modelutils

This module provides utilities to build Cython models based on Python models automatically.

Most model developers do not need to be aware of the features implemented in module modelutils, except that they need to initialise class Cythonizer within the main modules of their base and application models (see, for example, the source code of base model hland and application model hland_v1).

However, when implementing models with functionalities not envisaged so far, problems might arise. Please contact the HydPy developer team then, preferably by opening an issue on GitHub. Potentially, problems could occur when defining parameters or sequences with larger dimensionality than anticipated. The following example shows the Cython code lines for the get_point_states() method of class ELSModel, used for deriving the test model. By now, we did only implement 0-dimensional and 1-dimensional sequences requiring this method. After hackishly changing the dimensionality of sequences S, we still seem to get plausible results, but these are untested in model applications:

>>> from hydpy.models.test import cythonizer
>>> pyxwriter = cythonizer.pyxwriter
>>> pyxwriter.get_point_states
            . get_point_states
    cpdef inline void get_point_states(self) nogil:
        cdef int idx0
        self.sequences.states.s = self.sequences.states._s_points[self.numvars.idx_stage]
        for idx0 in range(self.sequences.states._sv_length):
            self.sequences.states.sv[idx0] = self.sequences.states._sv_points[self.numvars.idx_stage][idx0]

>>> pyxwriter.model.sequences.states.s.NDIM = 2
>>> pyxwriter.get_point_states
            . get_point_states
    cpdef inline void get_point_states(self) nogil:
        cdef int idx0, idx1
        for idx0 in range(self.sequences.states._s_length0):
            for idx1 in range(self.sequences.states._s_length1):
                self.sequences.states.s[idx0, idx1] = self.sequences.states._s_points[self.numvars.idx_stage][idx0, idx1]
        for idx0 in range(self.sequences.states._sv_length):
            self.sequences.states.sv[idx0] = self.sequences.states._sv_points[self.numvars.idx_stage][idx0]

>>> pyxwriter.model.sequences.states.s.NDIM = 3
>>> pyxwriter.get_point_states
Traceback (most recent call last):
...
NotImplementedError: NDIM of sequence `s` is higher than expected.

The following examples show the results for some methods which are also related to numerical integration but deal with FluxSequence objects. We start with the method integrate_fluxes():

>>> pyxwriter.integrate_fluxes
            . integrate_fluxes
    cpdef inline void integrate_fluxes(self) nogil:
        cdef int jdx, idx0
        self.sequences.fluxes.q = 0.
        for jdx in range(self.numvars.idx_method):
            self.sequences.fluxes.q = self.sequences.fluxes.q +self.numvars.dt * self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, jdx]*self.sequences.fluxes._q_points[jdx]
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes.qv[idx0] = 0.
            for jdx in range(self.numvars.idx_method):
                self.sequences.fluxes.qv[idx0] = self.sequences.fluxes.qv[idx0] + self.numvars.dt * self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, jdx]*self.sequences.fluxes._qv_points[jdx, idx0]

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 2
>>> pyxwriter.integrate_fluxes
            . integrate_fluxes
    cpdef inline void integrate_fluxes(self) nogil:
        cdef int jdx, idx0, idx1
        for idx0 in range(self.sequences.fluxes._q_length0):
            for idx1 in range(self.sequences.fluxes._q_length1):
                self.sequences.fluxes.q[idx0, idx1] = 0.
                for jdx in range(self.numvars.idx_method):
                    self.sequences.fluxes.q[idx0, idx1] = self.sequences.fluxes.q[idx0, idx1] + self.numvars.dt * self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, jdx]*self.sequences.fluxes._q_points[jdx, idx0, idx1]
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes.qv[idx0] = 0.
            for jdx in range(self.numvars.idx_method):
                self.sequences.fluxes.qv[idx0] = self.sequences.fluxes.qv[idx0] + self.numvars.dt * self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, jdx]*self.sequences.fluxes._qv_points[jdx, idx0]

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 3
>>> pyxwriter.integrate_fluxes
Traceback (most recent call last):
...
NotImplementedError: NDIM of sequence `q` is higher than expected.

Method reset_sum_fluxes():

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 0
>>> pyxwriter.reset_sum_fluxes
            . reset_sum_fluxes
    cpdef inline void reset_sum_fluxes(self) nogil:
        cdef int idx0
        self.sequences.fluxes._q_sum = 0.
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes._qv_sum[idx0] = 0.

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 2
>>> pyxwriter.reset_sum_fluxes
            . reset_sum_fluxes
    cpdef inline void reset_sum_fluxes(self) nogil:
        cdef int idx0, idx1
        for idx0 in range(self.sequences.fluxes._q_length0):
            for idx1 in range(self.sequences.fluxes._q_length1):
                self.sequences.fluxes._q_sum[idx0, idx1] = 0.
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes._qv_sum[idx0] = 0.

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 3
>>> pyxwriter.reset_sum_fluxes
Traceback (most recent call last):
...
NotImplementedError: NDIM of sequence `q` is higher than expected.

Method addup_fluxes():

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 0
>>> pyxwriter.addup_fluxes
            . addup_fluxes
    cpdef inline void addup_fluxes(self) nogil:
        cdef int idx0
        self.sequences.fluxes._q_sum = self.sequences.fluxes._q_sum + self.sequences.fluxes.q
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes._qv_sum[idx0] = self.sequences.fluxes._qv_sum[idx0] + self.sequences.fluxes.qv[idx0]

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 2
>>> pyxwriter.addup_fluxes
            . addup_fluxes
    cpdef inline void addup_fluxes(self) nogil:
        cdef int idx0, idx1
        for idx0 in range(self.sequences.fluxes._q_length0):
            for idx1 in range(self.sequences.fluxes._q_length1):
                self.sequences.fluxes._q_sum[idx0, idx1] = self.sequences.fluxes._q_sum[idx0, idx1] + self.sequences.fluxes.q[idx0, idx1]
        for idx0 in range(self.sequences.fluxes._qv_length):
            self.sequences.fluxes._qv_sum[idx0] = self.sequences.fluxes._qv_sum[idx0] + self.sequences.fluxes.qv[idx0]

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 3
>>> pyxwriter.addup_fluxes
Traceback (most recent call last):
...
NotImplementedError: NDIM of sequence `q` is higher than expected.

Method calculate_error():

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 0
>>> pyxwriter.calculate_error
            . calculate_error
    cpdef inline void calculate_error(self) nogil:
        cdef int idx0
        cdef double abserror
        self.numvars.abserror = 0.
        if self.numvars.use_relerror:
            self.numvars.relerror = 0.
        else:
            self.numvars.relerror = inf
        abserror = fabs(self.sequences.fluxes._q_results[self.numvars.idx_method]-self.sequences.fluxes._q_results[self.numvars.idx_method-1])
        self.numvars.abserror = max(self.numvars.abserror, abserror)
        if self.numvars.use_relerror:
            if self.sequences.fluxes._q_results[self.numvars.idx_method] == 0.:
                self.numvars.relerror = inf
            else:
                self.numvars.relerror = max(self.numvars.relerror, fabs(abserror/self.sequences.fluxes._q_results[self.numvars.idx_method]))
        for idx0 in range(self.sequences.fluxes._qv_length):
            abserror = fabs(self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0]-self.sequences.fluxes._qv_results[self.numvars.idx_method-1, idx0])
            self.numvars.abserror = max(self.numvars.abserror, abserror)
            if self.numvars.use_relerror:
                if self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0] == 0.:
                    self.numvars.relerror = inf
                else:
                    self.numvars.relerror = max(self.numvars.relerror, fabs(abserror/self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0]))

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 2
>>> pyxwriter.calculate_error
            . calculate_error
    cpdef inline void calculate_error(self) nogil:
        cdef int idx0, idx1
        cdef double abserror
        self.numvars.abserror = 0.
        if self.numvars.use_relerror:
            self.numvars.relerror = 0.
        else:
            self.numvars.relerror = inf
        for idx0 in range(self.sequences.fluxes._q_length0):
            for idx1 in range(self.sequences.fluxes._q_length1):
                abserror = fabs(self.sequences.fluxes._q_results[self.numvars.idx_method, idx0, idx1]-self.sequences.fluxes._q_results[self.numvars.idx_method-1, idx0, idx1])
                self.numvars.abserror = max(self.numvars.abserror, abserror)
                if self.numvars.use_relerror:
                    if self.sequences.fluxes._q_results[self.numvars.idx_method, idx0, idx1] == 0.:
                        self.numvars.relerror = inf
                    else:
                        self.numvars.relerror = max(self.numvars.relerror, fabs(abserror/self.sequences.fluxes._q_results[self.numvars.idx_method, idx0, idx1]))
        for idx0 in range(self.sequences.fluxes._qv_length):
            abserror = fabs(self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0]-self.sequences.fluxes._qv_results[self.numvars.idx_method-1, idx0])
            self.numvars.abserror = max(self.numvars.abserror, abserror)
            if self.numvars.use_relerror:
                if self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0] == 0.:
                    self.numvars.relerror = inf
                else:
                    self.numvars.relerror = max(self.numvars.relerror, fabs(abserror/self.sequences.fluxes._qv_results[self.numvars.idx_method, idx0]))

>>> pyxwriter.model.sequences.fluxes.q.NDIM = 3
>>> pyxwriter.calculate_error
Traceback (most recent call last):
...
NotImplementedError: NDIM of sequence `q` is higher than expected.

Module modelutils implements the following members:

  • get_dllextension() Return the DLL file extension for the current operating system.

  • Lines Handles code lines for .pyx file.

  • get_methodheader() Returns the Cython method header for methods without arguments except self.

  • decorate_method() The decorated method returns a Lines object including a method header. However, the Lines object is empty if the respective model does not implement a method with the same name as the wrapped method.

  • Cythonizer Handles the writing, compiling and initialisation of Cython models.

  • PyxWriter Translates the source code of Python models into Cython source code.

  • FuncConverter Helper class for class PyxWriter that analyses Python functions and provides the required Cython code via property pyxlines.

  • exp() Cython wrapper for the exp() function of module numpy applied on a single float object.

  • log() Cython wrapper for the log() function of module numpy applied on a single float object.

  • fabs() Cython wrapper for the exp() function of module math applied on a single float object.

  • sin() Cython wrapper for the sin() function of module numpy applied on a single float object.

  • cos() Cython wrapper for the cos() function of module numpy applied on a single float object.

  • tan() Cython wrapper for the tan() function of module numpy applied on a single float object.

  • asin() Cython wrapper for the arcsin() function of module numpy applied on a single float object.

  • acos() Cython wrapper for the arccos() function of module numpy applied on a single float object.

  • atan() Cython wrapper for the arctan() function of module numpy applied on a single float object.

  • isnan() Cython wrapper for the isnan() function of module numpy applied on a single float object.

  • isinf() Cython wrapper for the isinf() function of module numpy applied on a single float object.

hydpy.cythons.modelutils.get_dllextension() → str[source]

Return the DLL file extension for the current operating system.

The returned value depends on the response of function system() of module platform. get_dllextension() returns .pyd if system() returns the string “windows” and .so for all other strings:

>>> from hydpy.cythons.modelutils import get_dllextension
>>> import platform
>>> from unittest import mock
>>> with mock.patch.object(
...     platform, "system", side_effect=lambda: "Windows") as mocked:
...     get_dllextension()
'.pyd'
>>> with mock.patch.object(
...     platform, "system", side_effect=lambda: "Linux") as mocked:
...     get_dllextension()
'.so'
hydpy.cythons.modelutils.TYPE2STR: Dict[Optional[Type[Any]], str] = {<class 'bool'>: 'bint', <class 'int'>: 'numpy.int64_t', <class 'hydpy.core.parametertools.IntConstant'>: 'numpy.int64_t', <class 'float'>: 'double', <class 'str'>: 'str', None: 'void', <class 'hydpy.core.typingtools.Vector'>: 'double[:]', hydpy.core.typingtools.Vector[float]: 'double[:]'}

Maps Python types to Cython compatible type declarations.

The Cython type belonging to Python’s int is selected to agree with numpy’s default integer type on the current platform/system.

hydpy.cythons.modelutils.CHECKABLE_TYPES: Tuple[Type[Any], …] = (<class 'bool'>, <class 'int'>, <class 'hydpy.core.parametertools.IntConstant'>, <class 'float'>, <class 'str'>, <class 'hydpy.core.typingtools.Vector'>)

“Real types” of TYPE2STR allowed as second arguments of function isinstance().

class hydpy.cythons.modelutils.Lines(*args)[source]

Bases: list

Handles code lines for .pyx file.

add(indent: int, line: Union[str, Iterable[str]]) → None[source]

Append the given text line with prefixed spaces following the given number of indentation levels.

hydpy.cythons.modelutils.get_methodheader(methodname: str, nogil: bool = False, idxarg: bool = False) → str[source]

Returns the Cython method header for methods without arguments except self.

Note the influence of the configuration flag FASTCYTHON:

>>> from hydpy.cythons.modelutils import get_methodheader
>>> from hydpy import config
>>> config.FASTCYTHON = False
>>> print(get_methodheader(methodname="test", nogil=True, idxarg=False))
cpdef inline void test(self):
>>> config.FASTCYTHON = True
>>> print(get_methodheader(methodname="test", nogil=True, idxarg=True))
cpdef inline void test(self, int idx) nogil:
hydpy.cythons.modelutils.decorate_method(wrapped: Callable) → property[source]

The decorated method returns a Lines object including a method header. However, the Lines object is empty if the respective model does not implement a method with the same name as the wrapped method.

class hydpy.cythons.modelutils.Cythonizer[source]

Bases: object

Handles the writing, compiling and initialisation of Cython models.

Model: Type[hydpy.core.modeltools.Model]
Parameters: Type[hydpy.core.parametertools.Parameters]
Sequences: Type[hydpy.core.sequencetools.Sequences]
tester: hydpy.core.testtools.Tester
finalise() → None[source]

Test and cythonize the relevant model eventually.

Method finalise() might call method cythonize() and method perform_tests() depending on the actual values of the options autocompile, usecython, and skipdoctests as well the value currently returned by property outdated. To explain and test the considerable amount of relevant combinations, we make use of Python’s unittest mock library.

First, we import the Cythonizer instance responsible for application model hland_v1, the classes Cythonizer and Tester, module pub, and the unittest mock library:

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy.cythons.modelutils import Cythonizer
>>> from hydpy.core.testtools import Tester
>>> from hydpy import pub
>>> from unittest import mock

Second, we memorise the relevant settings to restore them later:

>>> autocompile = pub.options.autocompile
>>> skipdoctests = pub.options.skipdoctests
>>> usecython = pub.options.usecython
>>> outdated = Cythonizer.outdated

Third, we define a test function mocking methods cythonize() and perform_tests(), printing when the mocks are called and providing information on the current value of option usecython:

>>> def test():
...     sc = lambda: print(
...         f"calling method `cythonize` "
...         f"(usecython={bool(pub.options.usecython)})")
...     se = lambda: print(
...         f"calling method `perform_tests` "
...         f"(usecython={bool(pub.options.usecython)})")
...     with mock.patch.object(
...                 Cythonizer, "cythonize", side_effect=sc) as mc,\
...             mock.patch.object(
...                 Tester, "perform_tests", side_effect=se) as mt:
...         cythonizer.finalise()

With either option autocompile or property outdated being False, nothing happens:

>>> pub.options.autocompile = False
>>> Cythonizer.outdated = True
>>> test()

>>> pub.options.autocompile = True
>>> Cythonizer.outdated = False
>>> test()

Option usecython enables/disables the actual cythonization and option skipdoctests enables/disables the testing of the Python model and, if available, of the Cython model:

>>> Cythonizer.outdated = True
>>> pub.options.usecython = False
>>> pub.options.skipdoctests = True
>>> test()

>>> pub.options.skipdoctests = False
>>> test()
calling method `perform_tests` (usecython=False)

>>> pub.options.usecython = True
>>> pub.options.skipdoctests = True
>>> test()
calling method `cythonize` (usecython=True)

>>> pub.options.skipdoctests = False
>>> test()
calling method `perform_tests` (usecython=False)
calling method `cythonize` (usecython=False)
calling method `perform_tests` (usecython=True)

>>> pub.options.autocompile = autocompile
>>> Cythonizer.outdated = outdated
>>> pub.options.skipdoctests = skipdoctests
cythonize() → None[source]

Translate Python source code of the relevant model first into Cython and then into C, compile it, and move the resulting dll file to the autogen subfolder of subpackage cythons.

property pyname

Name of the original Python module or package.

>>> from hydpy.models.hland import cythonizer
>>> cythonizer.pyname
'hland'
>>> from hydpy.models.hland_v1 import cythonizer
>>> cythonizer.pyname
'hland_v1'
property cyname

Name of the compiled module.

>>> from hydpy.models.hland import cythonizer
>>> cythonizer.cyname
'c_hland'
>>> from hydpy.models.hland_v1 import cythonizer
>>> cythonizer.cyname
'c_hland_v1'
property cydirpath

The absolute path of the directory containing the compiled modules.

>>> from hydpy.models.hland import cythonizer
>>> from hydpy import repr_
>>> repr_(cythonizer.cydirpath)   
'.../hydpy/cythons/autogen'
>>> import os
>>> os.path.exists(cythonizer.cydirpath)
True
property cymodule

The compiled module.

Property cymodule returns the relevant DLL module:

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy.cythons.autogen import c_hland_v1
>>> c_hland_v1 is cythonizer.cymodule
True

However, if this module is missing for some reasons, it tries to create the module first and returns it afterwards. For demonstration purposes, we define a wrong cyname:

>>> from hydpy.cythons.modelutils import Cythonizer
>>> cyname = Cythonizer.cyname
>>> Cythonizer.cyname = "wrong"
>>> cythonizer._cymodule = None
>>> from unittest import mock
>>> with mock.patch.object(Cythonizer, "cythonize") as mock:
...     cythonizer.cymodule
Traceback (most recent call last):
...
ModuleNotFoundError: No module named 'hydpy.cythons.autogen.wrong'
>>> mock.call_args_list
[call()]

>>> Cythonizer.cyname = cyname
property pyxfilepath

The absolute path of the compiled module.

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy import repr_
>>> repr_(cythonizer.pyxfilepath)   
'.../hydpy/cythons/autogen/c_hland_v1.pyx'
>>> import os
>>> os.path.exists(cythonizer.pyxfilepath)
True
property dllfilepath

The absolute path of the compiled module.

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy import repr_
>>> repr_(cythonizer.dllfilepath)   
'.../hydpy/cythons/autogen/c_hland_v1...'
>>> import os
>>> os.path.exists(cythonizer.dllfilepath)
True
property buildpath

The absolute path for temporarily build files.

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy import repr_
>>> repr_(cythonizer.buildpath)   
'.../hydpy/cythons/autogen/_build'
property pyxwriter

A new PyxWriter instance.

>>> from hydpy.models.hland_v1 import cythonizer
>>> pyxwriter = cythonizer.pyxwriter
>>> from hydpy import classname
>>> classname(pyxwriter)
'PyxWriter'
>>> cythonizer.pyxwriter is pyxwriter
False
property pysourcefiles

All relevant source files of the actual model.

We consider source files to be relevant if they are part of the HydPy package and if they define ancestors of the classes of the considered model.

For the base model hland, all relevant modules seem to be covered:

>>> from hydpy.models.hland import cythonizer
>>> import os, pprint
>>> pprint.pprint([fn.split(os.path.sep)[-1] for fn in
...                sorted(cythonizer.pysourcefiles)])
['masktools.py',
 'modeltools.py',
 'parametertools.py',
 'sequencetools.py',
 'testtools.py',
 'variabletools.py',
 'modelutils.py',
 '__init__.py',
 'hland_masks.py',
 'hland_model.py']

However, this is not the case for application model hland_v1, where the base model files are missing. Hence, relevant changes in its base model might not be detected, resulting in an outdated application model. This issue is relevant for developers only, but we should fix it someday:

>>> from hydpy.models.hland_v1 import cythonizer
>>> import os, pprint
>>> pprint.pprint([fn.split(os.path.sep)[-1] for fn in
...                sorted(cythonizer.pysourcefiles)])
['masktools.py',
 'modeltools.py',
 'parametertools.py',
 'sequencetools.py',
 'testtools.py',
 'variabletools.py',
 'modelutils.py',
 'hland_v1.py']
property outdated

True/False flag indicating whether a Cythonizer object should renew its Cython model or not.

With option forcecompiling being True, property outdated also return True under all circumstances:

>>> from hydpy.models.hland_v1 import cythonizer
>>> from hydpy import pub
>>> forcecompiling = pub.options.forcecompiling
>>> pub.options.forcecompiling = True
>>> cythonizer.outdated
True

With option forcecompiling being False, property outdated generally return False if HydPy is a site-package (under the assumption the user does not modify his site-package files and for reasons of efficiency due to skipping the following tests):

>>> pub.options.forcecompiling = False
>>> from unittest import mock
>>> with mock.patch("hydpy.__path__", ["folder/somename-packages/hydpy"]):
...     cythonizer.outdated
False
>>> with mock.patch("hydpy.__path__", ["folder/pkgs/hydpy"]):
...     cythonizer.outdated
False

When working with a “local” HydPy package (that is not part of the site-packages directory) property outdated returns True if the required DLL file is not available at all:

>>> with mock.patch("hydpy.__path__", ["folder/local_dir/hydpy"]):
...     with mock.patch.object(
...             type(cythonizer), "dllfilepath",
...             new_callable=mock.PropertyMock) as dllfilepath:
...         dllfilepath.return_value = "missing"
...         cythonizer.outdated
True

If the DLL file is available, property outdated returns True or False depending on the timestamp of the DLL file itself and the timestamp of the newest file returned by property pysourcefiles:

>>> from hydpy import TestIO
>>> with TestIO():
...     with open("new.txt", "w"):
...         pass
...     with mock.patch("hydpy.__path__", ["folder/local_dir/hydpy"]):
...         with mock.patch.object(
...                 type(cythonizer), "dllfilepath",
...                 new_callable=mock.PropertyMock) as mocked:
...             mocked.return_value = "new.txt"
...             cythonizer.outdated
...         with mock.patch.object(
...                 type(cythonizer), "pysourcefiles",
...                 new_callable=mock.PropertyMock) as mocked:
...             mocked.return_value = ["new.txt"]
...             cythonizer.outdated
False
True

>>> pub.options.forcecompiling = forcecompiling
compile_() → None[source]

Translate Cython code to C code and compile it.

move_dll() → None[source]

Try to find the DLL file created my method compile_() and to move it into the autogen folder of the cythons subpackage.

Usually, one does not need to apply the move_dll() method directly. However, if you are a model developer, you might see one of the following error messages from time to time:

>>> from hydpy.models.hland_v1 import cythonizer
>>> cythonizer.move_dll()   
Traceback (most recent call last):
...
OSError: After trying to cythonize model `hland_v1`, the resulting file `c_hland_v1...` could not be found in directory `.../hydpy/cythons/autogen/_build` nor any of its subdirectories.  The distutil report should tell whether the file has been stored somewhere else, is named somehow else, or could not be build at all.

>>> import os
>>> from unittest import mock
>>> from hydpy import TestIO
>>> with TestIO():   
...     with mock.patch.object(
...             type(cythonizer), "buildpath", new_callable=mock.PropertyMock
...     ) as mocked_buildpath:
...         mocked_buildpath.return_value = "_build"
...         os.makedirs("_build/subdir", exist_ok=True)
...         filepath = f"_build/subdir/c_hland_v1{get_dllextension()}"
...         with open(filepath, "w"):
...             pass
...         with mock.patch(
...                 "shutil.move",
...                 side_effect=PermissionError("Denied!")):
...             cythonizer.move_dll()
Traceback (most recent call last):
...
PermissionError: After trying to cythonize module `hland_v1`, when trying to move the final cython module `c_hland_v1...` from directory `_build` to directory `.../hydpy/cythons/autogen`, the following error occurred: Denied! A likely error cause is that the cython module `c_hland_v1...` does already exist in this directory and is currently blocked by another Python process.  Maybe it helps to close all Python processes and restart the cythonization afterwards.
class hydpy.cythons.modelutils.PyxWriter(cythonizer: hydpy.cythons.modelutils.Cythonizer, model: hydpy.core.modeltools.Model, pyxpath: str)[source]

Bases: object

Translates the source code of Python models into Cython source code.

Method PyxWriter serves as a master method, which triggers the complete writing process. The other properties and methods supply the required code lines. Their names are selected to match the names of the original Python models as close as possible.

cythonizer: hydpy.cythons.modelutils.Cythonizer
model: hydpy.core.modeltools.Model
pyxpath: str
write() → None[source]

Collect the source code and write it into a Cython extension file (“pyx”).

property cythondistutilsoptions

Cython and Distutils option lines.

Use the configuration options “FASTCYTHON” and “PROFILECYTHON” to configure the cythonization processes as follows:

>>> from hydpy.cythons.modelutils import PyxWriter
>>> pyxwriter = PyxWriter(None, None, None)
>>> pyxwriter.cythondistutilsoptions
#!python
# cython: language_level=3
# cython: boundscheck=False
# cython: wraparound=False
# cython: initializedcheck=False
# cython: cdivision=True

>>> from hydpy import config
>>> config.FASTCYTHON = False
>>> config.PROFILECYTHON = True
>>> pyxwriter.cythondistutilsoptions
#!python
# cython: language_level=3
# cython: boundscheck=True
# cython: wraparound=True
# cython: initializedcheck=True
# cython: cdivision=False
# cython: linetrace=True
# distutils: define_macros=CYTHON_TRACE=1
# distutils: define_macros=CYTHON_TRACE_NOGIL=1

>>> config.FASTCYTHON = True
>>> config.PROFILECYTHON = False
property cimports

Import command lines.

property constants

Constants declaration lines.

property parameters

Parameter declaration lines.

property sequences

Sequence declaration lines.

static iosequence(seq: hydpy.core.sequencetools.IOSequence) → List[str][source]

Declaration lines for the given IOSequence object.

static open_files(subseqs: hydpy.core.sequencetools.IOSequences) → List[str][source]

Open file statements.

static close_files(subseqs: hydpy.core.sequencetools.IOSequences) → List[str][source]

Close file statements.

static load_data(subseqs: hydpy.core.sequencetools.IOSequences) → List[str][source]

Load data statements.

static save_data(subseqs: hydpy.core.sequencetools.IOSequences) → List[str][source]

Save data statements.

set_pointer(subseqs: Union[hydpy.core.sequencetools.InputSequences, hydpy.core.sequencetools.OutputSequences, hydpy.core.sequencetools.LinkSequences]) → List[str][source]

Set pointer statements for all input, output, and link sequences.

static set_pointer0d(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Set pointer statements for 0-dimensional link sequences.

static get_value(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Get value statements for link sequences.

static set_value(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Set value statements for link sequences.

static alloc(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Allocate memory statements for 1-dimensional link sequences.

static dealloc(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Deallocate memory statements for 1-dimensional link sequences.

static set_pointer1d(subseqs: hydpy.core.sequencetools.LinkSequences) → List[str][source]

Set_pointer statements for 1-dimensional link sequences.

static set_pointerinput(subseqs: hydpy.core.sequencetools.InputSequences) → List[str][source]

Set pointer statements for input sequences.

set_pointeroutput(subseqs: hydpy.core.sequencetools.OutputSequences) → List[str][source]

Set pointer statements for output sequences.

property numericalparameters

Numeric parameter declaration lines.

property submodels

Submodel declaration lines.

property modeldeclarations

The attribute declarations of the model class.

property modelstandardfunctions

The standard functions of the model class.

property modelnumericfunctions

Numerical integration functions of the model class.

property simulate

Simulation statements.

property iofunctions

Input/output functions of the model class.

The result of property iofunctions depends on the availability of different types of sequences. So far, the models implemented in HydPy do not reflect all possible combinations, which is why we modify the hland_v1 application model in the following examples:

>>> from hydpy.models.hland_v1 import cythonizer
>>> pyxwriter = cythonizer.pyxwriter
>>> pyxwriter.iofunctions
            . open_files
            . close_files
            . load_data
            . save_data
    cpdef inline void open_files(self):
        self.sequences.inputs.open_files(self.idx_sim)
        self.sequences.fluxes.open_files(self.idx_sim)
        self.sequences.states.open_files(self.idx_sim)
    cpdef inline void close_files(self):
        self.sequences.inputs.close_files()
        self.sequences.fluxes.close_files()
        self.sequences.states.close_files()
    cpdef inline void load_data(self) nogil:
        self.sequences.inputs.load_data(self.idx_sim)
    cpdef inline void save_data(self, int idx) nogil:
        self.sequences.inputs.save_data(self.idx_sim)
        self.sequences.fluxes.save_data(self.idx_sim)
        self.sequences.states.save_data(self.idx_sim)

>>> pyxwriter.model.sequences.fluxes = None
>>> pyxwriter.model.sequences.states = None
>>> pyxwriter.iofunctions
            . open_files
            . close_files
            . load_data
            . save_data
    cpdef inline void open_files(self):
        self.sequences.inputs.open_files(self.idx_sim)
    cpdef inline void close_files(self):
        self.sequences.inputs.close_files()
    cpdef inline void load_data(self) nogil:
        self.sequences.inputs.load_data(self.idx_sim)
    cpdef inline void save_data(self, int idx) nogil:
        self.sequences.inputs.save_data(self.idx_sim)

>>> pyxwriter.model.sequences.inputs = None
>>> pyxwriter.iofunctions
property new2old

Old states to new states statements.

property update_receivers

Lines of the model method with the same name.

property update_inlets

Lines of the model method with the same name.

run(model: hydpy.core.modeltools.AdHocModel) → List[str][source]

Return the lines of the model method with the same name.

property update_outlets

Lines of the model method with the same name.

property update_senders

Lines of the model method with the same name.

property update_outputs_model

Lines of the model method with the same name (except the _model suffix).

update_outputs(subseqs: hydpy.core.sequencetools.OutputSequences) → List[str][source]

Lines of the subsequences method with the same name.

calculate_single_terms(model: hydpy.core.modeltools.SolverModel) → List[str][source]

Return the lines of the model method with the same name.

calculate_full_terms(model: hydpy.core.modeltools.SolverModel) → List[str][source]

Return the lines of the model method with the same name.

property listofmodeluserfunctions

User functions of the model class.

property modeluserfunctions

Model-specific functions.

property solve

Lines of the model method with the same name.

property get_point_states

Lines of model method get_point_states.

property set_point_states

Lines of model method set_point_states.

property set_result_states

Lines of model method set_result_states.

property get_sum_fluxes

Lines of model method get_sum_fluxes.

property set_point_fluxes

Lines of model method set_point_fluxes.

property set_result_fluxes

Lines of model method set_result_fluxes.

property integrate_fluxes

Lines of model method integrate_fluxes.

property reset_sum_fluxes

Lines of model method reset_sum_fluxes.

property addup_fluxes

Lines of model method addup_fluxes.

property calculate_error

Lines of model method calculate_error.

property extrapolate_error

Extrapolate error statements.

write_stubfile()[source]

Write a stub file for the actual base or application model.

At the moment, HydPy creates model objects quite dynamically. In many regards, this comes with lots of conveniences. However, there two critical drawbacks compared to more static approaches: some amount of additional initialisation time and, more important, much opaqueness for code inspection tools. In this context, we experiment with “stub files” at the moment. These could either contain typing information only or define statically predefined model classes. The following example uses method write_stubfile() to write a (far from perfect) prototype stub file for base model hland:

>>> from hydpy.models.hland import *
>>> cythonizer.pyxwriter.write_stubfile()

This is the path to the written file:

>>> import os
>>> import hydpy
>>> filepath = os.path.join(hydpy.__path__[0], "hland.py")
>>> os.path.exists(filepath)
True

However, it’s just an experimental prototype, so we better remove it:

>>> os.remove(filepath)
>>> os.path.exists(filepath)
False
class hydpy.cythons.modelutils.FuncConverter(model: hydpy.core.modeltools.Model, funcname: str, func: Callable)[source]

Bases: object

Helper class for class PyxWriter that analyses Python functions and provides the required Cython code via property pyxlines.

func: Callable
model: hydpy.core.modeltools.Model
funcname: str
property argnames

The argument names of the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).argnames
['model']
property varnames

The variable names of the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).varnames
('self', 'con', 'inp', 'flu', 'k')
property locnames

The variable names of the handled function except for the argument names.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).locnames
['self', 'con', 'inp', 'flu', 'k']
property subgroupnames

The complete names of the subgroups relevant for the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).subgroupnames
['parameters.control', 'sequences.inputs', 'sequences.fluxes']
property subgroupshortcuts

The abbreviated names of the subgroups relevant for the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).subgroupshortcuts
['con', 'inp', 'flu']
property untypedvarnames

The names of the untyped variables used in the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).untypedvarnames
['k']
property untypedarguments

The names of the untyped arguments used by the current function.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).untypedarguments
[]
property untypedinternalvarnames

The names of the untyped variables used in the current function except for those of the arguments.

>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")
>>> FuncConverter(model, None, model.calc_tc_v1).untypedinternalvarnames
['k']
property cleanlines

The leaned code lines of the current function.

The implemented cleanups:

  • eventually, remove method version

  • remove all docstrings

  • remove all comments

  • remove all empty lines

  • remove line bracks within brackets

  • remove the phrase modelutils

  • remove all lines containing the phrase fastaccess

  • replace all shortcuts with complete reference names

  • replace “model.” with “self.”

static remove_linebreaks_within_equations(code: str) → str[source]

Remove line breaks within equations.

The following example is not an exhaustive test but shows how the method works in principle:

>>> code = "asdf = \\\n(a\n+b)"
>>> from hydpy.cythons.modelutils import FuncConverter
>>> FuncConverter.remove_linebreaks_within_equations(code)
'asdf = (a+b)'
static remove_imath_operators(lines: List[str])[source]

Remove mathematical expressions that require Pythons global interpreter locking mechanism.

The following example is not an exhaustive test but shows how the method works in principle:

>>> lines = ["    x += 1*1"]
>>> from hydpy.cythons.modelutils import FuncConverter
>>> FuncConverter.remove_imath_operators(lines)
>>> lines
['    x = x + (1*1)']
property pyxlines

Cython code lines of the current function.

Assumptions:

  • The function shall be a method.

  • The method shall be inlined.

  • Annotations specify all argument and return types.

  • Local variables are generally of type int but of type double when their name starts with d_.

We import some classes and prepare a pure-Python instance of application model hland_v1:

>>> from types import MethodType
>>> from hydpy.core.modeltools import Method, Model
>>> from hydpy.core.typingtools import Vector
>>> from hydpy.cythons.modelutils import FuncConverter
>>> from hydpy import prepare_model, pub
>>> with pub.options.usecython(False):
...     model = prepare_model("hland_v1")

First, we show an example on a standard method without additional arguments and returning nothing but requiring two local variables:

>>> class Calc_Test_V1(Method):
...     @staticmethod
...     def __call__(model: Model) -> None:
...         con = model.parameters.control.fastaccess
...         flu = model.sequences.fluxes.fastaccess
...         inp = model.sequences.inputs.fastaccess
...         for k in range(con.nmbzones):
...             d_pc = con.kg[k]*inp.p[k]
...             flu.pc[k] = d_pc
>>> model.calc_test_v1 = MethodType(Calc_Test_V1.__call__, model)
>>> FuncConverter(model, "calc_test_v1", model.calc_test_v1).pyxlines
    cpdef inline void calc_test_v1(self)  nogil:
        cdef double d_pc
        cdef int k
        for k in range(self.parameters.control.nmbzones):
            d_pc = self.parameters.control.kg[k]*self.sequences.inputs.p[k]
            self.sequences.fluxes.pc[k] = d_pc

The second example shows that float and Vector annotations translate into double and double[:] types, respectively:

>>> class Calc_Test_V2(Method):
...     @staticmethod
...     def __call__(
...             model: Model, value: float, values: Vector) -> float:
...         con = model.parameters.control.fastaccess
...         return con.kg[0]*value*values[1]
>>> model.calc_test_v2 = MethodType(Calc_Test_V2.__call__, model)
>>> FuncConverter(model, "calc_test_v2", model.calc_test_v2).pyxlines
    cpdef inline double calc_test_v2(self, double value, double[:] values)  nogil:
        return self.parameters.control.kg[0]*value*values[1]
hydpy.cythons.modelutils.exp(double: float) → float[source]

Cython wrapper for the exp() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import exp
>>> from unittest import mock
>>> with mock.patch("numpy.exp") as func:
...     _ = exp(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.log(double: float) → float[source]

Cython wrapper for the log() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import log
>>> from unittest import mock
>>> with mock.patch("numpy.log") as func:
...     _ = log(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.fabs(double: float) → float[source]

Cython wrapper for the exp() function of module math applied on a single float object.

>>> from hydpy.cythons.modelutils import fabs
>>> from unittest import mock
>>> with mock.patch("math.fabs") as func:
...     _ = fabs(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.sin(double: float) → float[source]

Cython wrapper for the sin() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import sin
>>> from unittest import mock
>>> with mock.patch("numpy.sin") as func:
...     _ = sin(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.cos(double: float) → float[source]

Cython wrapper for the cos() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import cos
>>> from unittest import mock
>>> with mock.patch("numpy.cos") as func:
...     _ = cos(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.tan(double: float) → float[source]

Cython wrapper for the tan() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import tan
>>> from unittest import mock
>>> with mock.patch("numpy.tan") as func:
...     _ = tan(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.asin(double: float) → float[source]

Cython wrapper for the arcsin() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import asin
>>> from unittest import mock
>>> with mock.patch("numpy.arcsin") as func:
...     _ = asin(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.acos(double: float) → float[source]

Cython wrapper for the arccos() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import acos
>>> from unittest import mock
>>> with mock.patch("numpy.arccos") as func:
...     _ = acos(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.atan(double: float) → float[source]

Cython wrapper for the arctan() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import atan
>>> from unittest import mock
>>> with mock.patch("numpy.arctan") as func:
...     _ = atan(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.isnan(double: float) → float[source]

Cython wrapper for the isnan() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import isnan
>>> from unittest import mock
>>> with mock.patch("numpy.isnan") as func:
...     _ = isnan(123.4)
>>> func.call_args
call(123.4)
hydpy.cythons.modelutils.isinf(double: float) → float[source]

Cython wrapper for the isinf() function of module numpy applied on a single float object.

>>> from hydpy.cythons.modelutils import isnan
>>> from unittest import mock
>>> with mock.patch("numpy.isinf") as func:
...     _ = isinf(123.4)
>>> func.call_args
call(123.4)