nonuniform discretization') tf = np.sum(opts.time_steps) if (tf - opts.tf) / tf >\",\n \"deepcopy from .generate_c_code_explicit_ode import generate_c_code_explicit_ode from .generate_c_code_implicit_ode import generate_c_code_implicit_ode from .generate_c_code_gnsf import generate_c_code_gnsf\",\n \"'time_qp_xcond', 'time_reg', # cpu time regularization 'sqp_iter', # number of SQP iterations 'statistics',\",\n \"= 'acados_sim_solver.in.c' out_file = 'acados_sim_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_sim_solver.in.h' out_file\",\n \"value_.astype(float) field = field_ field = field.encode('utf-8') stage = c_int(stage_) # treat parameters\",\n \"float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization']\",\n \"field.encode('utf-8') stage = c_int(stage_) self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]\",\n \"elif constraints.usg_e.shape[0] != nsg_e: raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.') dims.nsg_e =\",\n \"= constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent\",\n \"and (not is_empty(opts.shooting_nodes)): Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization') tf\",\n \"out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p,\",\n \"value_: of type int, float \\\"\\\"\\\" int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields =\",\n \"= ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,)) elif constraints.lsbx_e.shape[0] !=\",\n \"W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type ==\",\n \"json_path) # external cost - terminal if acados_ocp.cost.cost_type_e == 'EXTERNAL': template_dir = 'c_generated_code/{}_cost/'.format(name)\",\n \"Copy input ocp object dictionary ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__) # TODO: maybe make one\",\n \"c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype = c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp\",\n \"regarding shooting_nodes.') time_steps = np.zeros((dims.N,)) for i in range(dims.N): time_steps[i] = opts.shooting_nodes[i+1] -\",\n \"constraints.idxsphi_e.shape[0] if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension\",\n \"c_void_p] self.shared_lib.ocp_nlp_out_get(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, out_data) elif field_ in mem_fields: self.shared_lib.ocp_nlp_get_at_stage.argtypes\",\n \"License # # Redistribution and use in source and binary forms, with or\",\n \"Vz.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\\\\n') if cost.Vx.shape[1] != dims.nx and\",\n \"in the cost module of the solver: :param stage_: integer corresponding to shooting\",\n \"if is_empty(constraints.lsphi_e): constraints.lsphi_e = np.zeros((nsphi_e,)) elif constraints.lsphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,\",\n \"== 'IRK': # implicit model -- generate C code opts = dict(generate_hess=1) generate_c_code_implicit_ode(model,\",\n \"self.nlp_out, stage_, field) if value_.shape[0] != dims: msg = 'AcadosOcpSolver.set(): mismatching dimension for\",\n \"!= nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.') dims.nsbx_e = nsbx_e nsh_e\",\n \"nh, regarding lh, uh, con_h_expr.') else: dims.nh = nh if is_empty(model.con_phi_expr): dims.nphi =\",\n \"TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE\",\n \"constraints.lg_e.shape[0] if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e: raise Exception('inconsistent dimension ng_e,\",\n \"regarding idxsh_e, lsh_e.') if is_empty(constraints.ush_e): constraints.ush_e = np.zeros((nsh_e,)) elif constraints.ush_e.shape[0] != nsh_e: raise\",\n \"regarding W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal\",\n \"yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0]\",\n \"STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY\",\n \"= cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape\",\n \"self.nlp_dims, self.nlp_out, stage_, field, dims_data) value_shape = value_.shape if len(value_shape) == 1: value_shape\",\n \"= ['print_level', 'rti_phase', 'initialize_t_slacks'] double_fields = ['step_length'] string_fields = ['globalization'] if field_ in\",\n \"if (constraints.lbx_0 == [] and constraints.ubx_0 == []): dims.nbx_0 = 0 else: this_shape\",\n \"= value_.encode('utf-8') if field_ == 'rti_phase': if value_ < 0 or value_ >\",\n \"INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\",\n \"= tf != sum(opts.time_steps) = {tf}.') def get_ocp_nlp_layout(): current_module = sys.modules[__name__] acados_path =\",\n \"inequalities \\\\n t: slack variables corresponding to evaluation of all inequalities (at the\",\n \"raise Exception('{} not found!'.format(json_path)) template_dir = 'c_generated_code/' ## Render templates in_file = 'main.in.c'\",\n \"cost module of the solver: :param stage_: integer corresponding to shooting node :param\",\n \"if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir = 'c_generated_code/{}_constraints/'.format(name) in_file =\",\n \"= self.get_stats(\\\"stat_n\\\") min_size = min([stat_m, sqp_iter+1]) out = np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ),\",\n \"WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\",\n \"+ cost_fields + out_fields: raise Exception(\\\"AcadosOcpSolver.set(): {} is not a valid argument.\\\\ \\\\nPossible\",\n \"out_data = cast(out.ctypes.data, POINTER(c_double)) else: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double))\",\n \"FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT\",\n \"is_empty(constraints.usbx_e): constraints.usbx_e = np.zeros((nsbx_e,)) elif constraints.usbx_e.shape[0] != nsbx_e: raise Exception('inconsistent dimension nsbx_e, regarding\",\n \"terminal nbx_e = constraints.idxbx_e.shape[0] if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e: raise\",\n \"is_empty(model.cost_y_expr) and ny != 0: raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.') elif\",\n \"f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e\",\n \"structure description ocp_layout = get_ocp_nlp_layout() # Copy input ocp object dictionary ocp_nlp_dict =\",\n \"= c_void_p self.nlp_out = self.shared_lib.acados_get_nlp_out() self.shared_lib.acados_get_nlp_in.restype = c_void_p self.nlp_in = self.shared_lib.acados_get_nlp_in() self.shared_lib.acados_get_nlp_solver.restype =\",\n \"regarding W, Vx, Vu.' + \\\\ f'\\\\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\\\\n') if dims.nz !=\",\n \"the ocp with current input \\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self,\",\n \"int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj]))) if stat.shape[0]>3: print('\\\\t{:e}\\\\t{:e}\\\\t{:e}\\\\t{:e}'.format( \\\\ stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj])) print('\\\\n') return\",\n \"Exception('AcadosOcpSolver.get(): {} is an invalid argument.\\\\ \\\\n Possible values are {}. Exiting.'.format(field_, out_fields\",\n \"field \\\"{}\\\" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data,\",\n \"from casadi import CasadiMeta, Function, SX from copy import deepcopy from .generate_c_code_explicit_ode import\",\n \"dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e: raise Exception('inconsistent dimension ny_e:\",\n \"strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict, dims_dict) with open(json_file, 'w') as\",\n \"['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data, POINTER(c_int64)) elif field_\",\n \"non dict attributes if not isinstance(v, dict): continue # setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__))\",\n \"'acados_solver.in.c' out_file = 'acados_solver_{}.c'.format(name) render_template(in_file, out_file, template_dir, json_path) in_file = 'acados_solver.in.h' out_file =\",\n \"to evaluation of all inequalities (at the solution) \\\\n sl: slack variables of\",\n \"regarding idxsh, lsh.') if is_empty(constraints.ush): constraints.ush = np.zeros((nsh,)) elif constraints.ush.shape[0] != nsh: raise\",\n \"if field_ in ['sqp_iter', 'stat_m', 'stat_n']: out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64) out_data = cast(out.ctypes.data,\",\n \"the following disclaimer. # # 2. Redistributions in binary form must reproduce the\",\n \"{})'.format(field_, tuple(dims), value_shape)) value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes =\",\n \"elif field_ in out_fields: self.shared_lib.ocp_nlp_out_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"= {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\\\\n\\\\t'\\\\ + f'With nsbx_e\",\n \"Exception('inconsistent dimension nsh, regarding idxsh, ush.') dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if\",\n \"calls 'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp', #\",\n \"Vx_e should have nx columns.') if cost.yref_e.shape[0] != ny_e: raise Exception('inconsistent dimension: regarding\",\n \"W, yref.' + \\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny # terminal if\",\n \"2. Redistributions in binary form must reproduce the above copyright notice, # this\",\n \"ug, C, D.') else: dims.ng = ng if not is_empty(model.con_h_expr): nh = casadi_length(model.con_h_expr)\",\n \"\\\"\\\"\\\" status = self.shared_lib.acados_solve() return status def get(self, stage_, field_): \\\"\\\"\\\" get the\",\n \"path if cost.cost_type == 'LINEAR_LS': ny = cost.W.shape[0] if cost.Vx.shape[0] != ny or\",\n \"symbolics ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model']) # strip shooting_nodes ocp_nlp_dict['solver_options'].pop('shooting_nodes', None) dims_dict = acados_class2dict(acados_ocp.dims) ocp_check_against_layout(ocp_nlp_dict,\",\n \"time qp solution 'time_qp_solver_call', # cpu time inside qp solver (without converting the\",\n \"-*- # # Copyright 2019 <NAME>, <NAME>, <NAME>, # <NAME>, <NAME>, <NAME>, <NAME>,\",\n \"# cast value_ to avoid conversion issues value_ = value_.astype(float) field = field_\",\n \"solution of the solver: :param stage: integer corresponding to shooting node :param field_:\",\n \"ubx, lbx.') else: dims.nbx = nbx nbu = constraints.idxbu.shape[0] if constraints.ubu.shape[0] != nbu\",\n \"'.format(field_) msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0]) raise Exception(msg) value_data\",\n \"else: opts = dict(generate_hess=0) if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model,\",\n \"# nz if is_empty(model.z): dims.nz = 0 else: dims.nz = casadi_length(model.z) # np\",\n \"out_file = '{}_phi_e_constraint.h'.format(name) render_template(in_file, out_file, template_dir, json_path) # nonlinear constraints if acados_ocp.constraints.constr_type ==\",\n \"to shooting node :param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't',\",\n \"# terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0: template_dir\",\n \"'t'] # cast value_ to avoid conversion issues value_ = value_.astype(float) field =\",\n \"to interact with the acados ocp solver C object \\\"\\\"\\\" def __init__(self, acados_ocp,\",\n \"= np.ascontiguousarray( np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64) out_data = cast(out.ctypes.data, POINTER(c_double)) else: out\",\n \"Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \\\\ ' for field \\\"{}\\\" with dimension {} (you have\",\n \"can ' 'take only values 0, 1, 2 for SQP-RTI-type solvers') if self.acados_ocp.solver_options.nlp_solver_type\",\n \"constraints.idxsbu.shape[0] if is_empty(constraints.lsbu): constraints.lsbu = np.zeros((nsbu,)) elif constraints.lsbu.shape[0] != nsbu: raise Exception('inconsistent dimension\",\n \"c_int, c_char_p, c_void_p] self.shared_lib.ocp_nlp_out_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out, stage, field, value_data_p) return def cost_set(self,\",\n \"if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if cost.Vx_e.shape[0] != ny_e: raise Exception('inconsistent\",\n \"of appropriate size \\\"\\\"\\\" # cast value_ to avoid conversion issues value_ =\",\n \"\\\\ f'\\\\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\\\\n') dims.ny = ny elif cost.cost_type == 'NONLINEAR_LS': ny =\",\n \"c_int dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc) dims_data = cast(dims.ctypes.data, POINTER(c_int)) self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \\\\ self.nlp_dims, self.nlp_out,\",\n \"render templates ocp_render_templates(acados_ocp, json_file) ## Compile solver os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..')\",\n \"= \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns: wrong_field = \\\"zu\\\" dim\",\n \"= {nsphi}') dims.ns = ns nsbx_e = constraints.idxsbx_e.shape[0] if is_empty(constraints.lsbx_e): constraints.lsbx_e = np.zeros((nsbx_e,))\",\n \"if is_empty(constraints.usphi_e): constraints.usphi_e = np.zeros((nsphi_e,)) elif constraints.usphi_e.shape[0] != nsphi_e: raise Exception('inconsistent dimension nsphi_e,\",\n \"ns_e # discretization if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes): # uniform discretization opts.time_steps = opts.tf\",\n \"dims.nbx_0 = constraints.lbx_0.size if all(constraints.lbx_0 == constraints.ubx_0): dims.nbxe_0 = dims.nbx_0 # path nbx\",\n \"nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}') dims.ns = ns nsbx_e\",\n \"nsh_e: raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.') dims.nsh_e = nsh_e nsg_e =\",\n \"c_char_p, c_void_p] self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \\\\ self.nlp_dims, self.nlp_in, stage, field, value_data_p) elif field_ in out_fields:\",\n \"'EXTERNAL': generate_c_code_external_cost(model, True) def ocp_render_templates(acados_ocp, json_file): name = acados_ocp.model.name # setting up loader\",\n \"!= 0: raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.') elif casadi_length(model.cost_y_expr_e) != ny_e:\",\n \"acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0: generate_c_code_constraint(model, model.name, False, opts) if acados_ocp.dims.nphi_e\",\n \"!= nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.') if is_empty(constraints.usphi): constraints.usphi =\",\n \"value_ctypes = c_double(value_) elif field_ in string_fields: if not isinstance(value_, str): raise Exception('solver\",\n \"os.chdir('c_generated_code') os.system('make clean_ocp_shared_lib') os.system('make ocp_shared_lib') os.chdir('..') self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so'\",\n \"inequality constraints \\\\n \\\"\\\"\\\" out_fields = ['x', 'u', 'z', 'pi', 'lam', 't'] mem_fields\",\n \"= field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \\\\ [c_void_p, c_void_p, c_char_p, c_char_p]\",\n \"dims.ny = ny # terminal if cost.cost_type_e == 'LINEAR_LS': ny_e = cost.W_e.shape[0] if\",\n \"dims.nsh = nsh nsphi = constraints.idxsphi.shape[0] if is_empty(constraints.lsphi): constraints.lsphi = np.zeros((nsphi,)) elif constraints.lsphi.shape[0]\",\n \"inside qp solver (without converting the QP) 'time_qp_xcond', 'time_reg', # cpu time regularization\",\n \"SQP-type solvers') field = field_ field = field.encode('utf-8') if field_ in string_fields: self.shared_lib.ocp_nlp_solver_opts_set.argtypes\",\n \"cost.zl.shape[0] != ns: wrong_field = \\\"zl\\\" dim = cost.zl.shape[0] elif cost.zu.shape[0] != ns:\",\n \"or constraints.lbu.shape[0] != nbu: raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.') else:\",\n \"use cost_set, constraints_set def set(self, stage_, field_, value_): cost_fields = ['y_ref', 'yref'] constraints_fields\",\n \"if field_ in int_fields: if not isinstance(value_, int): raise Exception('solver option {} must\",\n \"self.shared_lib.acados_get_nlp_opts.restype = c_void_p self.nlp_opts = self.shared_lib.acados_get_nlp_opts() self.shared_lib.acados_get_nlp_dims.restype = c_void_p self.nlp_dims = self.shared_lib.acados_get_nlp_dims() self.shared_lib.acados_get_nlp_config.restype\",\n \"c_void_p self.nlp_solver = self.shared_lib.acados_get_nlp_solver() self.acados_ocp = acados_ocp def solve(self): \\\"\\\"\\\" solve the ocp\",\n \"value_data = cast(value_.ctypes.data, POINTER(c_double)) value_data_p = cast((value_data), c_void_p) self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \\\\ [c_void_p, c_void_p,\",\n \"source code must retain the above copyright notice, # this list of conditions\",\n \"value_): cost_fields = ['y_ref', 'yref'] constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu'] out_fields =\",\n \"= np.zeros((nsphi,)) elif constraints.lsphi.shape[0] != nsphi: raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.')\",\n \"opts.time_steps = opts.tf / dims.N * np.ones((dims.N,)) elif not is_empty(opts.shooting_nodes): if np.shape(opts.shooting_nodes)[0] !=\",\n \"opts) # dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx))\",\n \"'time_sim_la', # cpu time for integrator contribution of linear algebra 'time_qp', # cpu\",\n \"elif field_ in cost_fields: self.shared_lib.ocp_nlp_cost_model_set.argtypes = \\\\ [c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]\",\n \"# dummy matrices if not acados_ocp.cost.cost_type == 'LINEAR_LS': acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx)) acados_ocp.cost.Vu\"\n]","isConversational":false}}},{"rowIdx":2598,"cells":{"ngram":{"kind":"list like","value":["sys.exit('The input of pch_size must be a integer or a int tuple!') if","np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__ ==","for start_y in rangey: end_x = start_x + psize end_y = start_y +","- psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM,","h: end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y =","estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0,","noise estimation results. :param saveResults: Whether to save the estimation results or not.","h, w = img.shape psize = min(min(patchSize, h), w) psize -= psize %","for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] =","im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch =","An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference","tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size,","Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey","ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] <","to save the noise estimation results. :param saveResults: Whether to save the estimation","pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The","image, numpy format pch_size: (int, int) tuple or integer stride: (int, int) tuple","# dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn,","deviation of (additive white gaussian) noise of image patches. The noise is estimated","pch_size: (int, int) tuple or integer stride: (int, int) tuple or integer '''","dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut):","a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int):","a integer or a int tuple!') C, H, W = im.shape num_H =","== np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\"","end_x > w: end_x = w end_x = shift_factor * ((end_x) // shift_factor)","3 x H x W or 1 X H x W image, numpy","noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following paper: <NAME>","x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1,","to save the estimation results or not. :return: None \"\"\" # Load image","x W or 1 X H x W image, numpy format pch_size: (int,","W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W))","imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut,","jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C,","pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x H x","(int, int) tuple or integer stride: (int, int) tuple or integer ''' if","= range(0, h, patch_step) for start_x in rangex: for start_y in rangey: end_x","or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int):","__name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding","= pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu =","axis=0) # image to patch pch = im2patch(im, pch_size, 3) # C x","range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for","glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3","to patch pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size","= stride else: sys.exit('The input of stride must be a integer or a","+ h_, start_x : start_x + w_] = sigma if saveResults: if dirOut","Whether to save the estimation results or not. :return: None \"\"\" # Load","h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y +","Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where","= img / 255.0 h, w = img.shape psize = min(min(patchSize, h), w)","Efficient Statistical Method for Image Noise Level Estimation\" (2015) :param imgPath: Path to","stride=1): ''' Transform image to patches. Input: im: 3 x H x W","else: sys.exit('The input of stride must be a integer or a int tuple!')","+ psize if end_x > w: end_x = w end_x = shift_factor *","of pch_size must be a integer or a int tuple!') if isinstance(stride, tuple):","== 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) #","be a integer or a int tuple!') C, H, W = im.shape num_H","Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE","dirOut: Directory where to save the noise estimation results. :param saveResults: Whether to","len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch),","np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj","Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society,","num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in range(pch_W):","w = img.shape psize = min(min(patchSize, h), w) psize -= psize % 2","noise level ''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else:","-1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def","Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015.","estimatedNoiseMap except: return None # Example # if __name__ == '__main__': # dirIn","1 X H x W image, numpy format pch_size: (int, int) tuple or","= len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H *","0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch pch =","W image, numpy format pch_size: (int, int) tuple or integer stride: (int, int)","np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step)","for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii]","or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride,","mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for","int): stride_H = stride_W = stride else: sys.exit('The input of stride must be","sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii","= start_y + psize if end_x > w: end_x = w end_x =","num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau","np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of","tuple or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple):","stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W =","patches. Input: im: 3 x H x W or 1 X H x","num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following","2015. Input: im: the noise image, H x W x 3 or H","C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch =","np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True):","pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu","= start_x + psize end_y = start_y + psize if end_x > w:","W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)","pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the","Input: im: the noise image, H x W x 3 or H x","pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d","estimated noise level ''' if im.ndim == 3: im = im.transpose((2, 0, 1))","im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3)","numpy format pch_size: (int, int) tuple or integer stride: (int, int) tuple or","= im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1","/ num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1):","size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save","if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath):","- psize if end_y > h: end_y = h end_y = shift_factor *","= end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma","The noise is estimated patch by patch. Based on: \"An Efficient Statistical Method","pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X)","isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W =","else: im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size,","dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for","if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im,","= pch_size else: sys.exit('The input of pch_size must be a integer or a","= pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ =","temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk +=","= np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if","((end_x) // shift_factor) start_x = end_x - psize if end_y > h: end_y","pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu sigma_X =","im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x H","Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer","sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut =","Estimation\" (2015) :param imgPath: Path to the input image. :param patchSize: Image patch","of sub-image-patches. :param dirOut: Directory where to save the noise estimation results. :param","\"An Efficient Statistical Method for Image Noise Level Estimation\" (2015) :param imgPath: Path","the estimated noise level ''' if im.ndim == 3: im = im.transpose((2, 0,","noise of image patches. The noise is estimated patch by patch. Based on:","X H x W image, numpy format pch_size: (int, int) tuple or integer","or not. :return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img","try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w =","isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of stride must","estimation results or not. :return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath))","255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize -= psize","ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C,","= psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8)","in rangex: for start_y in rangey: end_x = start_x + psize end_y =","pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation","int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H","x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x","level estimation of the following paper: <NAME> , <NAME> , <NAME> . An","= h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y -","% 2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap =","_ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii])","pch_size, 3) # C x pch_size x pch_size x num_pch tensor num_pch =","dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive white gaussian) noise of","tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x :","to patches. Input: im: 3 x H x W or 1 X H","H x W x 3 or H x W numpy tensor, range [0,1]","image to patch pch = im2patch(im, pch_size, 3) # C x pch_size x","\".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128, 8,","pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W,","psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize","if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not","3 or H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level:","img = img / 255.0 h, w = img.shape psize = min(min(patchSize, h),","pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch -","patch pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size x","np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau)","# Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step)","psize -= psize % 2 patch_step = psize shift_factor = 2 # Result","be a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W =","estimation of the following paper: <NAME> , <NAME> , <NAME> . An Efficient","start_y + psize if end_x > w: end_x = w end_x = shift_factor","saveResults=True): \"\"\" Estimates the standard deviation of (additive white gaussian) noise of image","tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard","Transform image to patches. Input: im: 3 x H x W or 1","h_, start_x : start_x + w_] = sigma if saveResults: if dirOut is","W x 3 or H x W numpy tensor, range [0,1] pch_size: patch_size","noise is estimated patch by patch. Based on: \"An Efficient Statistical Method for",":param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut:","im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to","im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return","= os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if","temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8):","keepdims=True) # d x 1 X = pch - mu sigma_X = np.matmul(X,","integer or a int tuple!') C, H, W = im.shape num_H = len(range(0,","return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation","255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma if",". An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International","num_pch)) # d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True)","Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on","Statistical Method for Image Noise Level Estimation\" (2015) :param imgPath: Path to the","start_x = end_x - psize if end_y > h: end_y = h end_y","Based on: \"An Efficient Statistical Method for Image Noise Level Estimation\" (2015) :param","the estimation results or not. :return: None \"\"\" # Load image img =","'__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\"","end_x = start_x + psize end_y = start_y + psize if end_x >","stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch =","is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut)","''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im =",":param saveResults: Whether to save the estimation results or not. :return: None \"\"\"","= pch_W = pch_size else: sys.exit('The input of pch_size must be a integer","integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H",", <NAME> , <NAME> . An Efficient Statistical Method for Image Noise Level","num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H","num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in","Estimates the standard deviation of (additive white gaussian) noise of image patches. The","start_x + psize end_y = start_y + psize if end_x > w: end_x","results or not. :return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try:","range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :]","= temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im,","following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method for","num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C,","= 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0,","end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x","for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV).","tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if","= im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch","= imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName","noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap","# dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" #","* num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii","- mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort()","kk = 0 for ii in range(pch_H): for jj in range(pch_W): temp =","return estimatedNoiseMap except: return None # Example # if __name__ == '__main__': #","in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk,","estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma if saveResults:","in rangey: end_x = start_x + psize end_y = start_y + psize if","tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride","isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must","= end_x - psize if end_y > h: end_y = h end_y =","is estimated patch by patch. Based on: \"An Efficient Statistical Method for Image","shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ =","= np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize,","Example # if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut =","os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input:","len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W","-d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau)","x W x 3 or H x W numpy tensor, range [0,1] pch_size:","img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape","= img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0","if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example #","# if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\"","im: 3 x H x W or 1 X H x W image,","numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level '''","if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut,","pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else:","end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y","return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level","Output: noise_level: the estimated noise level ''' if im.ndim == 3: im =","psize % 2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap","not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if","as np import cv2 import sys import os import glob def im2patch(im, pch_size,","elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of stride","w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x - psize","if end_x > w: end_x = w end_x = shift_factor * ((end_x) //","img / 255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize","2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input:",", <NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015","im.ndim == 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0)","np import cv2 import sys import os import glob def im2patch(im, pch_size, stride=1):","# C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch","= sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut","X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _","noise_level: the estimated noise level ''' if im.ndim == 3: im = im.transpose((2,","* ((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x]","patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for start_y in","psize if end_y > h: end_y = h end_y = shift_factor * ((end_y)",": start_x + w_] = sigma if saveResults: if dirOut is not None:","num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch","x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch","of stride must be a integer or a int tuple!') C, H, W","= pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu sigma_X","''' Transform image to patches. Input: im: 3 x H x W or","or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H,","isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W =","for ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]","on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image,","matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X","= r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath","(additive white gaussian) noise of image patches. The noise is estimated patch by","shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y,","psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex","np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w","tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) *","end_x - psize if end_y > h: end_y = h end_y = shift_factor","cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize = min(min(patchSize,","+ \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None #","pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim == 3:","= pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d =","estimation results. :param saveResults: Whether to save the estimation results or not. :return:",":return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img,","by patch. Based on: \"An Efficient Statistical Method for Image Noise Level Estimation\"","pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1,","\"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img","start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y","if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut)","import cv2 import sys import os import glob def im2patch(im, pch_size, stride=1): '''","if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W","sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau =","w_] = sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0]","Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H","= os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except:","stride must be a integer or a int tuple!') C, H, W =","h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y - psize","(int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size","\".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example","sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x","= range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex:","img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y","int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif","os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return","h, patch_step) for start_x in rangex: for start_y in rangey: end_x = start_x","''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H =","patch_size Output: noise_level: the estimated noise level ''' if im.ndim == 3: im","must be a integer or a int tuple!') C, H, W = im.shape","= im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch","2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h,","the noise estimation results. :param saveResults: Whether to save the estimation results or","must be a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W","+ psize end_y = start_y + psize if end_x > w: end_x =","internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive white gaussian) noise","end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if","2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w,","Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer","a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride","results. :param saveResults: Whether to save the estimation results or not. :return: None","start_y in rangey: end_x = start_x + psize end_y = start_y + psize","ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:,","# Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img =","range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim","array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey =","// shift_factor) start_x = end_x - psize if end_y > h: end_y =",":param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save the","pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch","not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath","os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__","in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch))","number of sub-image-patches. :param dirOut: Directory where to save the noise estimation results.","np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1,","[0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim ==","tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath,","H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated","Noise Level Estimation\" (2015) :param imgPath: Path to the input image. :param patchSize:","else: sys.exit('The input of pch_size must be a integer or a int tuple!')","Implement of noise level estimation of the following paper: <NAME> , <NAME> ,","H x W image, numpy format pch_size: (int, int) tuple or integer stride:","if end_y > h: end_y = h end_y = shift_factor * ((end_y) //","Internal number of sub-image-patches. :param dirOut: Directory where to save the noise estimation","= stride_W = stride else: sys.exit('The input of stride must be a integer","= r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" +","image to patches. Input: im: 3 x H x W or 1 X","noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_]","> h: end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y","x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise","tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H =","== '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding =","= np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3) #","= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize","shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex =",":start_y + h_, start_x : start_x + w_] = sigma if saveResults: if","<NAME> , <NAME> , <NAME> . An Efficient Statistical Method for Image Noise","np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches,","stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of","Society, 2015. Input: im: the noise image, H x W x 3 or","noise level estimation of the following paper: <NAME> , <NAME> , <NAME> .","w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for","= np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for","int) tuple or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size,","the following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method","IEEE Computer Society, 2015. Input: im: the noise image, H x W x","pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must be a","W or 1 X H x W image, numpy format pch_size: (int, int)","= pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch","im: the noise image, H x W x 3 or H x W","os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return","sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to","shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if end_y >","pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of","< tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the","standard deviation of (additive white gaussian) noise of image patches. The noise is","pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in","integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif","pch_size must be a integer or a int tuple!') if isinstance(stride, tuple): stride_H,","= np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h,","saveResults: Whether to save the estimation results or not. :return: None \"\"\" #","= min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize shift_factor","rangey: end_x = start_x + psize end_y = start_y + psize if end_x","except: return None # Example # if __name__ == '__main__': # dirIn =","Input: im: 3 x H x W or 1 X H x W","Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise","= img.shape psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step","image patches. The noise is estimated patch by patch. Based on: \"An Efficient","Computer Society, 2015. Input: im: the noise image, H x W x 3","kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch))","w: end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x =","+= 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of","or H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the","start_x + w_] = sigma if saveResults: if dirOut is not None: imgName","tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size",":] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def","img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0","shift_factor) start_x = end_x - psize if end_y > h: end_y = h","X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1,","w) psize -= psize % 2 patch_step = psize shift_factor = 2 #","estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__ == '__main__':","num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1","imgPath: Path to the input image. :param patchSize: Image patch size. :param internalNumPatches:","Directory where to save the noise estimation results. :param saveResults: Whether to save","= \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128,","the standard deviation of (additive white gaussian) noise of image patches. The noise","of the following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical","Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision","patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w],","3) # C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3]","x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) #","of image patches. The noise is estimated patch by patch. Based on: \"An","# for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath, 128, 8, dirOut)","= noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x +","None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)","Level Estimation\" (2015) :param imgPath: Path to the input image. :param patchSize: Image","Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img","end_y > h: end_y = h end_y = shift_factor * ((end_y) // shift_factor)","+ w_] = sigma if saveResults: if dirOut is not None: imgName =","tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W =","> w: end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x","im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch pch","stride else: sys.exit('The input of stride must be a integer or a int","(ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H x W","patch. Based on: \"An Efficient Statistical Method for Image Noise Level Estimation\" (2015)","1)) else: im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im,","pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch))","= 0 for ii in range(pch_H): for jj in range(pch_W): temp = im[:,","integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W","input of pch_size must be a integer or a int tuple!') if isinstance(stride,","d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X =","patch by patch. Based on: \"An Efficient Statistical Method for Image Noise Level","import sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image","pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H):","= np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in","pch_size else: sys.exit('The input of pch_size must be a integer or a int","range(0, h, patch_step) for start_x in rangex: for start_y in rangey: end_x =","d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d","save the estimation results or not. :return: None \"\"\" # Load image img","* 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma","<reponame>MaikWischow/Camera-Condition-Monitoring<gh_stars>1-10 import numpy as np import cv2 import sys import os import glob","noise image, H x W x 3 or H x W numpy tensor,","import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches.","estimated patch by patch. Based on: \"An Efficient Statistical Method for Image Noise","dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in range(pch_W): temp","imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): # run(imgPath,","gaussian) noise of image patches. The noise is estimated patch by patch. Based","tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix","internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save the noise","None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath =","im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch tensor num_pch","def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x","num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for","of (additive white gaussian) noise of image patches. The noise is estimated patch","input of stride must be a integer or a int tuple!') C, H,","start_x in rangex: for start_y in rangey: end_x = start_x + psize end_y","C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0,","dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\"","d x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) /","psize end_y = start_y + psize if end_x > w: end_x = w","input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches.","end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM","patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory","x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x","in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):","pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of","pch_W = pch_size else: sys.exit('The input of pch_size must be a integer or","1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise","w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_,","w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for start_y","= np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h,","/ 255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize -=","# Example # if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut","x H x W or 1 X H x W image, numpy format","range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return","stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk","int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must be","h), w) psize -= psize % 2 patch_step = psize shift_factor = 2","Path to the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal","= w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x -","H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch","patches. The noise is estimated patch by patch. Based on: \"An Efficient Statistical","H x W or 1 X H x W image, numpy format pch_size:","numpy as np import cv2 import sys import os import glob def im2patch(im,","img.shape psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step =","Method for Image Noise Level Estimation\" (2015) :param imgPath: Path to the input","pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0]","white gaussian) noise of image patches. The noise is estimated patch by patch.","import numpy as np import cv2 import sys import os import glob def","int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W","range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk","def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following paper:","sub-image-patches. :param dirOut: Directory where to save the noise estimation results. :param saveResults:","of noise level estimation of the following paper: <NAME> , <NAME> , <NAME>","stride_W = stride else: sys.exit('The input of stride must be a integer or","# d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) #","paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method for Image","internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] =","sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) ==","rangex: for start_y in rangey: end_x = start_x + psize end_y = start_y","x W image, numpy format pch_size: (int, int) tuple or integer stride: (int,","start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape","os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap)","elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size","= pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input","def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive","run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive white","cv2 import sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform","# image to patch pch = im2patch(im, pch_size, 3) # C x pch_size","image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param","= shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM =","start_x : start_x + w_] = sigma if saveResults: if dirOut is not","num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): '''","= num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0","the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of","if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" #","num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk =","if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W","psize if end_x > w: end_x = w end_x = shift_factor * ((end_x)","return None # Example # if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\"","* ((end_x) // shift_factor) start_x = end_x - psize if end_y > h:","end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma =","os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not","np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3) # C","rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in","np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates","on: \"An Efficient Statistical Method for Image Noise Level Estimation\" (2015) :param imgPath:","not. :return: None \"\"\" # Load image img = np.array(cv2.imread(imgPath)) try: img =","num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d","0 for ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H,","kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement","IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im:","image, H x W x 3 or H x W numpy tensor, range","imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName +","r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)):","-= psize % 2 patch_step = psize shift_factor = 2 # Result array","where to save the noise estimation results. :param saveResults: Whether to save the","Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H x","((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_,","= im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch tensor","save the noise estimation results. :param saveResults: Whether to save the estimation results","or a int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1,","pch_size=8): ''' Implement of noise level estimation of the following paper: <NAME> ,","''' Implement of noise level estimation of the following paper: <NAME> , <NAME>","x 3 or H x W numpy tensor, range [0,1] pch_size: patch_size Output:","level ''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im","= shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if end_y","H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1,","stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride else:","to the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number","<NAME> , <NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]//","1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value,","for Image Noise Level Estimation\" (2015) :param imgPath: Path to the input image.","None # Example # if __name__ == '__main__': # dirIn = r\"../../../data/udacity/img/GT\" #","image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img /","end_y = start_y + psize if end_x > w: end_x = w end_x","the noise image, H x W x 3 or H x W numpy","sys.exit('The input of stride must be a integer or a int tuple!') C,","# imgFileEnding = \".jpg\" # for imgPath in glob.glob(os.path.join(dirIn, \"*\" + imgFileEnding)): #","not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath,","stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The","= len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W,",":param dirOut: Directory where to save the noise estimation results. :param saveResults: Whether","jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H,","# d x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose())","dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x","\"\"\" Estimates the standard deviation of (additive white gaussian) noise of image patches.","dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \".npz\")",":param imgPath: Path to the input image. :param patchSize: Image patch size. :param","= stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input","(2015) :param imgPath: Path to the input image. :param patchSize: Image patch size.","patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to","or 1 X H x W image, numpy format pch_size: (int, int) tuple","Image Noise Level Estimation\" (2015) :param imgPath: Path to the input image. :param","// shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_","3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image","saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\".\")[0] dirOut = os.path.join(dirOut) if","format pch_size: (int, int) tuple or integer stride: (int, int) tuple or integer","r\"../../../data/udacity/img/GT\" # dirOut = r\"../../../data/udacity/labels_noise_patchwise/PCA\" # imgFileEnding = \".jpg\" # for imgPath in","cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize =","= tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x","a int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H))","patch_step) for start_x in rangex: for start_y in rangey: end_x = start_x +","rangey = range(0, h, patch_step) for start_x in rangex: for start_y in rangey:","<NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE","patchSize, internalNumPatches, dirOut, saveResults=True): \"\"\" Estimates the standard deviation of (additive white gaussian)","for start_x in rangex: for start_y in rangey: end_x = start_x + psize","stride_H = stride_W = stride else: sys.exit('The input of stride must be a","International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the","imgName + \".npz\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None","min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize shift_factor =","W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level","mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu","import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im:","psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches)"],"string":"[\n \"sys.exit('The input of pch_size must be a integer or a int tuple!') if\",\n \"np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__ ==\",\n \"for start_y in rangey: end_x = start_x + psize end_y = start_y +\",\n \"- psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM,\",\n \"h: end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y =\",\n \"estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0,\",\n \"noise estimation results. :param saveResults: Whether to save the estimation results or not.\",\n \"h, w = img.shape psize = min(min(patchSize, h), w) psize -= psize %\",\n \"for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] =\",\n \"im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch =\",\n \"An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference\",\n \"tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size,\",\n \"Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey\",\n \"ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] <\",\n \"to save the noise estimation results. :param saveResults: Whether to save the estimation\",\n \"pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The\",\n \"image, numpy format pch_size: (int, int) tuple or integer stride: (int, int) tuple\",\n \"# dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn,\",\n \"deviation of (additive white gaussian) noise of image patches. The noise is estimated\",\n \"pch_size: (int, int) tuple or integer stride: (int, int) tuple or integer '''\",\n \"dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut):\",\n \"a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int):\",\n \"a integer or a int tuple!') C, H, W = im.shape num_H =\",\n \"== np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\"\",\n \"end_x > w: end_x = w end_x = shift_factor * ((end_x) // shift_factor)\",\n \"3 x H x W or 1 X H x W image, numpy\",\n \"noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following paper: <NAME>\",\n \"x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1,\",\n \"to save the estimation results or not. :return: None \\\"\\\"\\\" # Load image\",\n \"x W or 1 X H x W image, numpy format pch_size: (int,\",\n \"W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W))\",\n \"imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut,\",\n \"jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C,\",\n \"pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x H x\",\n \"(int, int) tuple or integer stride: (int, int) tuple or integer ''' if\",\n \"= range(0, h, patch_step) for start_x in rangex: for start_y in rangey: end_x\",\n \"or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int):\",\n \"__name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding\",\n \"= pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu =\",\n \"axis=0) # image to patch pch = im2patch(im, pch_size, 3) # C x\",\n \"range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for\",\n \"glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3\",\n \"to patch pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size\",\n \"= stride else: sys.exit('The input of stride must be a integer or a\",\n \"+ h_, start_x : start_x + w_] = sigma if saveResults: if dirOut\",\n \"Whether to save the estimation results or not. :return: None \\\"\\\"\\\" # Load\",\n \"h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y +\",\n \"Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where\",\n \"= img / 255.0 h, w = img.shape psize = min(min(patchSize, h), w)\",\n \"Efficient Statistical Method for Image Noise Level Estimation\\\" (2015) :param imgPath: Path to\",\n \"stride=1): ''' Transform image to patches. Input: im: 3 x H x W\",\n \"else: sys.exit('The input of stride must be a integer or a int tuple!')\",\n \"+ psize if end_x > w: end_x = w end_x = shift_factor *\",\n \"of pch_size must be a integer or a int tuple!') if isinstance(stride, tuple):\",\n \"== 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) #\",\n \"be a integer or a int tuple!') C, H, W = im.shape num_H\",\n \"Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE\",\n \"dirOut: Directory where to save the noise estimation results. :param saveResults: Whether to\",\n \"len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch),\",\n \"np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj\",\n \"Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society,\",\n \"num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in range(pch_W):\",\n \"w = img.shape psize = min(min(patchSize, h), w) psize -= psize % 2\",\n \"noise level ''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else:\",\n \"-1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def\",\n \"Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015.\",\n \"estimatedNoiseMap except: return None # Example # if __name__ == '__main__': # dirIn\",\n \"1 X H x W image, numpy format pch_size: (int, int) tuple or\",\n \"= len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H *\",\n \"0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch pch =\",\n \"W image, numpy format pch_size: (int, int) tuple or integer stride: (int, int)\",\n \"np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step)\",\n \"for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii]\",\n \"or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride,\",\n \"mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for\",\n \"int): stride_H = stride_W = stride else: sys.exit('The input of stride must be\",\n \"sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii\",\n \"= start_y + psize if end_x > w: end_x = w end_x =\",\n \"num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau\",\n \"np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of\",\n \"tuple or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple):\",\n \"stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W =\",\n \"patches. Input: im: 3 x H x W or 1 X H x\",\n \"num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following\",\n \"2015. Input: im: the noise image, H x W x 3 or H\",\n \"C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch =\",\n \"np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True):\",\n \"pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu\",\n \"= start_x + psize end_y = start_y + psize if end_x > w:\",\n \"W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)\",\n \"pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the\",\n \"Input: im: the noise image, H x W x 3 or H x\",\n \"pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d\",\n \"estimated noise level ''' if im.ndim == 3: im = im.transpose((2, 0, 1))\",\n \"im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3)\",\n \"numpy format pch_size: (int, int) tuple or integer stride: (int, int) tuple or\",\n \"= im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1\",\n \"/ num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1):\",\n \"size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save\",\n \"if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath):\",\n \"- psize if end_y > h: end_y = h end_y = shift_factor *\",\n \"= end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma\",\n \"The noise is estimated patch by patch. Based on: \\\"An Efficient Statistical Method\",\n \"pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X)\",\n \"isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W =\",\n \"else: im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size,\",\n \"dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for\",\n \"if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im,\",\n \"= pch_size else: sys.exit('The input of pch_size must be a integer or a\",\n \"= pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ =\",\n \"temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk +=\",\n \"= np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if\",\n \"((end_x) // shift_factor) start_x = end_x - psize if end_y > h: end_y\",\n \"pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu sigma_X =\",\n \"im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x H\",\n \"Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer\",\n \"sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut =\",\n \"Estimation\\\" (2015) :param imgPath: Path to the input image. :param patchSize: Image patch\",\n \"of sub-image-patches. :param dirOut: Directory where to save the noise estimation results. :param\",\n \"\\\"An Efficient Statistical Method for Image Noise Level Estimation\\\" (2015) :param imgPath: Path\",\n \"the estimated noise level ''' if im.ndim == 3: im = im.transpose((2, 0,\",\n \"noise of image patches. The noise is estimated patch by patch. Based on:\",\n \"X H x W image, numpy format pch_size: (int, int) tuple or integer\",\n \"or not. :return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img\",\n \"try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w =\",\n \"isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of stride must\",\n \"estimation results or not. :return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath))\",\n \"255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize -= psize\",\n \"ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C,\",\n \"= psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8)\",\n \"in rangex: for start_y in rangey: end_x = start_x + psize end_y =\",\n \"pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation\",\n \"int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H\",\n \"x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x\",\n \"level estimation of the following paper: <NAME> , <NAME> , <NAME> . An\",\n \"= h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y -\",\n \"% 2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap =\",\n \"_ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii])\",\n \"pch_size, 3) # C x pch_size x pch_size x num_pch tensor num_pch =\",\n \"dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian) noise of\",\n \"tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x :\",\n \"to patches. Input: im: 3 x H x W or 1 X H\",\n \"H x W x 3 or H x W numpy tensor, range [0,1]\",\n \"image to patch pch = im2patch(im, pch_size, 3) # C x pch_size x\",\n \"\\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128, 8,\",\n \"pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W,\",\n \"psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize\",\n \"if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not\",\n \"3 or H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level:\",\n \"img = img / 255.0 h, w = img.shape psize = min(min(patchSize, h),\",\n \"pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch -\",\n \"patch pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size x\",\n \"np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau)\",\n \"# Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step)\",\n \"psize -= psize % 2 patch_step = psize shift_factor = 2 # Result\",\n \"be a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W =\",\n \"estimation of the following paper: <NAME> , <NAME> , <NAME> . An Efficient\",\n \"start_y + psize if end_x > w: end_x = w end_x = shift_factor\",\n \"saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian) noise of image\",\n \"tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard\",\n \"Transform image to patches. Input: im: 3 x H x W or 1\",\n \"h_, start_x : start_x + w_] = sigma if saveResults: if dirOut is\",\n \"W x 3 or H x W numpy tensor, range [0,1] pch_size: patch_size\",\n \"noise is estimated patch by patch. Based on: \\\"An Efficient Statistical Method for\",\n \":param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut:\",\n \"im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to\",\n \"im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return\",\n \"= os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if\",\n \"temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8):\",\n \"keepdims=True) # d x 1 X = pch - mu sigma_X = np.matmul(X,\",\n \"integer or a int tuple!') C, H, W = im.shape num_H = len(range(0,\",\n \"return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation\",\n \"255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma if\",\n \". An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International\",\n \"num_pch)) # d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True)\",\n \"Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on\",\n \"Statistical Method for Image Noise Level Estimation\\\" (2015) :param imgPath: Path to the\",\n \"start_x = end_x - psize if end_y > h: end_y = h end_y\",\n \"Based on: \\\"An Efficient Statistical Method for Image Noise Level Estimation\\\" (2015) :param\",\n \"the estimation results or not. :return: None \\\"\\\"\\\" # Load image img =\",\n \"'__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\"\",\n \"end_x = start_x + psize end_y = start_y + psize if end_x >\",\n \"stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch =\",\n \"is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut)\",\n \"''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im =\",\n \":param saveResults: Whether to save the estimation results or not. :return: None \\\"\\\"\\\"\",\n \"= pch_W = pch_size else: sys.exit('The input of pch_size must be a integer\",\n \"integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H\",\n \", <NAME> , <NAME> . An Efficient Statistical Method for Image Noise Level\",\n \"num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H\",\n \"num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in\",\n \"Estimates the standard deviation of (additive white gaussian) noise of image patches. The\",\n \"start_x + psize end_y = start_y + psize if end_x > w: end_x\",\n \"results or not. :return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try:\",\n \"range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :]\",\n \"= temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im,\",\n \"following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method for\",\n \"num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C,\",\n \"= 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0,\",\n \"end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x\",\n \"for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision (ICCV).\",\n \"tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if\",\n \"= im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch\",\n \"= imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName\",\n \"noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap\",\n \"# dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" #\",\n \"* num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii\",\n \"- mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort()\",\n \"kk = 0 for ii in range(pch_H): for jj in range(pch_W): temp =\",\n \"return estimatedNoiseMap except: return None # Example # if __name__ == '__main__': #\",\n \"in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk,\",\n \"estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma if saveResults:\",\n \"in rangey: end_x = start_x + psize end_y = start_y + psize if\",\n \"tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride\",\n \"isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must\",\n \"= end_x - psize if end_y > h: end_y = h end_y =\",\n \"is estimated patch by patch. Based on: \\\"An Efficient Statistical Method for Image\",\n \"shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ =\",\n \"= np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize,\",\n \"Example # if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut =\",\n \"os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input:\",\n \"len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W\",\n \"-d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau)\",\n \"x W x 3 or H x W numpy tensor, range [0,1] pch_size:\",\n \"img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape\",\n \"= img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0\",\n \"if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example #\",\n \"# if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\"\",\n \"im: 3 x H x W or 1 X H x W image,\",\n \"numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level '''\",\n \"if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut,\",\n \"pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else:\",\n \"end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y\",\n \"return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level\",\n \"Output: noise_level: the estimated noise level ''' if im.ndim == 3: im =\",\n \"psize % 2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap\",\n \"not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if\",\n \"as np import cv2 import sys import os import glob def im2patch(im, pch_size,\",\n \"elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of stride\",\n \"w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x - psize\",\n \"if end_x > w: end_x = w end_x = shift_factor * ((end_x) //\",\n \"img / 255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize\",\n \"2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input:\",\n \", <NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015\",\n \"im.ndim == 3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0)\",\n \"np import cv2 import sys import os import glob def im2patch(im, pch_size, stride=1):\",\n \"# C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch\",\n \"= sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut\",\n \"X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value, _\",\n \"noise_level: the estimated noise level ''' if im.ndim == 3: im = im.transpose((2,\",\n \"* ((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x]\",\n \"patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for start_y in\",\n \"psize if end_y > h: end_y = h end_y = shift_factor * ((end_y)\",\n \": start_x + w_] = sigma if saveResults: if dirOut is not None:\",\n \"num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch\",\n \"x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch\",\n \"of stride must be a integer or a int tuple!') C, H, W\",\n \"= pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu sigma_X\",\n \"''' Transform image to patches. Input: im: 3 x H x W or\",\n \"or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H,\",\n \"isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W =\",\n \"for ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]\",\n \"on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image,\",\n \"matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X\",\n \"= r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath\",\n \"(additive white gaussian) noise of image patches. The noise is estimated patch by\",\n \"shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y,\",\n \"psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex\",\n \"np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w\",\n \"tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) *\",\n \"end_x - psize if end_y > h: end_y = h end_y = shift_factor\",\n \"cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize = min(min(patchSize,\",\n \"+ \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None #\",\n \"pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim == 3:\",\n \"= pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d =\",\n \"estimation results. :param saveResults: Whether to save the estimation results or not. :return:\",\n \":return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img,\",\n \"by patch. Based on: \\\"An Efficient Statistical Method for Image Noise Level Estimation\\\"\",\n \"pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1,\",\n \"\\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img\",\n \"start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y\",\n \"if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut)\",\n \"import cv2 import sys import os import glob def im2patch(im, pch_size, stride=1): '''\",\n \"if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W\",\n \"sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1, -1): tau =\",\n \"w_] = sigma if saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0]\",\n \"Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H\",\n \"= os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except:\",\n \"stride must be a integer or a int tuple!') C, H, W =\",\n \"h end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y - psize\",\n \"(int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size\",\n \"\\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example\",\n \"sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x\",\n \"= range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex:\",\n \"img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y\",\n \"int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif\",\n \"os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return\",\n \"h, patch_step) for start_x in rangex: for start_y in rangey: end_x = start_x\",\n \"''' if isinstance(pch_size, tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H =\",\n \"patch_size Output: noise_level: the estimated noise level ''' if im.ndim == 3: im\",\n \"must be a integer or a int tuple!') C, H, W = im.shape\",\n \"= im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch\",\n \"2 patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h,\",\n \"the noise estimation results. :param saveResults: Whether to save the estimation results or\",\n \"must be a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W\",\n \"+ psize end_y = start_y + psize if end_x > w: end_x =\",\n \"internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian) noise\",\n \"end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if\",\n \"2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w,\",\n \"Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer\",\n \"a integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride\",\n \"results. :param saveResults: Whether to save the estimation results or not. :return: None\",\n \"start_y in rangey: end_x = start_x + psize end_y = start_y + psize\",\n \"ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:,\",\n \"# Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img =\",\n \"range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim\",\n \"array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey =\",\n \"// shift_factor) start_x = end_x - psize if end_y > h: end_y =\",\n \":param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save the\",\n \"pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch\",\n \"not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath\",\n \"os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__\",\n \"in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch))\",\n \"number of sub-image-patches. :param dirOut: Directory where to save the noise estimation results.\",\n \"np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1,\",\n \"[0,1] pch_size: patch_size Output: noise_level: the estimated noise level ''' if im.ndim ==\",\n \"tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath,\",\n \"H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated\",\n \"Noise Level Estimation\\\" (2015) :param imgPath: Path to the input image. :param patchSize:\",\n \"else: sys.exit('The input of pch_size must be a integer or a int tuple!')\",\n \"Implement of noise level estimation of the following paper: <NAME> , <NAME> ,\",\n \"H x W image, numpy format pch_size: (int, int) tuple or integer stride:\",\n \"if end_y > h: end_y = h end_y = shift_factor * ((end_y) //\",\n \"Internal number of sub-image-patches. :param dirOut: Directory where to save the noise estimation\",\n \"= stride_W = stride else: sys.exit('The input of stride must be a integer\",\n \"= r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" +\",\n \"image to patches. Input: im: 3 x H x W or 1 X\",\n \"noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_]\",\n \"> h: end_y = h end_y = shift_factor * ((end_y) // shift_factor) start_y\",\n \"x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise\",\n \"tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H =\",\n \"== '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding =\",\n \"= np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3) #\",\n \"= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize\",\n \"shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w], dtype=np.int8) rangex =\",\n \":start_y + h_, start_x : start_x + w_] = sigma if saveResults: if\",\n \"<NAME> , <NAME> , <NAME> . An Efficient Statistical Method for Image Noise\",\n \"np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches,\",\n \"stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input of\",\n \"Society, 2015. Input: im: the noise image, H x W x 3 or\",\n \"noise level estimation of the following paper: <NAME> , <NAME> , <NAME> .\",\n \"w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for\",\n \"= np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for\",\n \"int) tuple or integer stride: (int, int) tuple or integer ''' if isinstance(pch_size,\",\n \"the following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method\",\n \"IEEE Computer Society, 2015. Input: im: the noise image, H x W x\",\n \"pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must be a\",\n \"W or 1 X H x W image, numpy format pch_size: (int, int)\",\n \"= pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch\",\n \"im: the noise image, H x W x 3 or H x W\",\n \"os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return\",\n \"sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to\",\n \"shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if end_y >\",\n \"pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of\",\n \"< tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the\",\n \"standard deviation of (additive white gaussian) noise of image patches. The noise is\",\n \"pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in\",\n \"integer or a int tuple!') if isinstance(stride, tuple): stride_H, stride_W = stride elif\",\n \"pch_size must be a integer or a int tuple!') if isinstance(stride, tuple): stride_H,\",\n \"= np.zeros([h, w], dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h,\",\n \"saveResults: Whether to save the estimation results or not. :return: None \\\"\\\"\\\" #\",\n \"= min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize shift_factor\",\n \"rangey: end_x = start_x + psize end_y = start_y + psize if end_x\",\n \"except: return None # Example # if __name__ == '__main__': # dirIn =\",\n \"Input: im: 3 x H x W or 1 X H x W\",\n \"Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise\",\n \"= img.shape psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step\",\n \"image patches. The noise is estimated patch by patch. Based on: \\\"An Efficient\",\n \"Computer Society, 2015. Input: im: the noise image, H x W x 3\",\n \"kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch))\",\n \"w: end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x =\",\n \"+= 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of\",\n \"or H x W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the\",\n \"start_x + w_] = sigma if saveResults: if dirOut is not None: imgName\",\n \"tuple): pch_H, pch_W = pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size\",\n \":] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def\",\n \"img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0\",\n \"shift_factor) start_x = end_x - psize if end_y > h: end_y = h\",\n \"X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in range(-1, -d-1,\",\n \"w) psize -= psize % 2 patch_step = psize shift_factor = 2 #\",\n \"estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__ == '__main__':\",\n \"num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1\",\n \"imgPath: Path to the input image. :param patchSize: Image patch size. :param internalNumPatches:\",\n \"Directory where to save the noise estimation results. :param saveResults: Whether to save\",\n \"= \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128,\",\n \"the standard deviation of (additive white gaussian) noise of image patches. The noise\",\n \"of the following paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical\",\n \"Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference on Computer Vision\",\n \"patch_step = psize shift_factor = 2 # Result array estimatedNoiseMap = np.zeros([h, w],\",\n \"3) # C x pch_size x pch_size x num_pch tensor num_pch = pch.shape[3]\",\n \"x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) #\",\n \"of image patches. The noise is estimated patch by patch. Based on: \\\"An\",\n \"# for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath, 128, 8, dirOut)\",\n \"= noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x +\",\n \"None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\",\n \"Level Estimation\\\" (2015) :param imgPath: Path to the input image. :param patchSize: Image\",\n \"Load image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img\",\n \"end_y > h: end_y = h end_y = shift_factor * ((end_y) // shift_factor)\",\n \"+ w_] = sigma if saveResults: if dirOut is not None: imgName =\",\n \"tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W =\",\n \"> w: end_x = w end_x = shift_factor * ((end_x) // shift_factor) start_x\",\n \"im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image to patch pch\",\n \"stride else: sys.exit('The input of stride must be a integer or a int\",\n \"(ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H x W\",\n \"patch. Based on: \\\"An Efficient Statistical Method for Image Noise Level Estimation\\\" (2015)\",\n \"1)) else: im = np.expand_dims(im, axis=0) # image to patch pch = im2patch(im,\",\n \"pch_size x pch_size x num_pch tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch))\",\n \"= 0 for ii in range(pch_H): for jj in range(pch_W): temp = im[:,\",\n \"integer stride: (int, int) tuple or integer ''' if isinstance(pch_size, tuple): pch_H, pch_W\",\n \"input of pch_size must be a integer or a int tuple!') if isinstance(stride,\",\n \"d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x 1 X =\",\n \"patch by patch. Based on: \\\"An Efficient Statistical Method for Image Noise Level\",\n \"import sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image\",\n \"pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for ii in range(pch_H):\",\n \"= np.matmul(X, X.transpose()) / num_pch sig_value, _ = np.linalg.eigh(sigma_X) sig_value.sort() for ii in\",\n \"pch_size else: sys.exit('The input of pch_size must be a integer or a int\",\n \"range(0, h, patch_step) for start_x in rangex: for start_y in rangey: end_x =\",\n \"d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d\",\n \"save the estimation results or not. :return: None \\\"\\\"\\\" # Load image img\",\n \"* 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma\",\n \"<reponame>MaikWischow/Camera-Condition-Monitoring<gh_stars>1-10 import numpy as np import cv2 import sys import os import glob\",\n \"noise image, H x W x 3 or H x W numpy tensor,\",\n \"import os import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches.\",\n \"estimated patch by patch. Based on: \\\"An Efficient Statistical Method for Image Noise\",\n \"dtype=im.dtype) kk = 0 for ii in range(pch_H): for jj in range(pch_W): temp\",\n \"imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): # run(imgPath,\",\n \"gaussian) noise of image patches. The noise is estimated patch by patch. Based\",\n \"tensor num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix\",\n \"internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to save the noise\",\n \"None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath =\",\n \"im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch tensor num_pch\",\n \"def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im: 3 x\",\n \"num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0 for\",\n \"of (additive white gaussian) noise of image patches. The noise is estimated patch\",\n \"input of stride must be a integer or a int tuple!') C, H,\",\n \"start_x in rangex: for start_y in rangey: end_x = start_x + psize end_y\",\n \"C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0,\",\n \"dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\"\",\n \"d x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) /\",\n \"psize end_y = start_y + psize if end_x > w: end_x = w\",\n \"input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches.\",\n \"end_y = shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM\",\n \"patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory\",\n \"x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) # d x\",\n \"in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):\",\n \"pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of\",\n \"pch_W = pch_size else: sys.exit('The input of pch_size must be a integer or\",\n \"1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement of noise\",\n \"w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_,\",\n \"w, patch_step) rangey = range(0, h, patch_step) for start_x in rangex: for start_y\",\n \"= np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h,\",\n \"/ 255.0 h, w = img.shape psize = min(min(patchSize, h), w) psize -=\",\n \"# Example # if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut\",\n \"x H x W or 1 X H x W image, numpy format\",\n \"range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau): return\",\n \"stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk\",\n \"int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size must be\",\n \"h), w) psize -= psize % 2 patch_step = psize shift_factor = 2\",\n \"Path to the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal\",\n \"= w end_x = shift_factor * ((end_x) // shift_factor) start_x = end_x -\",\n \"H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch\",\n \"patches. The noise is estimated patch by patch. Based on: \\\"An Efficient Statistical\",\n \"H x W or 1 X H x W image, numpy format pch_size:\",\n \"numpy as np import cv2 import sys import os import glob def im2patch(im,\",\n \"img.shape psize = min(min(patchSize, h), w) psize -= psize % 2 patch_step =\",\n \"Method for Image Noise Level Estimation\\\" (2015) :param imgPath: Path to the input\",\n \"pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d = pch.shape[0]\",\n \"white gaussian) noise of image patches. The noise is estimated patch by patch.\",\n \"import numpy as np import cv2 import sys import os import glob def\",\n \"int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W\",\n \"range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk\",\n \"def noise_estimate(im, pch_size=8): ''' Implement of noise level estimation of the following paper:\",\n \"sub-image-patches. :param dirOut: Directory where to save the noise estimation results. :param saveResults:\",\n \"of noise level estimation of the following paper: <NAME> , <NAME> , <NAME>\",\n \"stride_W = stride else: sys.exit('The input of stride must be a integer or\",\n \"# d x num_pch matrix d = pch.shape[0] mu = pch.mean(axis=1, keepdims=True) #\",\n \"paper: <NAME> , <NAME> , <NAME> . An Efficient Statistical Method for Image\",\n \"internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] =\",\n \"sig_value.sort() for ii in range(-1, -d-1, -1): tau = np.mean(sig_value[:ii]) if np.sum(sig_value[:ii]>tau) ==\",\n \"rangex: for start_y in rangey: end_x = start_x + psize end_y = start_y\",\n \"x W image, numpy format pch_size: (int, int) tuple or integer stride: (int,\",\n \"start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape\",\n \"os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap)\",\n \"elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input of pch_size\",\n \"= pch_size elif isinstance(pch_size, int): pch_H = pch_W = pch_size else: sys.exit('The input\",\n \"def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive\",\n \"run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white\",\n \"cv2 import sys import os import glob def im2patch(im, pch_size, stride=1): ''' Transform\",\n \"# image to patch pch = im2patch(im, pch_size, 3) # C x pch_size\",\n \"image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of sub-image-patches. :param\",\n \"= shift_factor * ((end_y) // shift_factor) start_y = end_y - psize tileM =\",\n \"start_x : start_x + w_] = sigma if saveResults: if dirOut is not\",\n \"num_pch)) kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): '''\",\n \"= num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk = 0\",\n \"the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number of\",\n \"if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" #\",\n \"num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype) kk =\",\n \"if isinstance(stride, tuple): stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W\",\n \"psize if end_x > w: end_x = w end_x = shift_factor * ((end_x)\",\n \"return None # Example # if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\"\",\n \"* ((end_x) // shift_factor) start_x = end_x - psize if end_y > h:\",\n \"end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma =\",\n \"os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not\",\n \"np.expand_dims(im, axis=0) # image to patch pch = im2patch(im, pch_size, 3) # C\",\n \"rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x in\",\n \"np.sum(sig_value[:ii] < tau): return np.sqrt(tau) def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates\",\n \"on: \\\"An Efficient Statistical Method for Image Noise Level Estimation\\\" (2015) :param imgPath:\",\n \"not. :return: None \\\"\\\"\\\" # Load image img = np.array(cv2.imread(imgPath)) try: img =\",\n \"num_pch = pch.shape[3] pch = pch.reshape((-1, num_pch)) # d x num_pch matrix d\",\n \"0 for ii in range(pch_H): for jj in range(pch_W): temp = im[:, ii:H-pch_H+ii+1:stride_H,\",\n \"kk += 1 return pch.reshape((C, pch_H, pch_W, num_pch)) def noise_estimate(im, pch_size=8): ''' Implement\",\n \"IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im:\",\n \"image, H x W x 3 or H x W numpy tensor, range\",\n \"imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName +\",\n \"r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)):\",\n \"-= psize % 2 patch_step = psize shift_factor = 2 # Result array\",\n \"where to save the noise estimation results. :param saveResults: Whether to save the\",\n \"Vision (ICCV). IEEE Computer Society, 2015. Input: im: the noise image, H x\",\n \"((end_y) // shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_,\",\n \"= im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch tensor\",\n \"save the noise estimation results. :param saveResults: Whether to save the estimation results\",\n \"or a int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1,\",\n \"pch_size=8): ''' Implement of noise level estimation of the following paper: <NAME> ,\",\n \"''' Implement of noise level estimation of the following paper: <NAME> , <NAME>\",\n \"x 3 or H x W numpy tensor, range [0,1] pch_size: patch_size Output:\",\n \"level ''' if im.ndim == 3: im = im.transpose((2, 0, 1)) else: im\",\n \"= shift_factor * ((end_x) // shift_factor) start_x = end_x - psize if end_y\",\n \"H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H)) num_W = len(range(0, W-pch_W+1,\",\n \"stride_H, stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride else:\",\n \"to the input image. :param patchSize: Image patch size. :param internalNumPatches: Internal number\",\n \"<NAME> , <NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]//\",\n \"1 X = pch - mu sigma_X = np.matmul(X, X.transpose()) / num_pch sig_value,\",\n \"for Image Noise Level Estimation\\\" (2015) :param imgPath: Path to the input image.\",\n \"None # Example # if __name__ == '__main__': # dirIn = r\\\"../../../data/udacity/img/GT\\\" #\",\n \"image img = np.array(cv2.imread(imgPath)) try: img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img /\",\n \"end_y = start_y + psize if end_x > w: end_x = w end_x\",\n \"the noise image, H x W x 3 or H x W numpy\",\n \"sys.exit('The input of stride must be a integer or a int tuple!') C,\",\n \"# imgFileEnding = \\\".jpg\\\" # for imgPath in glob.glob(os.path.join(dirIn, \\\"*\\\" + imgFileEnding)): #\",\n \"not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath,\",\n \"stride_W = stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The\",\n \"= len(range(0, W-pch_W+1, stride_W)) num_pch = num_H * num_W pch = np.zeros((C, pch_H*pch_W,\",\n \":param dirOut: Directory where to save the noise estimation results. :param saveResults: Whether\",\n \"jj:W-pch_W+jj+1:stride_W] pch[:, kk, :] = temp.reshape((C, num_pch)) kk += 1 return pch.reshape((C, pch_H,\",\n \"# d x 1 X = pch - mu sigma_X = np.matmul(X, X.transpose())\",\n \"dtype=np.int8) rangex = range(0, w, patch_step) rangey = range(0, h, patch_step) for start_x\",\n \"\\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian) noise of image patches.\",\n \"dirOut = os.path.join(dirOut) if not os.path.exists(dirOut): os.makedirs(dirOut) noiseMapPath = os.path.join(dirOut, imgName + \\\".npz\\\")\",\n \":param imgPath: Path to the input image. :param patchSize: Image patch size. :param\",\n \"= stride elif isinstance(stride, int): stride_H = stride_W = stride else: sys.exit('The input\",\n \"(2015) :param imgPath: Path to the input image. :param patchSize: Image patch size.\",\n \"patch size. :param internalNumPatches: Internal number of sub-image-patches. :param dirOut: Directory where to\",\n \"or 1 X H x W image, numpy format pch_size: (int, int) tuple\",\n \"Image Noise Level Estimation\\\" (2015) :param imgPath: Path to the input image. :param\",\n \"// shift_factor) start_y = end_y - psize tileM = img[start_y:end_y, start_x:end_x] h_, w_\",\n \"3: im = im.transpose((2, 0, 1)) else: im = np.expand_dims(im, axis=0) # image\",\n \"saveResults: if dirOut is not None: imgName = imgPath.split(os.sep)[-1].split(\\\".\\\")[0] dirOut = os.path.join(dirOut) if\",\n \"format pch_size: (int, int) tuple or integer stride: (int, int) tuple or integer\",\n \"r\\\"../../../data/udacity/img/GT\\\" # dirOut = r\\\"../../../data/udacity/labels_noise_patchwise/PCA\\\" # imgFileEnding = \\\".jpg\\\" # for imgPath in\",\n \"cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img = img / 255.0 h, w = img.shape psize =\",\n \"= tileM.shape sigma = noise_estimate(tileM, internalNumPatches) * 255.0 estimatedNoiseMap[start_y :start_y + h_, start_x\",\n \"a int tuple!') C, H, W = im.shape num_H = len(range(0, H-pch_H+1, stride_H))\",\n \"patch_step) for start_x in rangex: for start_y in rangey: end_x = start_x +\",\n \"rangey = range(0, h, patch_step) for start_x in rangex: for start_y in rangey:\",\n \"<NAME> . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE\",\n \"patchSize, internalNumPatches, dirOut, saveResults=True): \\\"\\\"\\\" Estimates the standard deviation of (additive white gaussian)\",\n \"for start_x in rangex: for start_y in rangey: end_x = start_x + psize\",\n \"stride_H = stride_W = stride else: sys.exit('The input of stride must be a\",\n \"International Conference on Computer Vision (ICCV). IEEE Computer Society, 2015. Input: im: the\",\n \"imgName + \\\".npz\\\") if not os.path.exists(noiseMapPath): np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None\",\n \"min(min(patchSize, h), w) psize -= psize % 2 patch_step = psize shift_factor =\",\n \"W numpy tensor, range [0,1] pch_size: patch_size Output: noise_level: the estimated noise level\",\n \"mu = pch.mean(axis=1, keepdims=True) # d x 1 X = pch - mu\",\n \"import glob def im2patch(im, pch_size, stride=1): ''' Transform image to patches. Input: im:\",\n \"psize tileM = img[start_y:end_y, start_x:end_x] h_, w_ = tileM.shape sigma = noise_estimate(tileM, internalNumPatches)\"\n]","isConversational":false}}},{"rowIdx":2599,"cells":{"ngram":{"kind":"list like","value":["when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state","state. Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves","PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the",":func:`run()` method is called. When the application finishes the AppState changes to *FINISHED*.","is called in an unsuitable app state, an :class:`AppStateError` is called. The application","*RUNNING*. This can only be done from the *CREATED* state. \"\"\" self.run() self._start_time","or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the","= AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app state. Returns -------","work after the application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class","\"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod","This is checked via the :class:`Application` type specific :func:`is_finished()` method. The user can","self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\"","application finishes the AppState changes to *FINISHED*. This is checked via the :class:`Application`","\"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\" pass","is *RUNNING*, this method waits until the application is *FINISHED*. Parameters ---------- timeout",":func:`start()` method, the app state is set to *RUNNING* and the :class:`Application` type","until this value (in seconds) runs out. After this time is exceeded a","time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting.","state. Returns ------- app_state : AppState The current app state. \"\"\" if self._state","base class specify the respective kind of software and the way of interacting","Subclasses of this abstract base class specify the respective kind of software and","out. After this time is exceeded a :class:`TimeoutError` is raised and the application","the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod","be set for the application run. After the user calls the :func:`start()` method,","inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application has finished.","class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\" pass class VersionError(Exception):","timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application","until the application is *FINISHED*. Parameters ---------- timeout : float, optional If this","enum type represents the app states of an application. \"\"\" CREATED = auto()","func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a","set for the application run. After the user calls the :func:`start()` method, the","Get the current app state. Returns ------- app_state : AppState The current app","except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up()","the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific","AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in","its state to *JOINED*. This can only be done from the *RUNNING* or","self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self):","= AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when","pass def clean_up(self): \"\"\" Do clean up work after the application terminates. PROTECTED:","type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method.","leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too,","source code is part of the Biotite package and is distributed # under",":class:`Application` type specific :func:`is_finished()` method. The user can now call the :func:`join()` method,","time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the","The required app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if","*CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are no accessible","CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED","Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass","is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further","This will constantly check :func:`is_finished()` and will directly go into the *JOINED* state","application run. After the user calls the :func:`start()` method, the app state is","not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired its","different app states (instances of enum :class:`AppState`) from its creation until its termination:","def is_finished(self): \"\"\" Check if the application has finished. PROTECTED: Override when inheriting.","AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function` is called,","there are no accessible results. If a method is called in an unsuitable","self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED","interval : float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\"","class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an external piece of","will constantly check :func:`is_finished()` and will directly go into the *JOINED* state as","happens immediately, if otherwise the application is *RUNNING*, this method waits until the","to *RUNNING*. This can only be done from the *CREATED* state. \"\"\" self.run()","of :func:`start()` and :func:`join()` other Python code can be executed, while the application","AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence","Biotite package and is distributed # under the 3-Clause BSD License. Please see","\"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application has finished. PROTECTED:","of software and the way of interacting with it. Every :class:`Application` runs through","seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\"","set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When","Override when inheriting. Returns ------- finished : bool True of the application has","if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The","AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in *RUNNING* or *FINISHED* state.","thread: Between the call of :func:`start()` and :func:`join()` other Python code can be","that the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that",":class:`AppStateError` in case the method is called, when the :class:`Application` is not in","when inheriting. Returns ------- interval : float Time (in seconds) between calls of","from enum import Flag, auto class AppState(Flag): \"\"\" This enum type represents the","the application is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter","executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type","method. :func:`join()` can even be called in the *RUNNING* state: This will constantly","application runs in the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def","is *FINISHED* the joining process happens immediately, if otherwise the application is *RUNNING*,","accessible results. If a method is called in an unsuitable app state, an","override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle","CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses","not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def","be called in the *RUNNING* state: This will constantly check :func:`is_finished()` and will","the :func:`join()` method, concluding the application in the *JOINED* state and making the","specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state: This","except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED)","terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that","the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in the","First parameter of method is always 'self' instance = args[0] if not instance._state","method waits until the application is *FINISHED*. Parameters ---------- timeout : float, optional","done from the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING","from its creation until its termination: Directly after its instantiation the app is","no accessible results. If a method is called in an unsuitable app state,","its termination: Directly after its instantiation the app is in the *CREATED* state.","app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is","in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are","called. The application run behaves like an additional thread: Between the call of","application is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter is","this time is exceeded a :class:`TimeoutError` is raised and the application is cancelled.","AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError(","the application run and set its state to *JOINED*. This can only be","TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval())","\"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass","timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's version is","application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED =","__all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from","calls the :func:`start()` method, the app state is set to *RUNNING* and the","inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated.","termination: Directly after its instantiation the app is in the *CREATED* state. In","time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time >","in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in","software in any sense. Subclasses of this abstract base class specify the respective","timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\" )","the application is *FINISHED* the joining process happens immediately, if otherwise the application","try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state =","method while the application is *RUNNING* or *FINISHED* leaves the application in the","runs in the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self):",":class:`Application` only waits for finishing until this value (in seconds) runs out. After","waits for finishing until this value (in seconds) runs out. After this time","type specific :func:`is_finished()` method. The user can now call the :func:`join()` method, concluding","the call of :func:`start()` and :func:`join()` other Python code can be executed, while","def start(self): \"\"\" Start the application run and set its state to *RUNNING*.","wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in the joining process. PROTECTED:","self.cancel() raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\" ) else:","will directly go into the *JOINED* state as soon as the application reaches","is in the *CREATED* state. In this state further parameters can be set","This can only be done from the *RUNNING* or *FINISHED* state. If the","raise AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state} state is","Returns ------- app_state : AppState The current app state. \"\"\" if self._state ==","method, the app state is set to *RUNNING* and the :class:`Application` type specific","the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start","subclasses that raises an :class:`AppStateError` in case the method is called, when the","inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up work after the application","app is in the *CREATED* state. In this state further parameters can be","def clean_up(self): \"\"\" Do clean up work after the application terminates. PROTECTED: Optionally","called in the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly","*FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method. The user","after its instantiation the app is in the *CREATED* state. In this state","Python code can be executed, while the application runs in the background. \"\"\"","This can only be done from the *CREATED* state. \"\"\" self.run() self._start_time =","violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\"","for the application run. After the user calls the :func:`start()` method, the app","method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even","exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError","def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always","in any sense. Subclasses of this abstract base class specify the respective kind","`timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None","auto class AppState(Flag): \"\"\" This enum type represents the app states of an",":class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()`","of method is always 'self' instance = args[0] if not instance._state & app_state:","is part of the Biotite package and is distributed # under the 3-Clause","the application run and set its state to *RUNNING*. This can only be","called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ----------","*JOINED*. This can only be done from the *RUNNING* or *FINISHED* state. If","= \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"]","cancel(self): \"\"\" Cancel the application when in *RUNNING* or *FINISHED* state. \"\"\" self._state","other Python code can be executed, while the application runs in the background.","in case the method is called, when the :class:`Application` is not in the","current app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED","run(self): \"\"\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting.","AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run and","of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results.","it. Every :class:`Application` runs through a different app states (instances of enum :class:`AppState`)","and the application is cancelled. Raises ------ TimeoutError If the joining process exceeds","wraps from enum import Flag, auto class AppState(Flag): \"\"\" This enum type represents","can even be called in the *RUNNING* state: This will constantly check :func:`is_finished()`","the :class:`Application` type specific :func:`is_finished()` method. The user can now call the :func:`join()`","\"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app state.","def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting.","of the application has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\"","self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None):","enum :class:`AppState`) from its creation until its termination: Directly after its instantiation the","If a method is called in an unsuitable app state, an :class:`AppStateError` is","finishing until this value (in seconds) runs out. After this time is exceeded","required\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This","raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING |","function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter","\"requires_state\"] import abc import time from functools import wraps from enum import Flag,","that raises an :class:`AppStateError` in case the method is called, when the :class:`Application`","false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()`","float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod","Flag, auto class AppState(Flag): \"\"\" This enum type represents the app states of","through a different app states (instances of enum :class:`AppState`) from its creation until","set its state to *RUNNING*. This can only be done from the *CREATED*","state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not","the method is called, when the :class:`Application` is not in the specified :class:`AppState`","application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's version","executed, while the application runs in the background. \"\"\" def __init__(self): self._state =","app states of an application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED","by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application`","or *FINISHED* leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()`","self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application","Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def",":class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState","*JOINED* state as soon as the application reaches the *FINISHED* state. Calling the","\"\"\" Get the current app state. Returns ------- app_state : AppState The current","else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise","def join(self, timeout=None): \"\"\" Conclude the application run and set its state to",":func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can","inheriting. Returns ------- interval : float Time (in seconds) between calls of :func:`is_finished()`","its instantiation the app is in the *CREATED* state. In this state further","has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True of","__init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and","specify the respective kind of software and the way of interacting with it.",":func:`is_finished()` method. The user can now call the :func:`join()` method, concluding the application","*RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get","finishes the AppState changes to *FINISHED*. This is checked via the :class:`Application` type","auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED = auto()","auto() def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses that raises","additional thread: Between the call of :func:`start()` and :func:`join()` other Python code can","in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean","see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__","software and the way of interacting with it. Every :class:`Application` runs through a","the *JOINED* state and making the results of the application accessible by executing","---------- app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError` when","app state. Returns ------- app_state : AppState The current app state. \"\"\" if","application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def","\"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated. \"\"\"","*FINISHED* the joining process happens immediately, if otherwise the application is *RUNNING*, this","the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining process","auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\"","Raises ------ TimeoutError If the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval())","from the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining","\"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import","Parameters ---------- timeout : float, optional If this parameter is specified, the :class:`Application`","the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly go into",":class:`Application` runs through a different app states (instances of enum :class:`AppState`) from its","of runnable software in any sense. Subclasses of this abstract base class specify","return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run. Called in :func:`start()`.","\"\"\" pass def clean_up(self): \"\"\" Do clean up work after the application terminates.","results of the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method.","application when in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def","can only be done from the *CREATED* state. \"\"\" self.run() self._start_time = time.time()","\"\"\" Indicate that the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate","self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application","not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required","evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\"","be done from the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state =","can be set for the application run. After the user calls the :func:`start()`","AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and set its state","class AppState(Flag): \"\"\" This enum type represents the app states of an application.","\"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's version is invalid. \"\"\"","when `function` is called, if :class:`Application` is not in one of the specified","'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ =","the *CREATED* state. In this state further parameters can be set for the","This class is a wrapper around an external piece of runnable software in","application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application is","Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\"","in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app","auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application`",":func:`clean_up()` method, too, but there are no accessible results. If a method is","parameter is specified, the :class:`Application` only waits for finishing until this value (in","state further parameters can be set for the application run. After the user","self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app state. Returns","self.clean_up() def get_app_state(self): \"\"\" Get the current app state. Returns ------- app_state :","finished : bool True of the application has finished, false otherwise \"\"\" pass","\"\"\" Start the application run and set its state to *RUNNING*. This can",">>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func): @wraps(func)","f\"but {app_state} state is required\" ) return func(*args, **kwargs) return wrapper return decorator","process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout","class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated. \"\"\" pass class","runs through a different app states (instances of enum :class:`AppState`) from its creation","app states (instances of enum :class:`AppState`) from its creation until its termination: Directly","\"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is","state and making the results of the application accessible by executing the :class:`Application`","joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if","is exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises ------","specified, the :class:`Application` only waits for finishing until this value (in seconds) runs","is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter is specified,","auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for","timeout : float, optional If this parameter is specified, the :class:`Application` only waits","PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up work","be executed, while the application runs in the background. \"\"\" def __init__(self): self._state","runnable software in any sense. Subclasses of this abstract base class specify the","@requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run and set","\"AppState\", \"requires_state\"] import abc import time from functools import wraps from enum import","import Flag, auto class AppState(Flag): \"\"\" This enum type represents the app states","the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\"","app state is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method","an unsuitable app state, an :class:`AppStateError` is called. The application run behaves like","is a wrapper around an external piece of runnable software in any sense.","start(self): \"\"\" Start the application run and set its state to *RUNNING*. This","timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise","when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application has","Do clean up work after the application terminates. PROTECTED: Optionally override when inheriting.","state: This will constantly check :func:`is_finished()` and will directly go into the *JOINED*","as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the","def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in the joining process.","(instances of enum :class:`AppState`) from its creation until its termination: Directly after its","= time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude","------- app_state : AppState The current app state. \"\"\" if self._state == AppState.RUNNING:","When the application finishes the AppState changes to *FINISHED*. This is checked via","only waits for finishing until this value (in seconds) runs out. After this","in an unsuitable app state, an :class:`AppStateError` is called. The application run behaves","requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError`","the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING |","This source code is part of the Biotite package and is distributed #","abstract base class specify the respective kind of software and the way of","run and set its state to *JOINED*. This can only be done from","Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application","this method waits until the application is *FINISHED*. Parameters ---------- timeout : float,","state. In this state further parameters can be set for the application run.","method is called in an unsuitable app state, an :class:`AppStateError` is called. The","in the joining process. PROTECTED: Override when inheriting. Returns ------- interval : float","\"\"\" This class is a wrapper around an external piece of runnable software","PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the","The user can now call the :func:`join()` method, concluding the application in the","decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always 'self'","with it. Every :class:`Application` runs through a different app states (instances of enum","the AppState changes to *FINISHED*. This is checked via the :class:`Application` type specific","otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls","of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass","now call the :func:`join()` method, concluding the application in the *JOINED* state and","**kwargs): # First parameter of method is always 'self' instance = args[0] if","this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called","directly go into the *JOINED* state as soon as the application reaches the","state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app","making the results of the application accessible by executing the :class:`Application` type specific","in {instance.get_app_state()} state, \" f\"but {app_state} state is required\" ) return func(*args, **kwargs)","executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in","\"\"\" The time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override","pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\" pass class","time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else:","only be done from the *RUNNING* or *FINISHED* state. If the application is","@abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when","part of the Biotite package and is distributed # under the 3-Clause BSD","def wrapper(*args, **kwargs): # First parameter of method is always 'self' instance =","enum import Flag, auto class AppState(Flag): \"\"\" This enum type represents the app","clean_up(self): \"\"\" Do clean up work after the application terminates. PROTECTED: Optionally override","*FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED*","up work after the application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass","raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel","float, optional If this parameter is specified, the :class:`Application` only waits for finishing","A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case","\"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools import wraps from","as soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method","joining process. PROTECTED: Override when inheriting. Returns ------- interval : float Time (in","check :func:`is_finished()` and will directly go into the *JOINED* state as soon as","expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's version is invalid.","method is called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`.","for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method","from functools import wraps from enum import Flag, auto class AppState(Flag): \"\"\" This",":func:`join()` can even be called in the *RUNNING* state: This will constantly check","information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\",","and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for","optional If this parameter is specified, the :class:`Application` only waits for finishing until",":class:`Application` subclasses that raises an :class:`AppStateError` in case the method is called, when","states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func):","application is cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout`","app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return","the *JOINED* state as soon as the application reaches the *FINISHED* state. Calling","but there are no accessible results. If a method is called in an","time is exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises","in the *CREATED* state. In this state further parameters can be set for","def get_app_state(self): \"\"\" Get the current app state. Returns ------- app_state : AppState",":class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING*","self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and set","value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and","is called. The application run behaves like an additional thread: Between the call","the :class:`Application` type specific :func:`run()` method is called. When the application finishes the","method. The user can now call the :func:`join()` method, concluding the application in","external piece of runnable software in any sense. Subclasses of this abstract base","the application in the *JOINED* state and making the results of the application","application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there","the :class:`Application` only waits for finishing until this value (in seconds) runs out.","the :func:`clean_up()` method, too, but there are no accessible results. If a method","method, too, but there are no accessible results. If a method is called","in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check","state. This triggers the :func:`clean_up()` method, too, but there are no accessible results.","the application runs in the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED)","# This source code is part of the Biotite package and is distributed","value (in seconds) runs out. After this time is exceeded a :class:`TimeoutError` is","is raised and the application is cancelled. Raises ------ TimeoutError If the joining","the application run. After the user calls the :func:`start()` method, the app state","\"\"\" Cancel the application when in *RUNNING* or *FINISHED* state. \"\"\" self._state =","Override when inheriting. Returns ------- interval : float Time (in seconds) between calls","creation until its termination: Directly after its instantiation the app is in the","pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override","class specify the respective kind of software and the way of interacting with","application run and set its state to *RUNNING*. This can only be done","is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired","Conclude the application run and set its state to *JOINED*. This can only","way of interacting with it. Every :class:`Application` runs through a different app states","wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an","an application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED","the application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state.","The application run behaves like an additional thread: Between the call of :func:`start()`","pass class VersionError(Exception): \"\"\" Indicate that the application's version is invalid. \"\"\" pass","the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not","method is called. When the application finishes the AppState changes to *FINISHED*. This","*RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining process happens","get_app_state(self): \"\"\" Get the current app state. Returns ------- app_state : AppState The","the respective kind of software and the way of interacting with it. Every","under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__","state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def","run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self):","the application is cancelled. Raises ------ TimeoutError If the joining process exceeds the","\"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time","bool True of the application has finished, false otherwise \"\"\" pass @abc.abstractmethod def","------ TimeoutError If the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while","when inheriting. Returns ------- finished : bool True of the application has finished,","application is in {instance.get_app_state()} state, \" f\"but {app_state} state is required\" ) return","the Biotite package and is distributed # under the 3-Clause BSD License. Please","the application has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The","calls in the joining process. PROTECTED: Override when inheriting. Returns ------- interval :","go into the *JOINED* state as soon as the application reaches the *FINISHED*","decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an external piece","piece of runnable software in any sense. Subclasses of this abstract base class","__author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc","an external piece of runnable software in any sense. Subclasses of this abstract","state, \" f\"but {app_state} state is required\" ) return func(*args, **kwargs) return wrapper","concluding the application in the *JOINED* state and making the results of the","| AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in *RUNNING* or *FINISHED*","instance = args[0] if not instance._state & app_state: raise AppStateError( f\"The application is","the user calls the :func:`start()` method, the app state is set to *RUNNING*","results. If a method is called in an unsuitable app state, an :class:`AppStateError`","code is part of the Biotite package and is distributed # under the","interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns","AppState(Flag): \"\"\" This enum type represents the app states of an application. \"\"\"","\"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`. PROTECTED:","@requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in *RUNNING* or","into the *JOINED* state as soon as the application reaches the *FINISHED* state.","Between the call of :func:`start()` and :func:`join()` other Python code can be executed,",":func:`start()` and :func:`join()` other Python code can be executed, while the application runs","while the application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED*","AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs):","state to *JOINED*. This can only be done from the *RUNNING* or *FINISHED*","background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the","the joining process happens immediately, if otherwise the application is *RUNNING*, this method","is always 'self' instance = args[0] if not instance._state & app_state: raise AppStateError(","TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\"","states of an application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED =","args[0] if not instance._state & app_state: raise AppStateError( f\"The application is in {instance.get_app_state()}","Parameters ---------- app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError`",":func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns ------- interval","this parameter is specified, the :class:`Application` only waits for finishing until this value","kind of software and the way of interacting with it. Every :class:`Application` runs","too, but there are no accessible results. If a method is called in","is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The","application run behaves like an additional thread: Between the call of :func:`start()` and","of the Biotite package and is distributed # under the 3-Clause BSD License.","raised and the application is cancelled. Raises ------ TimeoutError If the joining process","Indicate that the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that","Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an external piece of runnable","the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the application's","the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the application","Returns ------- interval : float Time (in seconds) between calls of :func:`is_finished()` in","this state further parameters can be set for the application run. After the","accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the","soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while","the application is *RUNNING*, this method waits until the application is *FINISHED*. Parameters","an :class:`AppStateError` in case the method is called, when the :class:`Application` is not","= AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self):","... def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): #","the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ =","wrapper(*args, **kwargs): # First parameter of method is always 'self' instance = args[0]","application is *FINISHED* the joining process happens immediately, if otherwise the application is","@requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and set its state to","= auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED =","run. After the user calls the :func:`start()` method, the app state is set","seconds) runs out. After this time is exceeded a :class:`TimeoutError` is raised and","abc import time from functools import wraps from enum import Flag, auto class","the app states of an application. \"\"\" CREATED = auto() RUNNING = auto()","JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for methods","= [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools","*CREATED* state. In this state further parameters can be set for the application","is called. When the application finishes the AppState changes to *FINISHED*. This is","... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of","*FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current","for finishing until this value (in seconds) runs out. After this time is","has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval","pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in the","License. Please see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__ =","\" f\"but {app_state} state is required\" ) return func(*args, **kwargs) return wrapper return","the current app state. Returns ------- app_state : AppState The current app state.","joining process happens immediately, if otherwise the application is *RUNNING*, this method waits","the application finishes the AppState changes to *FINISHED*. This is checked via the","if not instance._state & app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state,","respective kind of software and the way of interacting with it. Every :class:`Application`","\"\"\" Conclude the application run and set its state to *JOINED*. This can","checked via the :class:`Application` type specific :func:`is_finished()` method. The user can now call",") return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class","= auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator for methods of",":class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state. Examples --------","inheriting. Returns ------- finished : bool True of the application has finished, false","sense. Subclasses of this abstract base class specify the respective kind of software","called in an unsuitable app state, an :class:`AppStateError` is called. The application run","\"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in","user calls the :func:`start()` method, the app state is set to *RUNNING* and","further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\",",":func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the application in","state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state","app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function`","finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True of the","is cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout` value.","a wrapper around an external piece of runnable software in any sense. Subclasses","and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout \"","join(self, timeout=None): \"\"\" Conclude the application run and set its state to *JOINED*.","\"\"\" A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in","constantly check :func:`is_finished()` and will directly go into the *JOINED* state as soon","\"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools import wraps from enum","finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time interval of","# information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\",","| AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run and set its","After the user calls the :func:`start()` method, the app state is set to",":func:`start()`. PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if","when in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self):","@abc.abstractmethod def wait_interval(self): \"\"\" The time interval of :func:`is_finished()` calls in the joining","application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self):","self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel()","application has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self): \"\"\" The time","represents the app states of an application. \"\"\" CREATED = auto() RUNNING =","(in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self):","that the application's timeout expired. \"\"\" pass class VersionError(Exception): \"\"\" Indicate that the","in the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly go","timeout=None): \"\"\" Conclude the application run and set its state to *JOINED*. This",":func:`join()` method, concluding the application in the *JOINED* state and making the results","\"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools import wraps","wrapper around an external piece of runnable software in any sense. Subclasses of","further parameters can be set for the application run. After the user calls","Please see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\"","this abstract base class specify the respective kind of software and the way","@requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def","# First parameter of method is always 'self' instance = args[0] if not","return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around an external","the app is in the *CREATED* state. In this state further parameters can","even be called in the *RUNNING* state: This will constantly check :func:`is_finished()` and","is called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters","if otherwise the application is *RUNNING*, this method waits until the application is","\" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except:","def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First","| AppState.FINISHED) ... def function(self): ... pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args,","immediately, if otherwise the application is *RUNNING*, this method waits until the application","f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state} state is required\" )","state is required\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta):","is specified, the :class:`Application` only waits for finishing until this value (in seconds)","exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is","AppState changes to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()`","__name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\",","\"\"\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED = auto()","application has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True","return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is","------- interval : float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()`","clean up work after the application terminates. PROTECTED: Optionally override when inheriting. \"\"\"","called. When the application finishes the AppState changes to *FINISHED*. This is checked","This triggers the :func:`clean_up()` method, too, but there are no accessible results. If","is in {instance.get_app_state()} state, \" f\"but {app_state} state is required\" ) return func(*args,","its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError:","# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information.","def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses that raises an","f\"The application expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try:","the application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\"","\"\"\" Do clean up work after the application terminates. PROTECTED: Optionally override when","raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval())","f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state","class is a wrapper around an external piece of runnable software in any","user can now call the :func:`join()` method, concluding the application in the *JOINED*","state to *RUNNING*. This can only be done from the *CREATED* state. \"\"\"","AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run and set its state",":class:`AppStateError` is called. The application run behaves like an additional thread: Between the","PROTECTED: Override when inheriting. Returns ------- finished : bool True of the application","{instance.get_app_state()} state, \" f\"but {app_state} state is required\" ) return func(*args, **kwargs) return","\"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time","state, an :class:`AppStateError` is called. The application run behaves like an additional thread:","from the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING","type represents the app states of an application. \"\"\" CREATED = auto() RUNNING","waits until the application is *FINISHED*. Parameters ---------- timeout : float, optional If","s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state =","specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \"\"\" def","to *JOINED*. This can only be done from the *RUNNING* or *FINISHED* state.","the application when in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up()","of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns -------","between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate","Check if the application has finished. PROTECTED: Override when inheriting. Returns ------- finished","import time from functools import wraps from enum import Flag, auto class AppState(Flag):","if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def","application in the *JOINED* state and making the results of the application accessible","\"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self,","*JOINED* state and making the results of the application accessible by executing the","Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up work after","pass \"\"\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method","instantiation the app is in the *CREATED* state. In this state further parameters","> timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f} s)\"","---------- timeout : float, optional If this parameter is specified, the :class:`Application` only","results. Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\"","functools import wraps from enum import Flag, auto class AppState(Flag): \"\"\" This enum",":class:`AppStateError` when `function` is called, if :class:`Application` is not in one of the","!= AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise","triggers the :func:`clean_up()` method, too, but there are no accessible results. If a","parameter of method is always 'self' instance = args[0] if not instance._state &","AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\"",": AppState The current app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished():","*FINISHED* state. If the application is *FINISHED* the joining process happens immediately, if","runs out. After this time is exceeded a :class:`TimeoutError` is raised and the","If the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() !=","the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this","If this parameter is specified, the :class:`Application` only waits for finishing until this","self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run.","always 'self' instance = args[0] if not instance._state & app_state: raise AppStateError( f\"The","decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the","process happens immediately, if otherwise the application is *RUNNING*, this method waits until","otherwise the application is *RUNNING*, this method waits until the application is *FINISHED*.","RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def","for further # information. __name__ = \"biotite.application\" __author__ = \"<NAME>\" __all__ = [\"Application\",","of interacting with it. Every :class:`Application` runs through a different app states (instances","time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state","raises an :class:`AppStateError` in case the method is called, when the :class:`Application` is","call the :func:`join()` method, concluding the application in the *JOINED* state and making","current app state. Returns ------- app_state : AppState The current app state. \"\"\"","is called, if :class:`Application` is not in one of the specified states: >>>","not instance._state & app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state, \"","and set its state to *JOINED*. This can only be done from the","in the background. \"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\"","and making the results of the application accessible by executing the :class:`Application` type","can now call the :func:`join()` method, concluding the application in the *JOINED* state","If the application is *FINISHED* the joining process happens immediately, if otherwise the","= AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application run","and :func:`join()` other Python code can be executed, while the application runs in","of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method is called,","state. If the application is *FINISHED* the joining process happens immediately, if otherwise","Start the application run and set its state to *RUNNING*. This can only","to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When the","The time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when","After this time is exceeded a :class:`TimeoutError` is raised and the application is","method, concluding the application in the *JOINED* state and making the results of","pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application has finished. PROTECTED: Override","this value (in seconds) runs out. After this time is exceeded a :class:`TimeoutError`","Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the","and will directly go into the *JOINED* state as soon as the application","def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run","was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout expired.","\"\"\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \"\"\"","Directly after its instantiation the app is in the *CREATED* state. In this","be done from the *RUNNING* or *FINISHED* state. If the application is *FINISHED*","3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\"","self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run. Called in :func:`start()`. PROTECTED:","a :class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError If","after the application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception):","application run and set its state to *JOINED*. This can only be done","`function` is called, if :class:`Application` is not in one of the specified states:","parameters can be set for the application run. After the user calls the","Called in :func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do","= AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application run and set its","application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's","app state, an :class:`AppStateError` is called. The application run behaves like an additional","if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the","& app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state}","@abc.abstractmethod def is_finished(self): \"\"\" Check if the application has finished. PROTECTED: Override when",": float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass","AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state} state is required\"","done from the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the","the app state is set to *RUNNING* and the :class:`Application` type specific :func:`run()`","*RUNNING* state: This will constantly check :func:`is_finished()` and will directly go into the","application terminates. PROTECTED: Optionally override when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate",") else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED","called, if :class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING","is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called.","the *FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING* or",":func:`join()`. PROTECTED: Override when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up",": float, optional If this parameter is specified, the :class:`Application` only waits for","time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout \" f\"({timeout:.1f}","case the method is called, when the :class:`Application` is not in the specified","specific :func:`run()` method is called. When the application finishes the AppState changes to","like an additional thread: Between the call of :func:`start()` and :func:`join()` other Python","if :class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING |","TimeoutError If the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state()","while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout:","behaves like an additional thread: Between the call of :func:`start()` and :func:`join()` other","*RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This triggers the","AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception):","Returns ------- finished : bool True of the application has finished, false otherwise","-------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in one","BSD License. Please see 'LICENSE.rst' for further # information. __name__ = \"biotite.application\" __author__","*CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED)","\"\"\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \"\"\" Start the application","self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def","= \"<NAME>\" __all__ = [\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import","distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further #","Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be","application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes","and set its state to *RUNNING*. This can only be done from the","is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This triggers","changes to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method.","state is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is","the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but",":func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called in :func:`join()`.","(in seconds) runs out. After this time is exceeded a :class:`TimeoutError` is raised","= auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state):","application expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate()","run and set its state to *RUNNING*. This can only be done from","if the application has finished. PROTECTED: Override when inheriting. Returns ------- finished :","\"\"\" Check if the application has finished. PROTECTED: Override when inheriting. Returns -------","Indicate that the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate","specific :func:`is_finished()` method. The user can now call the :func:`join()` method, concluding the","the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are no","the joining process. PROTECTED: Override when inheriting. Returns ------- interval : float Time","the joining process exceeds the `timeout` value. \"\"\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED:","expired its timeout \" f\"({timeout:.1f} s)\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except","AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\" Get the current app state. Returns ------- app_state","while the application runs in the background. \"\"\" def __init__(self): self._state = AppState.CREATED","the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the","Every :class:`Application` runs through a different app states (instances of enum :class:`AppState`) from","import wraps from enum import Flag, auto class AppState(Flag): \"\"\" This enum type","pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was violated. \"\"\" pass","None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\"The application expired its timeout","required app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application`","its creation until its termination: Directly after its instantiation the app is in","is checked via the :class:`Application` type specific :func:`is_finished()` method. The user can now","or *FINISHED* state. If the application is *FINISHED* the joining process happens immediately,","the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application",":func:`is_finished()` and will directly go into the *JOINED* state as soon as the","reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING*","\"\"\" Indicate that the application lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\"","*RUNNING*, this method waits until the application is *FINISHED*. Parameters ---------- timeout :",":class:`Application` type specific :func:`run()` method is called. When the application finishes the AppState","------- finished : bool True of the application has finished, false otherwise \"\"\"","one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ...","when inheriting. \"\"\" pass def clean_up(self): \"\"\" Do clean up work after the","its state to *RUNNING*. This can only be done from the *CREATED* state.","self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \"\"\" Conclude the application","specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state. Examples","to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method. The","[\"Application\", \"AppStateError\", \"TimeoutError\", \"VersionError\", \"AppState\", \"requires_state\"] import abc import time from functools import",":func:`join()` other Python code can be executed, while the application runs in the",":class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError If the","and the :class:`Application` type specific :func:`run()` method is called. When the application finishes","app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but {app_state} state","of the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore","a different app states (instances of enum :class:`AppState`) from its creation until its",": bool True of the application has finished, false otherwise \"\"\" pass @abc.abstractmethod","= auto() def requires_state(app_state): \"\"\" A decorator for methods of :class:`Application` subclasses that","an additional thread: Between the call of :func:`start()` and :func:`join()` other Python code","unsuitable app state, an :class:`AppStateError` is called. The application run behaves like an","a method is called in an unsuitable app state, an :class:`AppStateError` is called.","can be executed, while the application runs in the background. \"\"\" def __init__(self):",":class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED)","time from functools import wraps from enum import Flag, auto class AppState(Flag): \"\"\"",":func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state: This will","application is *RUNNING*, this method waits until the application is *FINISHED*. Parameters ----------","methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method is","{app_state} state is required\" ) return func(*args, **kwargs) return wrapper return decorator class","code can be executed, while the application runs in the background. \"\"\" def","application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This","self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the application when in *RUNNING*","call of :func:`start()` and :func:`join()` other Python code can be executed, while the","This enum type represents the app states of an application. \"\"\" CREATED =",":func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application results. Called","**kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper","*FINISHED*. Parameters ---------- timeout : float, optional If this parameter is specified, the","= args[0] if not instance._state & app_state: raise AppStateError( f\"The application is in","calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def evaluate(self): \"\"\" Evaluate application","process. PROTECTED: Override when inheriting. Returns ------- interval : float Time (in seconds)","is not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ...","Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in one of","is required\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\"","PROTECTED: Override when inheriting. Returns ------- interval : float Time (in seconds) between","Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \"\"\" pass @abc.abstractmethod def","AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run. Called in","specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()`","the way of interacting with it. Every :class:`Application` runs through a different app","interacting with it. Every :class:`Application` runs through a different app states (instances of","== AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\"","app_state : AppState The current app state. \"\"\" if self._state == AppState.RUNNING: if","= auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A decorator","any sense. Subclasses of this abstract base class specify the respective kind of","type specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state:","the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state :","the results of the application accessible by executing the :class:`Application` type specific :func:`evaluate()`","in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self):","of enum :class:`AppState`) from its creation until its termination: Directly after its instantiation","run behaves like an additional thread: Between the call of :func:`start()` and :func:`join()`","of an application. \"\"\" CREATED = auto() RUNNING = auto() FINISHED = auto()","Cancel the application when in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED","Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self): \"\"\" Check if the application","when inheriting. \"\"\" pass class AppStateError(Exception): \"\"\" Indicate that the application lifecycle was","are no accessible results. If a method is called in an unsuitable app","only be done from the *CREATED* state. \"\"\" self.run() self._start_time = time.time() self._state","def run(self): \"\"\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when","def cancel(self): \"\"\" Cancel the application when in *RUNNING* or *FINISHED* state. \"\"\"","\"\"\" This enum type represents the app states of an application. \"\"\" CREATED","= AppState.FINISHED return self._state @abc.abstractmethod def run(self): \"\"\" Commence the application run. Called","in the *JOINED* state and making the results of the application accessible by","import abc import time from functools import wraps from enum import Flag, auto","cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout` value. \"\"\"",": AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function` is","In this state further parameters can be set for the application run. After","'self' instance = args[0] if not instance._state & app_state: raise AppStateError( f\"The application","AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING","of this abstract base class specify the respective kind of software and the","the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state.","package and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst'","until its termination: Directly after its instantiation the app is in the *CREATED*","state as soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()`",":class:`AppState`) from its creation until its termination: Directly after its instantiation the app","via the :class:`Application` type specific :func:`is_finished()` method. The user can now call the","else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \"\"\" Cancel the","`app_state`. Parameters ---------- app_state : AppState The required app state. Examples -------- Raises","FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \"\"\" A","*RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When the application","and the way of interacting with it. Every :class:`Application` runs through a different","an :class:`AppStateError` is called. The application run behaves like an additional thread: Between","@abc.abstractmethod def run(self): \"\"\" Commence the application run. Called in :func:`start()`. PROTECTED: Override","around an external piece of runnable software in any sense. Subclasses of this","states (instances of enum :class:`AppState`) from its creation until its termination: Directly after","True of the application has finished, false otherwise \"\"\" pass @abc.abstractmethod def wait_interval(self):","AppState The current app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state","type specific :func:`run()` method is called. When the application finishes the AppState changes","*FINISHED* leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method,","in *RUNNING* or *FINISHED* state. \"\"\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \"\"\"","The current app state. \"\"\" if self._state == AppState.RUNNING: if self.is_finished(): self._state =","method is always 'self' instance = args[0] if not instance._state & app_state: raise","is_finished(self): \"\"\" Check if the application has finished. PROTECTED: Override when inheriting. Returns","@wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always 'self' instance","the application has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool","lifecycle was violated. \"\"\" pass class TimeoutError(Exception): \"\"\" Indicate that the application's timeout","can only be done from the *RUNNING* or *FINISHED* state. If the application","set its state to *JOINED*. This can only be done from the *RUNNING*","the :func:`start()` method, the app state is set to *RUNNING* and the :class:`Application`","instance._state & app_state: raise AppStateError( f\"The application is in {instance.get_app_state()} state, \" f\"but","return wrapper return decorator class Application(metaclass=abc.ABCMeta): \"\"\" This class is a wrapper around","Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in"],"string":"[\n \"when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state\",\n \"state. Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves\",\n \"PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the\",\n \":func:`run()` method is called. When the application finishes the AppState changes to *FINISHED*.\",\n \"is called in an unsuitable app state, an :class:`AppStateError` is called. The application\",\n \"*RUNNING*. This can only be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time\",\n \"or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the\",\n \"= AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns -------\",\n \"work after the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class\",\n \"\\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod\",\n \"This is checked via the :class:`Application` type specific :func:`is_finished()` method. The user can\",\n \"self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\"\",\n \"application finishes the AppState changes to *FINISHED*. This is checked via the :class:`Application`\",\n \"\\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass\",\n \"is *RUNNING*, this method waits until the application is *FINISHED*. Parameters ---------- timeout\",\n \":func:`start()` method, the app state is set to *RUNNING* and the :class:`Application` type\",\n \"until this value (in seconds) runs out. After this time is exceeded a\",\n \"time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting.\",\n \"state. Returns ------- app_state : AppState The current app state. \\\"\\\"\\\" if self._state\",\n \"base class specify the respective kind of software and the way of interacting\",\n \"Subclasses of this abstract base class specify the respective kind of software and\",\n \"out. After this time is exceeded a :class:`TimeoutError` is raised and the application\",\n \"the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod\",\n \"be set for the application run. After the user calls the :func:`start()` method,\",\n \"inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has finished.\",\n \"class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception):\",\n \"timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application\",\n \"until the application is *FINISHED*. Parameters ---------- timeout : float, optional If this\",\n \"enum type represents the app states of an application. \\\"\\\"\\\" CREATED = auto()\",\n \"func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a\",\n \"set for the application run. After the user calls the :func:`start()` method, the\",\n \"Get the current app state. Returns ------- app_state : AppState The current app\",\n \"except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up()\",\n \"the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific\",\n \"AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in\",\n \"its state to *JOINED*. This can only be done from the *RUNNING* or\",\n \"self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self):\",\n \"= AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when\",\n \"pass def clean_up(self): \\\"\\\"\\\" Do clean up work after the application terminates. PROTECTED:\",\n \"type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method.\",\n \"leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too,\",\n \"source code is part of the Biotite package and is distributed # under\",\n \":class:`Application` type specific :func:`is_finished()` method. The user can now call the :func:`join()` method,\",\n \"time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the\",\n \"The required app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if\",\n \"*CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are no accessible\",\n \"CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED\",\n \"Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass\",\n \"is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further\",\n \"This will constantly check :func:`is_finished()` and will directly go into the *JOINED* state\",\n \"application run. After the user calls the :func:`start()` method, the app state is\",\n \"not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired its\",\n \"different app states (instances of enum :class:`AppState`) from its creation until its termination:\",\n \"def is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED: Override when inheriting.\",\n \"AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function` is called,\",\n \"there are no accessible results. If a method is called in an unsuitable\",\n \"self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED\",\n \"interval : float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\"\",\n \"class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an external piece of\",\n \"will constantly check :func:`is_finished()` and will directly go into the *JOINED* state as\",\n \"happens immediately, if otherwise the application is *RUNNING*, this method waits until the\",\n \"to *RUNNING*. This can only be done from the *CREATED* state. \\\"\\\"\\\" self.run()\",\n \"of :func:`start()` and :func:`join()` other Python code can be executed, while the application\",\n \"AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence\",\n \"Biotite package and is distributed # under the 3-Clause BSD License. Please see\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED:\",\n \"of software and the way of interacting with it. Every :class:`Application` runs through\",\n \"seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\"\",\n \"set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When\",\n \"Override when inheriting. Returns ------- finished : bool True of the application has\",\n \"if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The\",\n \"AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED* state.\",\n \"thread: Between the call of :func:`start()` and :func:`join()` other Python code can be\",\n \"that the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that\",\n \":class:`AppStateError` in case the method is called, when the :class:`Application` is not in\",\n \"when inheriting. Returns ------- interval : float Time (in seconds) between calls of\",\n \"from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the\",\n \"the application is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter\",\n \"executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type\",\n \"method. :func:`join()` can even be called in the *RUNNING* state: This will constantly\",\n \"application runs in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def\",\n \"is *FINISHED* the joining process happens immediately, if otherwise the application is *RUNNING*,\",\n \"accessible results. If a method is called in an unsuitable app state, an\",\n \"override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle\",\n \"CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses\",\n \"not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def\",\n \"be called in the *RUNNING* state: This will constantly check :func:`is_finished()` and will\",\n \"the :func:`join()` method, concluding the application in the *JOINED* state and making the\",\n \"specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state: This\",\n \"except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that\",\n \"the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in the\",\n \"First parameter of method is always 'self' instance = args[0] if not instance._state\",\n \"method waits until the application is *FINISHED*. Parameters ---------- timeout : float, optional\",\n \"done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING\",\n \"from its creation until its termination: Directly after its instantiation the app is\",\n \"no accessible results. If a method is called in an unsuitable app state,\",\n \"its termination: Directly after its instantiation the app is in the *CREATED* state.\",\n \"app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is\",\n \"in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are\",\n \"called. The application run behaves like an additional thread: Between the call of\",\n \"application is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter is\",\n \"this time is exceeded a :class:`TimeoutError` is raised and the application is cancelled.\",\n \"AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError(\",\n \"the application run and set its state to *JOINED*. This can only be\",\n \"TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval())\",\n \"\\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass\",\n \"timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is\",\n \"application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED =\",\n \"__all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from\",\n \"calls the :func:`start()` method, the app state is set to *RUNNING* and the\",\n \"inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated.\",\n \"termination: Directly after its instantiation the app is in the *CREATED* state. In\",\n \"time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time >\",\n \"in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in\",\n \"software in any sense. Subclasses of this abstract base class specify the respective\",\n \"timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" )\",\n \"the application is *FINISHED* the joining process happens immediately, if otherwise the application\",\n \"try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state =\",\n \"method while the application is *RUNNING* or *FINISHED* leaves the application in the\",\n \"runs in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self):\",\n \":class:`Application` only waits for finishing until this value (in seconds) runs out. After\",\n \"waits for finishing until this value (in seconds) runs out. After this time\",\n \"type specific :func:`is_finished()` method. The user can now call the :func:`join()` method, concluding\",\n \"the call of :func:`start()` and :func:`join()` other Python code can be executed, while\",\n \"def start(self): \\\"\\\"\\\" Start the application run and set its state to *RUNNING*.\",\n \"wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the joining process. PROTECTED:\",\n \"self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else:\",\n \"will directly go into the *JOINED* state as soon as the application reaches\",\n \"is in the *CREATED* state. In this state further parameters can be set\",\n \"This can only be done from the *RUNNING* or *FINISHED* state. If the\",\n \"raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is\",\n \"Returns ------- app_state : AppState The current app state. \\\"\\\"\\\" if self._state ==\",\n \"method, the app state is set to *RUNNING* and the :class:`Application` type specific\",\n \"the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start\",\n \"subclasses that raises an :class:`AppStateError` in case the method is called, when the\",\n \"inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work after the application\",\n \"app is in the *CREATED* state. In this state further parameters can be\",\n \"def clean_up(self): \\\"\\\"\\\" Do clean up work after the application terminates. PROTECTED: Optionally\",\n \"called in the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly\",\n \"*FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method. The user\",\n \"after its instantiation the app is in the *CREATED* state. In this state\",\n \"Python code can be executed, while the application runs in the background. \\\"\\\"\\\"\",\n \"This can only be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time =\",\n \"violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\"\",\n \"for the application run. After the user calls the :func:`start()` method, the app\",\n \"method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even\",\n \"exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError\",\n \"def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always\",\n \"in any sense. Subclasses of this abstract base class specify the respective kind\",\n \"`timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None\",\n \"auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the app states of an\",\n \":class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()`\",\n \"of method is always 'self' instance = args[0] if not instance._state & app_state:\",\n \"is part of the Biotite package and is distributed # under the 3-Clause\",\n \"the application run and set its state to *RUNNING*. This can only be\",\n \"called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ----------\",\n \"*JOINED*. This can only be done from the *RUNNING* or *FINISHED* state. If\",\n \"= \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"]\",\n \"cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state\",\n \"other Python code can be executed, while the application runs in the background.\",\n \"in case the method is called, when the :class:`Application` is not in the\",\n \"current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED\",\n \"run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting.\",\n \"AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and\",\n \"of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results.\",\n \"it. Every :class:`Application` runs through a different app states (instances of enum :class:`AppState`)\",\n \"and the application is cancelled. Raises ------ TimeoutError If the joining process exceeds\",\n \"wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents\",\n \"can even be called in the *RUNNING* state: This will constantly check :func:`is_finished()`\",\n \"the :class:`Application` type specific :func:`is_finished()` method. The user can now call the :func:`join()`\",\n \"\\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state.\",\n \"def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting.\",\n \"of the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\"\",\n \"self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None):\",\n \"enum :class:`AppState`) from its creation until its termination: Directly after its instantiation the\",\n \"If a method is called in an unsuitable app state, an :class:`AppStateError` is\",\n \"finishing until this value (in seconds) runs out. After this time is exceeded\",\n \"required\\\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This\",\n \"raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING |\",\n \"function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter\",\n \"\\\"requires_state\\\"] import abc import time from functools import wraps from enum import Flag,\",\n \"that raises an :class:`AppStateError` in case the method is called, when the :class:`Application`\",\n \"false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()`\",\n \"float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod\",\n \"Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the app states of\",\n \"through a different app states (instances of enum :class:`AppState`) from its creation until\",\n \"set its state to *RUNNING*. This can only be done from the *CREATED*\",\n \"state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not\",\n \"the method is called, when the :class:`Application` is not in the specified :class:`AppState`\",\n \"application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version\",\n \"executed, while the application runs in the background. \\\"\\\"\\\" def __init__(self): self._state =\",\n \"app states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED\",\n \"by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the :class:`Application`\",\n \"or *FINISHED* leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()`\",\n \"self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application\",\n \"Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def\",\n \":class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState\",\n \"*JOINED* state as soon as the application reaches the *FINISHED* state. Calling the\",\n \"\\\"\\\"\\\" Get the current app state. Returns ------- app_state : AppState The current\",\n \"else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise\",\n \"def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set its state to\",\n \":func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can\",\n \"inheriting. Returns ------- interval : float Time (in seconds) between calls of :func:`is_finished()`\",\n \"its instantiation the app is in the *CREATED* state. In this state further\",\n \"has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True of\",\n \"__init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and\",\n \"specify the respective kind of software and the way of interacting with it.\",\n \":func:`is_finished()` method. The user can now call the :func:`join()` method, concluding the application\",\n \"*RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get\",\n \"finishes the AppState changes to *FINISHED*. This is checked via the :class:`Application` type\",\n \"auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED = auto()\",\n \"auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that raises\",\n \"additional thread: Between the call of :func:`start()` and :func:`join()` other Python code can\",\n \"in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean\",\n \"see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__\",\n \"software and the way of interacting with it. Every :class:`Application` runs through a\",\n \"the *JOINED* state and making the results of the application accessible by executing\",\n \"---------- app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError` when\",\n \"app state. Returns ------- app_state : AppState The current app state. \\\"\\\"\\\" if\",\n \"application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"\\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\"\",\n \"*FINISHED* the joining process happens immediately, if otherwise the application is *RUNNING*, this\",\n \"the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining process\",\n \"auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\"\",\n \"Raises ------ TimeoutError If the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval())\",\n \"from the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining\",\n \"\\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import\",\n \"Parameters ---------- timeout : float, optional If this parameter is specified, the :class:`Application`\",\n \"the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly go into\",\n \":class:`Application` runs through a different app states (instances of enum :class:`AppState`) from its\",\n \"of runnable software in any sense. Subclasses of this abstract base class specify\",\n \"return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`.\",\n \"\\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work after the application terminates.\",\n \"results of the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method.\",\n \"application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def\",\n \"can only be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time()\",\n \"\\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate\",\n \"self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application\",\n \"not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required\",\n \"evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\"\",\n \"be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state =\",\n \"can be set for the application run. After the user calls the :func:`start()`\",\n \"AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and set its state\",\n \"class AppState(Flag): \\\"\\\"\\\" This enum type represents the app states of an application.\",\n \"\\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is invalid. \\\"\\\"\\\"\",\n \"when `function` is called, if :class:`Application` is not in one of the specified\",\n \"'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ =\",\n \"the *CREATED* state. In this state further parameters can be set for the\",\n \"This class is a wrapper around an external piece of runnable software in\",\n \"application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application is\",\n \"Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\"\",\n \"in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app\",\n \"auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application`\",\n \":func:`clean_up()` method, too, but there are no accessible results. If a method is\",\n \"parameter is specified, the :class:`Application` only waits for finishing until this value (in\",\n \"state further parameters can be set for the application run. After the user\",\n \"self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns\",\n \"self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns ------- app_state :\",\n \"finished : bool True of the application has finished, false otherwise \\\"\\\"\\\" pass\",\n \"\\\"\\\"\\\" Start the application run and set its state to *RUNNING*. This can\",\n \">>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func)\",\n \"f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs) return wrapper return decorator\",\n \"process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout\",\n \"class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass class\",\n \"runs through a different app states (instances of enum :class:`AppState`) from its creation\",\n \"app states (instances of enum :class:`AppState`) from its creation until its termination: Directly\",\n \"\\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is\",\n \"state and making the results of the application accessible by executing the :class:`Application`\",\n \"joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if\",\n \"is exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises ------\",\n \"specified, the :class:`Application` only waits for finishing until this value (in seconds) runs\",\n \"is *FINISHED*. Parameters ---------- timeout : float, optional If this parameter is specified,\",\n \"auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for\",\n \"timeout : float, optional If this parameter is specified, the :class:`Application` only waits\",\n \"PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work\",\n \"be executed, while the application runs in the background. \\\"\\\"\\\" def __init__(self): self._state\",\n \"runnable software in any sense. Subclasses of this abstract base class specify the\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set\",\n \"\\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import wraps from enum import\",\n \"import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the app states\",\n \"the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\"\",\n \"app state is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method\",\n \"an unsuitable app state, an :class:`AppStateError` is called. The application run behaves like\",\n \"is a wrapper around an external piece of runnable software in any sense.\",\n \"start(self): \\\"\\\"\\\" Start the application run and set its state to *RUNNING*. This\",\n \"timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise\",\n \"when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has\",\n \"Do clean up work after the application terminates. PROTECTED: Optionally override when inheriting.\",\n \"state: This will constantly check :func:`is_finished()` and will directly go into the *JOINED*\",\n \"as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the\",\n \"def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the joining process.\",\n \"(instances of enum :class:`AppState`) from its creation until its termination: Directly after its\",\n \"= time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude\",\n \"------- app_state : AppState The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING:\",\n \"When the application finishes the AppState changes to *FINISHED*. This is checked via\",\n \"only waits for finishing until this value (in seconds) runs out. After this\",\n \"in an unsuitable app state, an :class:`AppStateError` is called. The application run behaves\",\n \"requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError`\",\n \"the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING |\",\n \"This source code is part of the Biotite package and is distributed #\",\n \"abstract base class specify the respective kind of software and the way of\",\n \"run and set its state to *JOINED*. This can only be done from\",\n \"Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application\",\n \"this method waits until the application is *FINISHED*. Parameters ---------- timeout : float,\",\n \"state. In this state further parameters can be set for the application run.\",\n \"method is called in an unsuitable app state, an :class:`AppStateError` is called. The\",\n \"in the joining process. PROTECTED: Override when inheriting. Returns ------- interval : float\",\n \"\\\"\\\"\\\" This class is a wrapper around an external piece of runnable software\",\n \"PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"The user can now call the :func:`join()` method, concluding the application in the\",\n \"decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always 'self'\",\n \"with it. Every :class:`Application` runs through a different app states (instances of enum\",\n \"the AppState changes to *FINISHED*. This is checked via the :class:`Application` type specific\",\n \"otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls\",\n \"of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass\",\n \"now call the :func:`join()` method, concluding the application in the *JOINED* state and\",\n \"**kwargs): # First parameter of method is always 'self' instance = args[0] if\",\n \"this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called\",\n \"directly go into the *JOINED* state as soon as the application reaches the\",\n \"state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app\",\n \"making the results of the application accessible by executing the :class:`Application` type specific\",\n \"in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs)\",\n \"executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in\",\n \"\\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override\",\n \"pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass class\",\n \"time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else:\",\n \"only be done from the *RUNNING* or *FINISHED* state. If the application is\",\n \"@abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override when\",\n \"part of the Biotite package and is distributed # under the 3-Clause BSD\",\n \"def wrapper(*args, **kwargs): # First parameter of method is always 'self' instance =\",\n \"enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type represents the app\",\n \"clean_up(self): \\\"\\\"\\\" Do clean up work after the application terminates. PROTECTED: Optionally override\",\n \"*FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED*\",\n \"up work after the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass\",\n \"raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel\",\n \"float, optional If this parameter is specified, the :class:`Application` only waits for finishing\",\n \"A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case\",\n \"\\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import wraps from\",\n \"as soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method\",\n \"joining process. PROTECTED: Override when inheriting. Returns ------- interval : float Time (in\",\n \"check :func:`is_finished()` and will directly go into the *JOINED* state as soon as\",\n \"expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is invalid.\",\n \"method is called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`.\",\n \"for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method\",\n \"from functools import wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This\",\n \":func:`join()` can even be called in the *RUNNING* state: This will constantly check\",\n \"information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\",\",\n \"and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for\",\n \"optional If this parameter is specified, the :class:`Application` only waits for finishing until\",\n \":class:`Application` subclasses that raises an :class:`AppStateError` in case the method is called, when\",\n \"states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func):\",\n \"application is cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout`\",\n \"app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return\",\n \"the *JOINED* state as soon as the application reaches the *FINISHED* state. Calling\",\n \"but there are no accessible results. If a method is called in an\",\n \"time is exceeded a :class:`TimeoutError` is raised and the application is cancelled. Raises\",\n \"in the *CREATED* state. In this state further parameters can be set for\",\n \"def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns ------- app_state : AppState\",\n \":class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING*\",\n \"self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and set\",\n \"value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and\",\n \"is called. The application run behaves like an additional thread: Between the call\",\n \"the :class:`Application` type specific :func:`run()` method is called. When the application finishes the\",\n \"method. The user can now call the :func:`join()` method, concluding the application in\",\n \"external piece of runnable software in any sense. Subclasses of this abstract base\",\n \"the application in the *JOINED* state and making the results of the application\",\n \"application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there\",\n \"the :class:`Application` only waits for finishing until this value (in seconds) runs out.\",\n \"the :func:`clean_up()` method, too, but there are no accessible results. If a method\",\n \"method, too, but there are no accessible results. If a method is called\",\n \"in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check\",\n \"state. This triggers the :func:`clean_up()` method, too, but there are no accessible results.\",\n \"the application runs in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED)\",\n \"# This source code is part of the Biotite package and is distributed\",\n \"value (in seconds) runs out. After this time is exceeded a :class:`TimeoutError` is\",\n \"is raised and the application is cancelled. Raises ------ TimeoutError If the joining\",\n \"the application run. After the user calls the :func:`start()` method, the app state\",\n \"\\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state =\",\n \"Override when inheriting. Returns ------- interval : float Time (in seconds) between calls\",\n \"creation until its termination: Directly after its instantiation the app is in the\",\n \"pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED: Override\",\n \"class specify the respective kind of software and the way of interacting with\",\n \"application run and set its state to *RUNNING*. This can only be done\",\n \"is not None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired\",\n \"Conclude the application run and set its state to *JOINED*. This can only\",\n \"way of interacting with it. Every :class:`Application` runs through a different app states\",\n \"wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an\",\n \"an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED\",\n \"the application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state.\",\n \"The application run behaves like an additional thread: Between the call of :func:`start()`\",\n \"pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's version is invalid. \\\"\\\"\\\" pass\",\n \"the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not\",\n \"method is called. When the application finishes the AppState changes to *FINISHED*. This\",\n \"*RUNNING* or *FINISHED* state. If the application is *FINISHED* the joining process happens\",\n \"get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns ------- app_state : AppState The\",\n \"the respective kind of software and the way of interacting with it. Every\",\n \"under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__\",\n \"state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def\",\n \"run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self):\",\n \"the application is cancelled. Raises ------ TimeoutError If the joining process exceeds the\",\n \"\\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time\",\n \"bool True of the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"------ TimeoutError If the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while\",\n \"when inheriting. Returns ------- finished : bool True of the application has finished,\",\n \"application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" ) return\",\n \"the Biotite package and is distributed # under the 3-Clause BSD License. Please\",\n \"the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The\",\n \"calls in the joining process. PROTECTED: Override when inheriting. Returns ------- interval :\",\n \"go into the *JOINED* state as soon as the application reaches the *FINISHED*\",\n \"decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an external piece\",\n \"piece of runnable software in any sense. Subclasses of this abstract base class\",\n \"__author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc\",\n \"an external piece of runnable software in any sense. Subclasses of this abstract\",\n \"state, \\\" f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs) return wrapper\",\n \"concluding the application in the *JOINED* state and making the results of the\",\n \"| AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED*\",\n \"instance = args[0] if not instance._state & app_state: raise AppStateError( f\\\"The application is\",\n \"the user calls the :func:`start()` method, the app state is set to *RUNNING*\",\n \"results. If a method is called in an unsuitable app state, an :class:`AppStateError`\",\n \"code is part of the Biotite package and is distributed # under the\",\n \"interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns\",\n \"AppState(Flag): \\\"\\\"\\\" This enum type represents the app states of an application. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`. PROTECTED:\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or\",\n \"into the *JOINED* state as soon as the application reaches the *FINISHED* state.\",\n \"Between the call of :func:`start()` and :func:`join()` other Python code can be executed,\",\n \":func:`start()` and :func:`join()` other Python code can be executed, while the application runs\",\n \"while the application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED*\",\n \"AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs):\",\n \"state to *JOINED*. This can only be done from the *RUNNING* or *FINISHED*\",\n \"background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the\",\n \"the joining process happens immediately, if otherwise the application is *RUNNING*, this method\",\n \"is always 'self' instance = args[0] if not instance._state & app_state: raise AppStateError(\",\n \"TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\"\",\n \"states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED =\",\n \"args[0] if not instance._state & app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()}\",\n \"Parameters ---------- app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError`\",\n \":func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns ------- interval\",\n \"this parameter is specified, the :class:`Application` only waits for finishing until this value\",\n \"kind of software and the way of interacting with it. Every :class:`Application` runs\",\n \"too, but there are no accessible results. If a method is called in\",\n \"is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The\",\n \"application run behaves like an additional thread: Between the call of :func:`start()` and\",\n \"of the Biotite package and is distributed # under the 3-Clause BSD License.\",\n \"raised and the application is cancelled. Raises ------ TimeoutError If the joining process\",\n \"Indicate that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that\",\n \"Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an external piece of runnable\",\n \"the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the application's\",\n \"the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the application\",\n \"Returns ------- interval : float Time (in seconds) between calls of :func:`is_finished()` in\",\n \"this state further parameters can be set for the application run. After the\",\n \"accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes the\",\n \"soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while\",\n \"the application is *RUNNING*, this method waits until the application is *FINISHED*. Parameters\",\n \"an :class:`AppStateError` in case the method is called, when the :class:`Application` is not\",\n \"= AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self):\",\n \"... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): #\",\n \"the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ =\",\n \"wrapper(*args, **kwargs): # First parameter of method is always 'self' instance = args[0]\",\n \"application is *FINISHED* the joining process happens immediately, if otherwise the application is\",\n \"@requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and set its state to\",\n \"= auto() RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED =\",\n \"run. After the user calls the :func:`start()` method, the app state is set\",\n \"seconds) runs out. After this time is exceeded a :class:`TimeoutError` is raised and\",\n \"abc import time from functools import wraps from enum import Flag, auto class\",\n \"the app states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto()\",\n \"JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods\",\n \"= [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools\",\n \"*CREATED* state. In this state further parameters can be set for the application\",\n \"is called. When the application finishes the AppState changes to *FINISHED*. This is\",\n \"... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of\",\n \"*FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current\",\n \"for finishing until this value (in seconds) runs out. After this time is\",\n \"has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval\",\n \"pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the\",\n \"License. Please see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__ =\",\n \"\\\" f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs) return wrapper return\",\n \"the current app state. Returns ------- app_state : AppState The current app state.\",\n \"joining process happens immediately, if otherwise the application is *RUNNING*, this method waits\",\n \"the application finishes the AppState changes to *FINISHED*. This is checked via the\",\n \"if not instance._state & app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state,\",\n \"respective kind of software and the way of interacting with it. Every :class:`Application`\",\n \"\\\"\\\"\\\" Conclude the application run and set its state to *JOINED*. This can\",\n \"checked via the :class:`Application` type specific :func:`is_finished()` method. The user can now call\",\n \") return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class\",\n \"= auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of\",\n \":class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state. Examples --------\",\n \"inheriting. Returns ------- finished : bool True of the application has finished, false\",\n \"sense. Subclasses of this abstract base class specify the respective kind of software\",\n \"called in an unsuitable app state, an :class:`AppStateError` is called. The application run\",\n \"\\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in\",\n \"user calls the :func:`start()` method, the app state is set to *RUNNING* and\",\n \"further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\",\",\n \":func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the application in\",\n \"state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state\",\n \"app_state : AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function`\",\n \"finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True of the\",\n \"is cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout` value.\",\n \"a wrapper around an external piece of runnable software in any sense. Subclasses\",\n \"and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\"\",\n \"join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set its state to *JOINED*.\",\n \"\\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in\",\n \"constantly check :func:`is_finished()` and will directly go into the *JOINED* state as soon\",\n \"\\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import wraps from enum\",\n \"finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of\",\n \"# information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\",\",\n \"| AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set its\",\n \"After the user calls the :func:`start()` method, the app state is set to\",\n \":func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if\",\n \"when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self):\",\n \"@abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time interval of :func:`is_finished()` calls in the joining\",\n \"application results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self):\",\n \"self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel()\",\n \"application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self): \\\"\\\"\\\" The time\",\n \"represents the app states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING =\",\n \"(in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self):\",\n \"that the application's timeout expired. \\\"\\\"\\\" pass class VersionError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"in the *RUNNING* state: This will constantly check :func:`is_finished()` and will directly go\",\n \"timeout=None): \\\"\\\"\\\" Conclude the application run and set its state to *JOINED*. This\",\n \":func:`join()` method, concluding the application in the *JOINED* state and making the results\",\n \"\\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import wraps\",\n \"wrapper around an external piece of runnable software in any sense. Subclasses of\",\n \"further parameters can be set for the application run. After the user calls\",\n \"Please see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\"\",\n \"this abstract base class specify the respective kind of software and the way\",\n \"@requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def\",\n \"# First parameter of method is always 'self' instance = args[0] if not\",\n \"return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around an external\",\n \"the app is in the *CREATED* state. In this state further parameters can\",\n \"even be called in the *RUNNING* state: This will constantly check :func:`is_finished()` and\",\n \"is called, when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters\",\n \"if otherwise the application is *RUNNING*, this method waits until the application is\",\n \"\\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except:\",\n \"def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First\",\n \"| AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args,\",\n \"immediately, if otherwise the application is *RUNNING*, this method waits until the application\",\n \"f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" )\",\n \"state is required\\\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta):\",\n \"is specified, the :class:`Application` only waits for finishing until this value (in seconds)\",\n \"exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is\",\n \"AppState changes to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()`\",\n \"__name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\",\",\n \"\\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED = auto() JOINED = auto()\",\n \"application has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool True\",\n \"return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is\",\n \"------- interval : float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()`\",\n \"clean up work after the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\"\",\n \"called. When the application finishes the AppState changes to *FINISHED*. This is checked\",\n \"This triggers the :func:`clean_up()` method, too, but there are no accessible results. If\",\n \"is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" ) return func(*args,\",\n \"its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError:\",\n \"# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further # information.\",\n \"def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that raises an\",\n \"f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try:\",\n \"the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" Do clean up work after the application terminates. PROTECTED: Optionally override when\",\n \"raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval())\",\n \"f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state\",\n \"class is a wrapper around an external piece of runnable software in any\",\n \"user can now call the :func:`join()` method, concluding the application in the *JOINED*\",\n \"state to *RUNNING*. This can only be done from the *CREATED* state. \\\"\\\"\\\"\",\n \"AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run and set its state\",\n \":class:`AppStateError` is called. The application run behaves like an additional thread: Between the\",\n \"PROTECTED: Override when inheriting. Returns ------- finished : bool True of the application\",\n \"{instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\" ) return func(*args, **kwargs) return\",\n \"\\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time\",\n \"state, an :class:`AppStateError` is called. The application run behaves like an additional thread:\",\n \"from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING\",\n \"type represents the app states of an application. \\\"\\\"\\\" CREATED = auto() RUNNING\",\n \"waits until the application is *FINISHED*. Parameters ---------- timeout : float, optional If\",\n \"s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state =\",\n \"specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ... pass \\\"\\\"\\\" def\",\n \"to *JOINED*. This can only be done from the *RUNNING* or *FINISHED* state.\",\n \"the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up()\",\n \"of :func:`is_finished()` calls in the joining process. PROTECTED: Override when inheriting. Returns -------\",\n \"between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate\",\n \"Check if the application has finished. PROTECTED: Override when inheriting. Returns ------- finished\",\n \"import time from functools import wraps from enum import Flag, auto class AppState(Flag):\",\n \"if self._state == AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def\",\n \"application in the *JOINED* state and making the results of the application accessible\",\n \"\\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self,\",\n \"*JOINED* state and making the results of the application accessible by executing the\",\n \"Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work after\",\n \"pass \\\"\\\"\\\" def decorator(func): @wraps(func) def wrapper(*args, **kwargs): # First parameter of method\",\n \"instantiation the app is in the *CREATED* state. In this state further parameters\",\n \"> timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\"\",\n \"---------- timeout : float, optional If this parameter is specified, the :class:`Application` only\",\n \"results. Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\"\",\n \"functools import wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum\",\n \":class:`AppStateError` when `function` is called, if :class:`Application` is not in one of the\",\n \"!= AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout: self.cancel() raise\",\n \"triggers the :func:`clean_up()` method, too, but there are no accessible results. If a\",\n \"parameter of method is always 'self' instance = args[0] if not instance._state &\",\n \"AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\"\",\n \": AppState The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished():\",\n \"*FINISHED* state. If the application is *FINISHED* the joining process happens immediately, if\",\n \"runs out. After this time is exceeded a :class:`TimeoutError` is raised and the\",\n \"If the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() !=\",\n \"the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this\",\n \"If this parameter is specified, the :class:`Application` only waits for finishing until this\",\n \"self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run.\",\n \"always 'self' instance = args[0] if not instance._state & app_state: raise AppStateError( f\\\"The\",\n \"decorator for methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the\",\n \"process happens immediately, if otherwise the application is *RUNNING*, this method waits until\",\n \"otherwise the application is *RUNNING*, this method waits until the application is *FINISHED*.\",\n \"RUNNING = auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def\",\n \"for further # information. __name__ = \\\"biotite.application\\\" __author__ = \\\"<NAME>\\\" __all__ = [\\\"Application\\\",\",\n \"of interacting with it. Every :class:`Application` runs through a different app states (instances\",\n \"time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state\",\n \"raises an :class:`AppStateError` in case the method is called, when the :class:`Application` is\",\n \"call the :func:`join()` method, concluding the application in the *JOINED* state and making\",\n \"current app state. Returns ------- app_state : AppState The current app state. \\\"\\\"\\\"\",\n \"is called, if :class:`Application` is not in one of the specified states: >>>\",\n \"not instance._state & app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\"\",\n \"and set its state to *JOINED*. This can only be done from the\",\n \"in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\"\",\n \"and making the results of the application accessible by executing the :class:`Application` type\",\n \"can now call the :func:`join()` method, concluding the application in the *JOINED* state\",\n \"If the application is *FINISHED* the joining process happens immediately, if otherwise the\",\n \"= AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application run\",\n \"and :func:`join()` other Python code can be executed, while the application runs in\",\n \"of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method is called,\",\n \"state. If the application is *FINISHED* the joining process happens immediately, if otherwise\",\n \"Start the application run and set its state to *RUNNING*. This can only\",\n \"to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When the\",\n \"The time interval of :func:`is_finished()` calls in the joining process. PROTECTED: Override when\",\n \"After this time is exceeded a :class:`TimeoutError` is raised and the application is\",\n \"method, concluding the application in the *JOINED* state and making the results of\",\n \"pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED: Override\",\n \"this value (in seconds) runs out. After this time is exceeded a :class:`TimeoutError`\",\n \"Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves the\",\n \"and will directly go into the *JOINED* state as soon as the application\",\n \"def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run\",\n \"was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout expired.\",\n \"\\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when inheriting. \\\"\\\"\\\"\",\n \"Directly after its instantiation the app is in the *CREATED* state. In this\",\n \"be done from the *RUNNING* or *FINISHED* state. If the application is *FINISHED*\",\n \"3-Clause BSD License. Please see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\"\",\n \"self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED:\",\n \"a :class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError If\",\n \"after the application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception):\",\n \"application run and set its state to *JOINED*. This can only be done\",\n \"`function` is called, if :class:`Application` is not in one of the specified states:\",\n \"parameters can be set for the application run. After the user calls the\",\n \"Called in :func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do\",\n \"= AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application run and set its\",\n \"application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's\",\n \"app state, an :class:`AppStateError` is called. The application run behaves like an additional\",\n \"if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the\",\n \"& app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state}\",\n \"@abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED: Override when\",\n \": float Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass\",\n \"AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state is required\\\"\",\n \"done from the *RUNNING* or *FINISHED* state. If the application is *FINISHED* the\",\n \"the app state is set to *RUNNING* and the :class:`Application` type specific :func:`run()`\",\n \"*RUNNING* state: This will constantly check :func:`is_finished()` and will directly go into the\",\n \"application terminates. PROTECTED: Optionally override when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate\",\n \") else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except AppStateError: raise except: self._state = AppState.CANCELLED\",\n \"called, if :class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING\",\n \"is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is called.\",\n \"the *FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING* or\",\n \":func:`join()`. PROTECTED: Override when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up\",\n \": float, optional If this parameter is specified, the :class:`Application` only waits for\",\n \"time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout \\\" f\\\"({timeout:.1f}\",\n \"case the method is called, when the :class:`Application` is not in the specified\",\n \"specific :func:`run()` method is called. When the application finishes the AppState changes to\",\n \"like an additional thread: Between the call of :func:`start()` and :func:`join()` other Python\",\n \"if :class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING |\",\n \"TimeoutError If the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state()\",\n \"while self.get_app_state() != AppState.FINISHED: if timeout is not None and time.time()-self._start_time > timeout:\",\n \"behaves like an additional thread: Between the call of :func:`start()` and :func:`join()` other\",\n \"*RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This triggers the\",\n \"AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception):\",\n \"Returns ------- finished : bool True of the application has finished, false otherwise\",\n \"-------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in one\",\n \"BSD License. Please see 'LICENSE.rst' for further # information. __name__ = \\\"biotite.application\\\" __author__\",\n \"*CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"\\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED @requires_state(AppState.CREATED) def start(self): \\\"\\\"\\\" Start the application\",\n \"self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def\",\n \"= \\\"<NAME>\\\" __all__ = [\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import\",\n \"distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst' for further #\",\n \"Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()` can even be\",\n \"application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore this executes\",\n \"and set its state to *RUNNING*. This can only be done from the\",\n \"is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This triggers\",\n \"changes to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method.\",\n \"state is set to *RUNNING* and the :class:`Application` type specific :func:`run()` method is\",\n \"the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but\",\n \":func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called in :func:`join()`.\",\n \"(in seconds) runs out. After this time is exceeded a :class:`TimeoutError` is raised\",\n \"= auto() FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state):\",\n \"application expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate()\",\n \"run and set its state to *RUNNING*. This can only be done from\",\n \"if the application has finished. PROTECTED: Override when inheriting. Returns ------- finished :\",\n \"\\\"\\\"\\\" Check if the application has finished. PROTECTED: Override when inheriting. Returns -------\",\n \"Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate\",\n \"specific :func:`is_finished()` method. The user can now call the :func:`join()` method, concluding the\",\n \"the *CANCELLED* state. This triggers the :func:`clean_up()` method, too, but there are no\",\n \"the joining process. PROTECTED: Override when inheriting. Returns ------- interval : float Time\",\n \"the joining process exceeds the `timeout` value. \\\"\\\"\\\" time.sleep(self.wait_interval()) while self.get_app_state() != AppState.FINISHED:\",\n \"expired its timeout \\\" f\\\"({timeout:.1f} s)\\\" ) else: time.sleep(self.wait_interval()) time.sleep(self.wait_interval()) try: self.evaluate() except\",\n \"AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\" Get the current app state. Returns ------- app_state\",\n \"while the application runs in the background. \\\"\\\"\\\" def __init__(self): self._state = AppState.CREATED\",\n \"the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the\",\n \"Every :class:`Application` runs through a different app states (instances of enum :class:`AppState`) from\",\n \"import wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\" This enum type\",\n \"pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass\",\n \"None and time.time()-self._start_time > timeout: self.cancel() raise TimeoutError( f\\\"The application expired its timeout\",\n \"required app state. Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application`\",\n \"its creation until its termination: Directly after its instantiation the app is in\",\n \"is checked via the :class:`Application` type specific :func:`is_finished()` method. The user can now\",\n \"or *FINISHED* state. If the application is *FINISHED* the joining process happens immediately,\",\n \"the application reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application\",\n \":func:`is_finished()` and will directly go into the *JOINED* state as soon as the\",\n \"reaches the *FINISHED* state. Calling the :func:`cancel()` method while the application is *RUNNING*\",\n \"\\\"\\\"\\\" Indicate that the application lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\"\",\n \"*RUNNING*, this method waits until the application is *FINISHED*. Parameters ---------- timeout :\",\n \":class:`Application` type specific :func:`run()` method is called. When the application finishes the AppState\",\n \"------- finished : bool True of the application has finished, false otherwise \\\"\\\"\\\"\",\n \"one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self): ...\",\n \"when inheriting. \\\"\\\"\\\" pass def clean_up(self): \\\"\\\"\\\" Do clean up work after the\",\n \"its state to *RUNNING*. This can only be done from the *CREATED* state.\",\n \"self._state = AppState.RUNNING @requires_state(AppState.RUNNING | AppState.FINISHED) def join(self, timeout=None): \\\"\\\"\\\" Conclude the application\",\n \"specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state. Examples\",\n \"to *FINISHED*. This is checked via the :class:`Application` type specific :func:`is_finished()` method. The\",\n \"[\\\"Application\\\", \\\"AppStateError\\\", \\\"TimeoutError\\\", \\\"VersionError\\\", \\\"AppState\\\", \\\"requires_state\\\"] import abc import time from functools import\",\n \":func:`join()` other Python code can be executed, while the application runs in the\",\n \":class:`TimeoutError` is raised and the application is cancelled. Raises ------ TimeoutError If the\",\n \"and the :class:`Application` type specific :func:`run()` method is called. When the application finishes\",\n \"app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but {app_state} state\",\n \"of the application accessible by executing the :class:`Application` type specific :func:`evaluate()` method. Furthermore\",\n \"a different app states (instances of enum :class:`AppState`) from its creation until its\",\n \": bool True of the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod\",\n \"= auto() def requires_state(app_state): \\\"\\\"\\\" A decorator for methods of :class:`Application` subclasses that\",\n \"an additional thread: Between the call of :func:`start()` and :func:`join()` other Python code\",\n \"unsuitable app state, an :class:`AppStateError` is called. The application run behaves like an\",\n \"a method is called in an unsuitable app state, an :class:`AppStateError` is called.\",\n \"can be executed, while the application runs in the background. \\\"\\\"\\\" def __init__(self):\",\n \":class:`Application` is not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED)\",\n \"time from functools import wraps from enum import Flag, auto class AppState(Flag): \\\"\\\"\\\"\",\n \":func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state: This will\",\n \"application is *RUNNING*, this method waits until the application is *FINISHED*. Parameters ----------\",\n \"methods of :class:`Application` subclasses that raises an :class:`AppStateError` in case the method is\",\n \"{app_state} state is required\\\" ) return func(*args, **kwargs) return wrapper return decorator class\",\n \"code can be executed, while the application runs in the background. \\\"\\\"\\\" def\",\n \"application is *RUNNING* or *FINISHED* leaves the application in the *CANCELLED* state. This\",\n \"self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING*\",\n \"call of :func:`start()` and :func:`join()` other Python code can be executed, while the\",\n \"This enum type represents the app states of an application. \\\"\\\"\\\" CREATED =\",\n \":func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application results. Called\",\n \"**kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper\",\n \"*FINISHED*. Parameters ---------- timeout : float, optional If this parameter is specified, the\",\n \"= args[0] if not instance._state & app_state: raise AppStateError( f\\\"The application is in\",\n \"calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def evaluate(self): \\\"\\\"\\\" Evaluate application\",\n \"process. PROTECTED: Override when inheriting. Returns ------- interval : float Time (in seconds)\",\n \"is not in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ...\",\n \"Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in one of\",\n \"is required\\\" ) return func(*args, **kwargs) return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\"\",\n \"PROTECTED: Override when inheriting. Returns ------- interval : float Time (in seconds) between\",\n \"Time (in seconds) between calls of :func:`is_finished()` in :func:`join()` \\\"\\\"\\\" pass @abc.abstractmethod def\",\n \"AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called in\",\n \"specific :func:`evaluate()` method. Furthermore this executes the :class:`Application` type specific :func:`clean_up()` method. :func:`join()`\",\n \"the way of interacting with it. Every :class:`Application` runs through a different app\",\n \"interacting with it. Every :class:`Application` runs through a different app states (instances of\",\n \"== AppState.RUNNING: if self.is_finished(): self._state = AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\"\",\n \"app_state : AppState The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if\",\n \"= auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A decorator\",\n \"any sense. Subclasses of this abstract base class specify the respective kind of\",\n \"type specific :func:`clean_up()` method. :func:`join()` can even be called in the *RUNNING* state:\",\n \"the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state :\",\n \"the results of the application accessible by executing the :class:`Application` type specific :func:`evaluate()`\",\n \"in one of the specified states: >>> @requires_state(AppState.RUNNING | AppState.FINISHED) ... def function(self):\",\n \"of enum :class:`AppState`) from its creation until its termination: Directly after its instantiation\",\n \"run behaves like an additional thread: Between the call of :func:`start()` and :func:`join()`\",\n \"of an application. \\\"\\\"\\\" CREATED = auto() RUNNING = auto() FINISHED = auto()\",\n \"Cancel the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED\",\n \"Override when inheriting. \\\"\\\"\\\" pass @abc.abstractmethod def is_finished(self): \\\"\\\"\\\" Check if the application\",\n \"when inheriting. \\\"\\\"\\\" pass class AppStateError(Exception): \\\"\\\"\\\" Indicate that the application lifecycle was\",\n \"are no accessible results. If a method is called in an unsuitable app\",\n \"only be done from the *CREATED* state. \\\"\\\"\\\" self.run() self._start_time = time.time() self._state\",\n \"def run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED: Override when\",\n \"def cancel(self): \\\"\\\"\\\" Cancel the application when in *RUNNING* or *FINISHED* state. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" This enum type represents the app states of an application. \\\"\\\"\\\" CREATED\",\n \"= AppState.FINISHED return self._state @abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called\",\n \"in the *JOINED* state and making the results of the application accessible by\",\n \"import abc import time from functools import wraps from enum import Flag, auto\",\n \"cancelled. Raises ------ TimeoutError If the joining process exceeds the `timeout` value. \\\"\\\"\\\"\",\n \": AppState The required app state. Examples -------- Raises :class:`AppStateError` when `function` is\",\n \"In this state further parameters can be set for the application run. After\",\n \"'self' instance = args[0] if not instance._state & app_state: raise AppStateError( f\\\"The application\",\n \"AppStateError: raise except: self._state = AppState.CANCELLED raise else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING\",\n \"of this abstract base class specify the respective kind of software and the\",\n \"the specified :class:`AppState` `app_state`. Parameters ---------- app_state : AppState The required app state.\",\n \"package and is distributed # under the 3-Clause BSD License. Please see 'LICENSE.rst'\",\n \"until its termination: Directly after its instantiation the app is in the *CREATED*\",\n \"state as soon as the application reaches the *FINISHED* state. Calling the :func:`cancel()`\",\n \":class:`AppState`) from its creation until its termination: Directly after its instantiation the app\",\n \"via the :class:`Application` type specific :func:`is_finished()` method. The user can now call the\",\n \"else: self._state = AppState.JOINED self.clean_up() @requires_state(AppState.RUNNING | AppState.FINISHED) def cancel(self): \\\"\\\"\\\" Cancel the\",\n \"`app_state`. Parameters ---------- app_state : AppState The required app state. Examples -------- Raises\",\n \"FINISHED = auto() JOINED = auto() CANCELLED = auto() def requires_state(app_state): \\\"\\\"\\\" A\",\n \"*RUNNING* and the :class:`Application` type specific :func:`run()` method is called. When the application\",\n \"and the way of interacting with it. Every :class:`Application` runs through a different\",\n \"an :class:`AppStateError` is called. The application run behaves like an additional thread: Between\",\n \"@abc.abstractmethod def run(self): \\\"\\\"\\\" Commence the application run. Called in :func:`start()`. PROTECTED: Override\",\n \"around an external piece of runnable software in any sense. Subclasses of this\",\n \"states (instances of enum :class:`AppState`) from its creation until its termination: Directly after\",\n \"True of the application has finished, false otherwise \\\"\\\"\\\" pass @abc.abstractmethod def wait_interval(self):\",\n \"AppState The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state\",\n \"type specific :func:`run()` method is called. When the application finishes the AppState changes\",\n \"*FINISHED* leaves the application in the *CANCELLED* state. This triggers the :func:`clean_up()` method,\",\n \"in *RUNNING* or *FINISHED* state. \\\"\\\"\\\" self._state = AppState.CANCELLED self.clean_up() def get_app_state(self): \\\"\\\"\\\"\",\n \"The current app state. \\\"\\\"\\\" if self._state == AppState.RUNNING: if self.is_finished(): self._state =\",\n \"method is always 'self' instance = args[0] if not instance._state & app_state: raise\",\n \"is_finished(self): \\\"\\\"\\\" Check if the application has finished. PROTECTED: Override when inheriting. Returns\",\n \"@wraps(func) def wrapper(*args, **kwargs): # First parameter of method is always 'self' instance\",\n \"the application has finished. PROTECTED: Override when inheriting. Returns ------- finished : bool\",\n \"lifecycle was violated. \\\"\\\"\\\" pass class TimeoutError(Exception): \\\"\\\"\\\" Indicate that the application's timeout\",\n \"can only be done from the *RUNNING* or *FINISHED* state. If the application\",\n \"set its state to *JOINED*. This can only be done from the *RUNNING*\",\n \"the :func:`start()` method, the app state is set to *RUNNING* and the :class:`Application`\",\n \"instance._state & app_state: raise AppStateError( f\\\"The application is in {instance.get_app_state()} state, \\\" f\\\"but\",\n \"return wrapper return decorator class Application(metaclass=abc.ABCMeta): \\\"\\\"\\\" This class is a wrapper around\",\n \"Examples -------- Raises :class:`AppStateError` when `function` is called, if :class:`Application` is not in\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":25,"numItemsPerPage":100,"numTotalItems":12866649,"offset":2500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzQ1NDUzNSwianRpIjoiMzY2YjNjOGMtNWI1My00NjM1LWI1MjctNTQ2OTQ5MTY3NThkIiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzQ1ODEzNSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.YwUeK7kOwfXmUIr93BEBPbn8rbWD0T5iToIjXF6nsFGz4dPo5ySSc8FTRQXlrxr5nguBoRZVjmkHkhfGZF6TCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"-*- coding: utf-8 -*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EMAIL>' __version__ = '0.1.0'",
"# -*- coding: utf-8 -*- __author__ = \"\"\"<NAME>\"\"\" __email__ = '<EMAIL>' __version__ ="
] |
[
"%s\" % path) with ZipFile(self._path, 'a', ZIP_DEFLATED, allowZip64=True) as zipfile: for base,dirs,files in",
"name): self.name = name _, self._path = mkstemp() logger.debug(\"Created temporary file %s\" %",
"to a temporary location (and using unnecessary extra space) and storing up the",
"ZIP file backup N... |
[
"serialize=False, verbose_name='投递ID')), ('delivery_status', models.CharField(choices=[('DD', '待定'), ('YQ', '邀请面试'), ('WJ', '婉拒')], default='DD', max_length=2, verbose_name='投递状态')), ],",
"verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('is_delete', models.Boolea... |
[
"8 hours 15 minutes\" assert date_time(\"17.12.1990 07:42\") == \"17 December 1990 year 7",
"months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\",",
"return f\"{int(time[0:2])} {months[int(time[3:5])-1]} {time[6:10]} year {h... |
[
"util.protocol_tuple(version_str) return util.version_string(ptuple) @cachedproperty def protocol_min(self): \"\"\"Minimum protocol version as a string, e.g.,",
"[] if isinstance(features, dict): hosts = features.get('hosts') if isinstance(hosts, dict): peers = [Peer(host,",
"License (MIT) # # Permission is her... |
[
"Platform from .test_gateway import DECONZ_CONFIG, setup_deconz_integration from tests.components.diagnostics import get_diagnostics_for_config_entry async def test_entry_diagnostics(",
"deCONZ diagnostics.\"\"\" from unittest.mock import patch from pydeconz.websocket import STATE_RUNNING from homeassistant.const... |
[
"idx: Array reference_position: Array did_buffer_overflow: Array max_occupancy: int = dataclasses.static_field() format: NeighborListFormat =",
"List @dataclasses.dataclass class CellList: \"\"\"Stores the spatial partition of a system into a",
"Unless required by applicable law or agreed to in writing, softwar... |
[
"10, \"seed\": seed} base_args.update(**EXPERIMENTS) commands = make_commands( SCRIPT, base_args=base_args, common_hyper_args={\"environment\": ENVIRONMENTS} ) runner.run_batch(commands)",
"\"RARL\", \"base-dir\": RARL_DIR}) runner = init_runner(\"EvaluateMassChange.\", num_threads=4) for seed in [0, 1, 2,",
"\... |
[
"sentry.api.bases.project import ProjectEndpoint from sentry.models import TagKey, TagKeyStatus class ProjectTagsEndpoint(ProjectEndpoint): def get(self, request,",
"class ProjectTagsEndpoint(ProjectEndpoint): def get(self, request, project): tag_keys = TagKey.objects.filter( project=project, status=TagKeyStatus.... |
[
"= asyncio.get_event_loop() @async_eel.expose async def py_random(): return random.random() async def print_num(n): \"\"\"callback of",
"from Javascript:', n) async def main(): try: async_eel.init('web') await async_eel.start('callbacks.html', size=(400, 300)) #",
"with an inline callback await async_eel.js_ran... |
[
"import index_driver_by_name, index_drivers from .index import Index _LOG = logging.getLogger(__name__) def index_connect(local_config=None, application_name=None,",
"immediately :rtype: datacube.index.index.Index :raises datacube.drivers.postgres._connections.IndexSetupError: \"\"\" if local_config is None: loca... |
[
"that can\\'t be parsed)') optparser.add_option('--filter-msgid', dest='filter_msgid', help='Only process message with given msgid') (opt,",
"optparser.add_option('-m', '--mbox', dest='mbox', help='Load all messages in mbox') optparser.add_option('-i', '--interactive', dest='interactive', action='store_true', hel... |
[
"from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration):",
"Generated by Django 1.11.29 on 2020-03-10 14:30 from __future__ import unicode_literals from django.db",
"[ ('shop', '0008_auto_20200310_1134'), ] operatio... |
[
"= random.randint(0, self.num_classes - 1) elif self.cfg.TRAIN.SAMPLER.WEIGHTED_SAMPLER.TYPE == \"square\": sample_class = np.random.choice(np.arange(self.num_classes), p=self.square_p)",
"__init__(self, mode='train', cfg=None, transform=None): super(iNaturalist, self).__init__(mode, cfg, transform) random.seed(0... |
[
"username = '<EMAIL>' title = 'good songs' conference = ConferenceFactory() body = 'dragon",
"order to access m2m fields, e.g. tags assert_true(created) assert_equal(fetched.fullname, fullname) assert_equal(fetched.username, username) assert_true('is_spam'",
"hmac.new( key=settings.MAILGUN_API_KEY, msg='{}{}'.f... |
[
"class Server(object): def __init__(self, handlers): self.handlers = handlers self.config = None self.servers =",
"self, \"host\": host, \"port\": port, \"handler\": handler}) def run(config, handlers): server = Server(handlers)",
"% kwargs) Handler(server, self.request) self.server = TCPServer((kwargs[\"host\"... |
[
"!= -1: fxName=\"\" OnOffblockSize = 0x30 for j in range(12): if data[OnOffstart +",
"1 except: pass #print(\"Found {} parameters.\".format(numParameters)) tD['Parameters'] = [] # 0 is the",
"(j) * OnOffblockSize + 0x1B] == 0x00 ): print(\"Empty next slot\") break \"\"\"",
"tD = { \"fxname\" :fxName } mmax = ... |
[
"= [chr(x) for x in range(ord('0'), ord('9') + 1)] source += \"\".join(lower_char) source",
"import random source = '' lower_char = [chr(x) for x in range(ord('a'), ord('z')",
"number_char = [chr(x) for x in range(ord('0'), ord('9') + 1)] source += \"\".join(lower_char)",
"= '' lower_char = [chr(x) for x in r... |
[
"= GBICListViewSet.as_view(actions={'get': 'retrieve'}) gbic_type_test = GBICType.objects.create(description='muitoruim') gbic_test = GBIC.objects.create( serial='showdomilhao', patrimony_number='777', gbic_type=gbic_type_test )",
"GBICTest(TestCase): def test_gbic_view_set(self): request = APIRequestFactory().ge... |
[
"\" if len(self._preparateurs) == 0: summary += \"-\" else: summary += \"\\n\\t+ \"",
"sieved_data[:, k+it*nf:k+(it+1)*nf] = sieve.transform( iterated_data[:, it, :], cache=self._cache, ) for callback in callbacks:",
"sums signature for different :class:`~fruits.words.word.Word` objects the user can specify. - ... |
[
"udev rule and systemd service file\\n\" f\"to your system to enable automated mounting",
"s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect((\"8.8.8.8\", 80)) ip_address = s.getsockname()[0] s.close() return ip_address def",
"variables={ 'address_plus_port': url, 'wkz_executable': wkz_executable, ... |
[
"dirs, config): QUALITY_CUTOFF = 5 to_trim = _get_sequences_to_trim(config) resources = config_utils.get_resources(\"AlienTrimmer\", config) try:",
"to avoid this? \"\"\" if quality_format == \"illumina\": quality_base = \"64\" else: quality_base",
"cores): \"\"\"Trimming with cutadapt, using version installed ... |
[
"== 'results.zip': filePath = os.path.join(folderName, filename) # Add file to zip zipObj.write(filePath, os.path.basename(filePath))",
"= '''please check if the docker is running, and if a container with",
"client = docker.from_env() try: container = client.containers.get(\"FEniCSDocker\") container.stop() ret... |
[
"'or <appname>:<author>:<version>' ) app_fs = fs_class( appname, author=author, version=version, create=create ) app_fs =",
"import OpenerError from ..subfs import ClosingSubFS from .. import appfs class AppFSOpener(Opener): \"\"\"``AppFS``",
"= parse_result.resource.partition('/') tokens = resource.split(':', ... |
[
"# Isolation Forest Hyper-parameters estimators = [200, 100] contaminations = [0.01] dimensions =",
"tnr, 'model': 'svm', 'auc': area, 'f_beta': fb, 'projection': 'RP'}, ignore_index=True) return svm_results def",
"n, 'TPR_train': tpr_train, 'TPR_test': tpr_test, 'TNR': tnr, 'model': 'svm', 'auc': area, 'f_beta... |
[
"res[\"response_code\"]) def test_message_provider(self): res = send_message(self.provider, self.provider_token, to=self.user, msg=\"foo test\") self.assertEqual(2201, res[\"response_code\"]) def",
"def setUp(self): self.provider = \"test_provider\" self.provider_banned = \"test_provider_msg_banned\" self.user = ... |
[
"= [x if x < 180. else x-360. for x in X] return",
"SphereGrid: XY = P.SphereGrid(Delta=Delta, Unwrap=Unwrap) else: XY = P.CartGrid(Dx=Delta, Dy=Delta, Bounds=Box) Win =",
".Selection as Sel import .Exploration as Exp import .CatUtils as CU #----------------------------------------------------------------------... |
[
"\"\"\" super().__init__(name=name, supported_entities=[supported_entity], supported_language=supported_language) self.terms_recognizer = TermsRecognizer(phrase_list) self.allowed_prepositions = allowed_prepositions if allowed_prepositions else",
"to the lexicon phrase itself). Empty list (which means preposition... |
[
"folder, command, test ): filepath = PathToTestFile( folder, test[ 'req' ][ 0 ]",
"'main.cpp', 4, 10 ), 'res': ( 'quote/b.hpp', 1, 1 ) }, { 'req':",
"{ 'fixits': [] } ) ) def FixIt_Check_cpp11_MultiFirst( results ): assert_that( results, has_entries(",
"27, 8 ), 'res': 'Cannot jump to location' }, ] for test ... |
[
"JSON file. Args: filepath: path of the JSON file. \"\"\" with open(filepath, \"r\")",
"scoring tool settings. \"\"\" def __init__(self): \"\"\"Initializes the Config instance. \"\"\" #Elasticsearch settings",
"config from a JSON file. Args: filepath: path of the JSON file. \"\"\"",
"self.sleep_not_idle_secs ... |
[
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"images_original[i]) ** 2) logger.info(\"MSE= {}\".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_invert) def test_inve... |
[
"pos, expected in [[(1, 1), [(0, 1), (2, 1), (1, 0), (1, 2)]],",
"1], [2, 2, 1, 1, 1], [1, 2, 1, 1, 1], [2, 2,",
"t, candies): collector = candy.CollectCandies(n, m, t, candies) for pos, expected in [[(1,",
"= candy.CollectCandies(n, m, t, candies) for pos, expected in [[(1, 1), [(0, 1),",
"1, 1, 1], [2, 2,... |
[
"song\" if not os.uname()[0].startswith(\"Darw\"): pygame.mixer.music.fadeout(1000) #fadeout current music over 1 sec. pygame.mixer.music.load(\"music/\" +",
"\"play song\" if not os.uname()[0].startswith(\"Darw\"): pygame.mixer.music.fadeout(1000) #fadeout current music over 1 sec. pygame.mixer.music.load(\"musi... |
[
"= 'query_items: Expecting FilterExpression parameter for table.scan() but non found' try: self.handler.table.scan.assert_called_once_with() #",
"\"name\": \"<NAME>\"} self.handler.update_item(key, data) self.handler.table.update_item.assert_called_once() _, kwargs = self.handler.table.update_item.call_args self.... |
[
"\"Montreal\", \"Montreal\"] }) fig = px.bar(df, x=\"Fruit\", y=\"Amount\", color=\"City\", barmode=\"group\") app.layout = html.Div(children=[",
"1, 2, 2, 4, 5], \"City\": [\"SF\", \"SF\", \"SF\", \"Montreal\", \"Montreal\", \"Montreal\"] })",
"for more options df = pd.DataFrame({ \"Fruit\": [\"Apples\", \"Ora... |
[
"failures.append( \"If any of %s is provided, all must be provided.\" % str(option_set)",
"technology submitted.\", ) # require `contact` when uploading a PEM file if formStash.results[\"account_key_file_pem\"]",
"the explicit-option privateKeySelection = _PrivateKeySelection() if private_key_option == \"privat... |
[
"delay in streams NBitsData = 32; if( dtval.get() == 'int16'): NBitsData = 16",
"Xilinx, Inc. # # Licensed under the Apache License, Version 2.0 (the \"License\");",
"KIND, either express or implied. # See the License for the specific language",
"in phases S = np.zeros((NPhases,int(NSamples1/NPhases),1+HasIma... |
[
"int print(type(item1_quantity)) # int print(type(item1_price_total)) # int # output: # <class 'str'> #",
"5 item1_price_total = item1_price * item1_quantity print(type(item1)) # str print(type(item1_price)) # int print(type(item1_quantity))",
"int # output: # <class 'str'> # <class 'int'> # <class 'int'> #",
... |
[] |
[
"from .paging import get_page, select_page, process_args from .results import serialize_bookmark, unserialize_bookmark, Page, Paging",
".paging import get_page, select_page, process_args from .results import serialize_bookmark, unserialize_bookmark, Page, Paging __all__",
".columns import OC from .paging import... |
[
"License. \"\"\"Simple model for image classification. The model is multiple conv/locally_connected/wide_conv/low_rank_locally_connected layers followed",
"KIND, either express or implied. # See the License for the specific language",
"= len(filters_list) self.pass_is_training_list = [] self.layers_list = [] if... |
[
"between two data rates # Best viewed in the Mu serial plotter where",
"# the data rate affects the resolution of the data while True: icm.gyro_data_rate_divisor",
"# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT import time import",
"= ICM20948(i2c) # Cycle betwe... |
[
"x in range(a-1): out = '*' + ' ' * x + '*'",
"* x + '*' + ' ' * x + '*' print(out.center(2*a-1)) a",
"* (a-x-2) + '*' + ' ' * (a-x-2) + '*' print(out.center(2*a-1)) print('*'",
"<gh_stars>0 a = int(input()) while a: for x in range(a-1): out = '*'",
"* a - 1)) for x in range(a-1): out = '*' + '",
"1)) for... |
[
"get_key_methods: List[Callable]): print(f\"getting sorted paths from {root_path}...\") sorted_paths = get_sorted_paths(path=root_path) print(f\"got {len(sorted_paths)} sorted",
"print(f\"got {len(related_path_details)} related paths\") print(\"building events...\") events = build_events_for_related_path_details(... |
[
"2019-09-27 14:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('schoolio',",
"Django 2.2.1 on 2019-09-27 14:23 from django.db import migrations, models class Migration(migrations.Migration): dependencies",
"[ migrations.AlterField( model_name='student_assessm... |
[
"<gh_stars>0 class Dataset: _data = None _first_text_col = 'text' _second_text_col = None _label_col",
"elif isinstance(item, slice): start = item.start if item.start else 0 stop = item.stop",
"item = self._data.iloc[item] if self._second_text_col: return item[self._first_text_col], item[self._second_text_col],... |
[
"status of the command Returns ------- Boolean bool \"\"\" # subprocess command for",
"Use the google scholar web URL and requests API to obtain the citations",
"morphed_identity.text) else: return True else: # return just the status return False except:",
"password req = TorRequest(password='<PASSWORD>') # r... |
[
"t y2 = y0 + radius y3 = y0 + height - radius",
"<URL: http://www.tinaja.com/bezarc1.pdf>.\"\"\" self._curves(pdfgeom.bezierArc(x1,y1, x2,y2, startAng, extent)) def arcTo(self, x1,y1, x2,y2, startAng=0, extent=90): \"\"\"Like",
"to piddlePDF by <NAME>, 28/7/99. Draw a partial ellipse inscribed within the rectan... |
[
"print text answer = wikipedia.summary(text,sentences=3) answer +=\"\\n\" print answer client.send_out(answer) #mic.say(answer) def isValid(text):",
"\"entering wiki term\" text = client.grab_input() while text.upper()==\"WIKIPEDIA\": print \"entering while\" text =",
"text answer = wikipedia.summary(text,sente... |
[
"his \" , random.choice(plural) , \".\") print(\"In his report, the inspector \" +",
"in range(2): a = input(\"Enter adjective : \") adjective.append(a) adverb = input(\"Enter adverb",
"\") print(\"An inspector from the Department of Health and \", random.choice(noun) , \"",
", random.choice(adjective) , \" s... |
[] |
[
"self.light, pe.create_string_item(INVALID_ITEM_NAME))) def testOnSwitchTurnedOn_withNonApplicableZone_returnsFalse(self): zone = Zone('ff', [self.light, self.motionSensor]) self.zm.add_zone(zone) self.assertFalse(self.zm.get_immutable_instance().on_switch_turned_on( pe.get_event_dispatcher(), self.light,",
"[sel... |
[
"= OrderGoods.objects.filter(order_id = order_id, is_commented=False) return skus # 保存评论 class SaveSkuComment(CreateAPIView): serializer_class =",
"orders.models import OrderInfo,OrderGoods from orders.utils import PageNum from rest_framework.filters import OrderingFilter # 展示订单信息",
"sku in skus: sku.count = ca... |
[
"log.info(f\"Starting player: {args.player}\") if player: player.open() except OSError as err: console.exit(f\"Failed to start",
"or args.force_progress) ) show_record_progress = ( hasattr(output, \"record\") and isinstance(output.record, FileOutput) and output.record.fd",
"# Linux / other else: os_version = pl... |
[
"False tagcount = 0 thrashcount = 0 for item in foundMails: for tag",
"new_list_new.append({\"totalsize\":totalsize}) logger.debug(new_list_new) #new_list_new.sort(key = lambda x : x[\"name\"]) return json.dumps(new_list_new, default=json_util.default) else: all_list",
"import StringIO import json from bson.dbr... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"50, 'monitoring_dataset': 'test_monitoring_dataset', 'monitoring_table': 'test_monitoring_table', 'monitoring_bq_conn_id': 'test_monitoring_conn', '... |
[
"def add_arguments(self, parser): parser.add_argument('file', nargs=1, type=str, help=\"The XML file to load\") parser.add_argument('type', nargs=1,",
"= None if options['delete']: collision_behavior = \"delete\" elif options['update']: collision_behavior = \"update\" else:",
"Load Report\\n\\tNew: {len(new_ids... |
[
"path='tests') self.assertFalse(is_there) for k in range(10): teststring = 'test%s' % k filename =",
"as g: g.write(teststring) self.assertEqual(recfile.open(filename, \"r\").read(), teststring) is_there = recfile.exists(filename) self.assertTrue(is_there) recfile.remove(filename) is_there =",
"g: g.write(tests... |
[
"= subprocess.Popen( [\"aws\", \"lambda\", \"list-functions\"], stdout=subprocess.PIPE ) output = res.communicate() function_dict.update(json.loads(output[0])) return function_dict['Functions']",
"subprocess import check_output, Popen, PIPE load_dotenv() # Accessing variables. CLIENT_ID = os.environ.get('CLIENT_I... |
[
"# -*- coding: utf-8 -*- ########################################################################### ## Python code generated with wxFormBuilder (version",
"wx.GBSpan( 1, 1 ), wx.ALL, 5 ) self.m_button03 = wx.Button( self, wx.ID_ANY, wx.EmptyString,",
"onButton13Click( self, event ): event.Skip() def onButton14... |
[
"module implements injectable Celery task. :copyright: (c) 2016-2020 by dry-python team. :license: BSD,",
"(c) 2016-2020 by dry-python team. :license: BSD, see LICENSE for more details. \"\"\"",
"--------------------------- This module implements injectable Celery task. :copyright: (c) 2016-2020 by dry-python t... |
[
"0][x[..., 0]<0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x>1]",
"image.resize((nw,nh), Image.BICUBIC) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image)/255.",
"hue) sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat) val = rand(1,",
"- crop_y... |
[
"logging import pathlib logging.basicConfig(level=logging.INFO) # Dirs ROOT_DIR = pathlib.Path(__file__).parent.absolute() DUMP_DIR = ROOT_DIR /",
"import pathlib logging.basicConfig(level=logging.INFO) # Dirs ROOT_DIR = pathlib.Path(__file__).parent.absolute() DUMP_DIR = ROOT_DIR / 'dumps'",
"<gh_stars>0 impor... |
[
"try: change = int(change) except ValueError: change = 0 else: change = 0",
"Download the rankings page for the requested year and create a PyQuery object.",
"for the final rankings or 'Preseason' for preseason rankings (str), 'previous': The team's",
"most recent rankings from the Associated Press where each... |
[
"'UPGO'], 'vtrace_error': ['hpc_rll.rl_utils.vtrace', 'VTrace'], } fn_str = fn_name_mapping[fn_name] cls = getattr(importlib.import_module(fn_str[0]), fn_str[1]) hpc_fn",
"namedtuple_data=False, include_args=[], include_kwargs=[], is_cls_method=False): def decorate(fn): @wraps(fn) def wrapper(*args, **kwargs): if... |
[
"#Return the count of int(s) in passed array. def number_of_occurrences(s, xs): return xs.count(s)"
] |
[
"boolTrue#997275b5 = Bool; 0x3fedd339, # true#3fedd339 = True; 0x1cb5c415, # vector#1cb5c415 {t:Type} #",
"' ').replace('>', '')\\ .replace('{', '').replace('}', '') representation = re.sub( r' \\w+:flags\\.\\d+\\?true', r'', representation",
"on the specified scheme.tl file.\"\"\" layer_regex = re.compile(r'^/... |
[
"arguments = [args], env = {\"RUNFILES_MANIFEST_FILE\": customresgen.files_to_run.runfiles_manifest.path}, mnemonic = \"CoreResxCompile\", input_manifests = resolve[1],",
"\"Target\" else [src] dotnet.actions.run( inputs = inputs + resolve[0].to_list(), tools = customresgen.default_runfiles.files, outputs",
"en... |
[
"file is not meant to be run directly, use:\\n\\n\" \"\\tpython test/test_jit.py TESTNAME\\n\\n\" \"instead.\")",
"sys from torch.testing._internal.jit_utils import JitTestCase # Make the helper files in test/ importable",
"has a __constants__ attribute defined # and intialized to a list. class Net(torch.nn.Lin... |
[
"import gym import pix_sample_arena import time import pybullet as p import pybullet_data import",
"import pybullet as p import pybullet_data import cv2 if __name__ == \"__main__\": env",
"__name__ == \"__main__\": env = gym.make(\"pix_sample_arena-v0\") x = 0 while True: p.stepSimulation() time.sleep(100)",
... |
[
"from datetime import time import pytest from i3_battery_block_vgg.timeparser import __parse_time_manually from i3_battery_block_vgg.timeparser import",
"] ) def test_manually_time_parsing(time_input: str, expected: time): assert __parse_time_manually(time_input) == expected, \"manual time",
"(\"12:13:14\", tim... |
[
"import annotations import mlir import pycde from pycde import (Input, Output, Parameter, module,",
"label=\"top\"; # CHECK: [shape=record,label=\"{hw.constant\\ni32\\n\\nvalue: 23 : i32}\"]; poly.print() # CHECK-LABEL: hw.module @top() ->",
"{parameters = {}} : (i32) -> i32 # CHECK: %example2.y_0 = hw.instance... |
[
"arc.Endpoint.COMPUTINGINFO) ] retriever2 = retrieve(uc, computing_elements) # Get all the ExecutionTargets on these",
"these ComputingServices targets2 = retriever2.GetExecutionTargets() sys.stdout.write(\"The discovered ExecutionTargets:\\n\") for target in targets2: sys.stdout.write(\"%s\\n\"%str(target))",
... |
[
"is_exploration_public(exploration_id): \"\"\"Returns whether exploration is public. Args: exploration_id: str. ID of the exploration.",
"import collection_services collection_services.update_collection_summary( activity_rights.id, None) def _update_activity_summary(activity_type, activity_rights): \"\"\"Updates ... |
[
"work around this, mypy allows the typing import to be behind a False-y",
"= [\"TYPE_CHECKING\", \"cast\"] # The TYPE_CHECKING constant defined by the typing module is",
"use - uses the `typing` module, which provides core functionality fundamental to mypy's",
"a curious maintainer can reach here to read this... |
[
"// 2)) # can_sends.append(create_lta_steer_command(self.packer, actuators.steeringAngleDeg, apply_steer_req, frame // 2)) # we can spam",
"== 0 and CS.CP.openpilotLongitudinalControl) or pcm_cancel_cmd: lead = lead or CS.out.vEgo < 12.",
"between read value and gas cmd. # This prevents unexpected pedal range r... |
[] |
[
"code_size = SelectField('Size', choices=[('15', 'Size'), ('5', '5'), ('10', '10'), ('15', '15'), ('20', '20'),",
"'Colour'), (\"white\", \"White\"), ('yellow', \"Yellow\"), ('lime', \"Green\"), (\"#ffa500\", \"Orange\")]) code_correction = SelectField('Error Correction',",
"import FlaskForm from wtforms import... |
[
"a stock price, and return the most recent (last) volatility estimate. Parameters ----------",
"pandas.Series A series of adjusted closing prices for a stock. l : float",
"the exponential moving average model. Making this value smaller will cause the model",
"exponential moving average model of the volatility... |
[
"q = map(int, sys.stdin.readline().split()) s = '$' + sys.stdin.readline().rstrip() lr = zip(*[map(int, sys.stdin.read().split())]",
"'C') & 1 prev = s[i] for l, r in lr: yield res[r]",
"= [None] * (n + 1); res[0] = 0 prev = '$' for",
"lr: yield res[r] - res[l] if __name__ == '__main__': ans = main() print(*a... |
[
"= torch.sqrt(torch.FloatTensor([embed_dim])).to(device) self.device = device def forward(self, trg, enc_src, trg_mask, src_mask): #trg =",
"src len] batch_size = trg.shape[0] trg_len = trg.shape[1] pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)",
"output = self.fc_out(trg) #o... |
[
"from salt.exceptions import NxosClientError log = logging.getLogger(__name__) __proxyenabled__ = [\"nxos\"] __virtualname__ = \"nxos\"",
"return False, err return __virtualname__ def system_information(proxy=None): if salt.utils.platform.is_proxy(): if proxy is None:",
"False, err return __virtualname__ def sy... |
[
"self.account = account self.id = device_id self.ipaddr = ipaddr self.mac = mac self.remote_custom_field",
"{}, provider: {}, account: {}\"\"\".format( self.id, self.ipaddr, self.mac, self.unique_id, self.remote_custom_field, self.comment, self.last_online, self.last_fw_ver,",
"unique_id: str = \"\") -> Optiona... |
[
"def _walled_grad(field, dx, walls): assert field.shape == walls.shape assert dx > 0.0 grad",
"Scalar.__init__(self, L, dim, dx, a_0=a_0) self.D = D self.dt = dt if self.D",
"dim, dx, walls, D, dt, a_0=0.0): Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0)",
"make_repr_str(self, fs) class WalledScalar(Sca... |
[
"variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ \"\"\" import",
"\"\"\" ASGI config for example_django project. It exposes the ASGI callable as a",
"information on this file, see https://docs.djangoproject.com/en/3.2/howto/depl... |
[
"function for combinations of discount factor and present bias values. The goal is",
"function. Given observed moments and weighting matrix in `OUT_ANALYSIS`, \"msm_estimation\", generate values of",
"{\"beta\": beta, \"delta\": delta, \"val\": val} results.append(result) return pd.DataFrame.from_dict(results) ... |
[
"import dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event):",
"dataclass from space_game.domain_names import KeyId from space_game.events.Event import Event @dataclass class KeyPressedEvent(Event): key_id:",
"from dataclasses imp... |
[
"= frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading =",
"supports = frozenset([TEAMS_SUPPORT]) def __init__(self, config): self.webhook = config['webhook'] def send(self, message): heading",
"logging from oncall.constants import TEAMS_SUPPO... |
[
"test/valid data. \"\"\" if data is None: raise ValueError(\"Input invalid data of None\")",
"save(self, checkpoint_file): \"\"\" Save the best model after HPO. :param checkpoint_file: The location",
"OR CONDITIONS OF ANY KIND, either exp' # ress or implied. # See",
"sync training results and checkpoints. It ... |
[
"= unconstrained_RQS( upper, W, H, D, inverse = True, tail_bound = self.B) log_det",
"@ self.w, 1) + self.b z = x + u * self.h(lin) phi",
"nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float), diagonal = 1)) self.W_inv = None def forward(self, x):",
"x, log_det class ActNorm(nn.Module): \"\"\" ActNorm... |
[
"s.pop(), 0 if isinstance(op, tuple): op, num_args = op if op == \"unary",
"# note: operands are pushed onto the stack in reverse order op2 =",
"return fn[op](*args) elif op[0].isalpha(): raise Exception(\"invalid identifier '%s'\" % op) else: return float(op)",
"else 1 if a > epsilon else 0, } exprStack = []... |
[
"|= flags if dport in scan.ports: return # Add weight for port if",
"scan.proto = proto self.scans[key] = scan def loop(self): pc = pcap.pcap() decode =",
"None does scan using RST, however this could be # return packets from",
"target host # If A does an idle scan on C with B",
"socket import time import t... |
[
"from itertools import combinations import copy def get_reverse(n): if n == 1: return",
"elif target2[0] == domains2[0][0]: closetd2 = get_closet_domain_to_target(target2, domains2) return closetd1, closetd2 def get_closest_target(domains,",
"visited[i]: visited[i] = True miggroup.append([i]) cnt += 1 for j in ... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"specific language governing permissions and # limitations under the License. \"\"\" Sparse Fine-tuning",
"task. Pointers for this are left as comm... |
[
"# Raid configuration in progress, checking status if not driver_internal_info.get('ilo_raid_create_in_progress'): ilo_object.create_raid_configuration(target_raid_config) self._prepare_for_read_raid(task, 'create_raid')",
"Unless required by applicable law or agreed to in writing, software # distributed",
"# R... |
[
") from ...base.events import ( # These are generally just bootstrap events. DisposeCompleteEvent,",
"...base.bus import ( EventBus, ListenerRegistrar, ListenerSetup, QueuePriority, ExtensionMetadataStruct, register_event, EVENT_WILDCARD, TARGET_WILDCARD, QUEUE_EVENT_NORMAL, QUEUE_EVENT_HIGH,",
") from ...base.... |
[
"\"<unknown device partition property %d>\") for v in info_value])) else: try: print(f\"{info_name}: {info_value}\")",
"== cl.device_info and info_name == \"PARTITION_TYPES_EXT\" and isinstance(info_value, list)): print(\"{}: {}\".format(info_name, [ cl.device_partition_property_ext.to_string(v,",
"= result.rep... |
[
"and the following disclaimer in the documentation # and/or other materials provided with",
"render_template(in_file, out_file, template_dir, json_path) # terminal nonlinear constraints if acados_ocp.constraints.constr_type_e == 'BGH' and",
"ny: raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' ... |
[
"sys.exit('The input of pch_size must be a integer or a int tuple!') if",
"np.savez_compressed(noiseMapPath, estimatedNoiseMap) return estimatedNoiseMap except: return None # Example # if __name__ ==",
"for start_y in rangey: end_x = start_x + psize end_y = start_y +",
"- psize tileM = img[start_y:end_y, star... |
[
"when the :class:`Application` is not in the specified :class:`AppState` `app_state`. Parameters ---------- app_state",
"state. Calling the :func:`cancel()` method while the application is *RUNNING* or *FINISHED* leaves",
"PROTECTED: Override when inheriting. \"\"\" pass @abc.abstractmethod def is_finished(self... |