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two_dim.py

Source:two_dim.py

1'''2    Two dimensional measured objects where the second abscissa variable is either3        * discrete (:class:`FunctionBundle`), or4        * continuous (:class:`MeasuredSurface`)5'''6import matplotlib.pyplot as plt7import numpy as np8from scipy import interpolate9import matplotlib.cm as cm10from functools import wraps11from itertools import repeat12from .one_dim import MeasuredFunction, Waveform13from lightlab.laboratory import Hashable14class FunctionBundle(Hashable):  # pylint: disable=eq-without-hash15    ''' A bundle of :class:`~lightlab.util.data.one_dim.MeasuredFunction`'s: "z" vs. "x", "i"16        The key is that they have the same abscissa base.17        This class will take care of resampling in a common abscissa base.18        The bundle can be:19            * iterated to get the individual :class`~lightlab.util.data.one_dim.MeasuredFunction`'s20            * operated on with other ``FunctionBundles``21            * plotted with :meth`simplePlot` and :meth:`multiAxisPlot`22        Feeds through **callable** signal processing methods to its members (type MeasuredFunction),23        If the method is not found in the FunctionBundle, and it is in it's member,24        it will be mapped to every function in the bundle, returning a new bundle.25        Distinct from a :class:`MeasuredSurface` because26        the additional axis does not represent a continuous thing.27        It is discrete and sometimes unordered.28        Distinct from a :class:`FunctionalBasis` because29        it does not support most linear algebra-like stuff30        (e.g. decomposision, matrix multiplication, etc.).31        This is not a strict rule.32    '''33    def __init__(self, measFunList=None):34        ''' Can be initialized fully, or initialized with None to be built interactively.35            Args:36                measFunList (list[MeasuredFunction] or None): list of MeasuredFunctions that must have the same abscissa.37        '''38        self.absc = None39        self.ordiMat = None40        self.memberType = None41        self.nDims = 042        if measFunList is not None:43            if type(measFunList) is list:44                for m in measFunList:45                    self.addDim(m)46            else:47                self.addDim(measFunList)48    def addDim(self, newMeasFun):49        if self.absc is None:50            self.absc = newMeasFun.absc51            self.ordiMat = np.matrix(newMeasFun.ordi)52            self.memberType = type(newMeasFun)53        else:54            y = self._putInTimebase(newMeasFun)  # This does the checking for type and timebase55            self.ordiMat = np.append(self.ordiMat, [y], axis=0)56        self.nDims += 157    def __getitem__(self, index):58        ''' Gives out individual measured functions of the type used, or59            If it gets a slice, it returns a function bundle,60            just like a sliced numpy array returns a numpy array.61            Args:62                index (int or slice): which of the function(s) do you want to get63            Returns:64                (MeasuredFunction or FunctionBundle): depending on type of index65        '''66        if type(index) is int:67            theOrdi = self.ordiMat[index, :].A1  # A1 is a special numpy thing that converts from matrix to 1-d array68            return self.memberType(self.absc, theOrdi)69        elif type(index) is slice:70            newBundle = self.copy()71            newOrdiMat = self.ordiMat[index, :]72            newBundle.ordiMat = newOrdiMat73            newBundle.nDims = np.shape(newOrdiMat)[0]74            return newBundle75    def __len__(self):76        return self.ordiMat.shape[0]77    def __eq__(self, other):78        return (self.memberType is other.memberType and79                self.absc == other.absc and80                self.ordiMat == other.ordiMat)81    def __add__(self, other):82        ''' This works with scalars, vectors, MeasuredFunctions, and FunctionBundles83        '''84        if np.isscalar(other) or isinstance(other, MeasuredFunction):85            other = repeat(other, self.nDims)86        newBundle = type(self)()87        for selfItem, otherItem in zip(self, other):88            newItem = selfItem + otherItem89            newBundle.addDim(newItem)90        return newBundle91    def __radd__(self, other):92        return self.__add__(other)93    def __sub__(self, other):94        return self.__add__(-1 * other)95    def __mul__(self, other):96        ''' This works with scalars, vectors, MeasuredFunctions, and FunctionBundles97        '''98        if np.isscalar(other) or isinstance(other, MeasuredFunction):99            other = repeat(other, self.nDims)100        newBundle = type(self)()101        for selfItem, otherItem in zip(self, other):102            newItem = selfItem * otherItem103            newBundle.addDim(newItem)104        return newBundle105    def __rmul__(self, other):106        return self.__mul__(other)107    def __truediv__(self, other):108        return self * (1 / other)109    def __getattr__(self, attrName):110        ''' Feeds through **callable** methods to its members (type MeasuredFunction),111            if the attribute is not found in the FunctionBundle.112            If it finds it there, then applies it to every function in the bundle.113            With binary operator math, it only works with scalars.114            For example, starting with::115            .. code-block:: python116                spct1 = Spectrum([0, 1, 2], [2, 5, 3])117                spct2 = Spectrum([0, 1, 2], [4, 4, 1])118                funBun = FunctionBundle([spct1, spct2])119            You can do120            .. code-block:: python121                croppedFunBund = FunctionBundle([122                                      spct1.crop([0, 1]),123                                      spct2.crop([0, 1]))124                                      ])125                funBun.crop([0, 1]) == croppedFunBund126            Note:127                Be careful about "overloading" a MeasuredFunction method in FunctionBundle.128                It can have a different meaning than what would happen if not overloaded.129                For example ``MeasuredFunction.__mul__(nonscalar)`` is function multiplication,130                while ``FunctionBundle.__mul__(nonscalar)`` means vector dot product.131            Todo:132                This only works with :class:`~lightlab.util.data.one_dim.SignalProcessingMixin`133                methods that are MF-in/MF-out. How should we handle characteristic-type methods,134                such as ``centerOfMass``, or ``getRange``?135        '''136        if attrName == 'memberType':137            raise RuntimeError('Missed "memberType"')138        try:139            memberClassFunc = getattr(self.memberType, attrName)140        except AttributeError as err:141            betterErrStr = err.args[0] + ', neither does \'{}\' object'.format(type(self).__name__)142            err.args = tuple([betterErrStr, err.args[1:]])143            raise144        if not callable(memberClassFunc):145            raise TypeError(f'{memberClassFunc} in'146                            ' {} of {}'.format(type(self).__name__, self.memberType.__name__) + ' '147                            'is not callable')148        @wraps(memberClassFunc)149        def fakeFun(*args, **kwargs):150            newBundle = type(self)()151            for item in self:152                newItem = memberClassFunc(item, *args, **kwargs)153                newBundle.addDim(newItem)154            return newBundle155        return fakeFun156    def copy(self):157        newObj = type(self)()158        newObj.__dict__ = self.__dict__.copy()159        newObj.ordiMat = self.ordiMat.copy()160        return newObj161    def extend(self, otherFunctionBund):162        for func in otherFunctionBund:163            self.addDim(func)164    def max(self):165        ''' Returns a single MeasuredFunction(subclass) that is the maximum of all in this bundle166        '''167        return self.memberType(self.absc, np.max(self.ordiMat, axis=0).A1)168    def min(self):169        ''' Returns a single MeasuredFunction(subclass) that is the minimum of all in this bundle170        '''171        return self.memberType(self.absc, np.min(self.ordiMat, axis=0).A1)172    def mean(self):173        ''' Returns a single MeasuredFunction(subclass) that is the mean of all in this bundle174        '''175        return self.memberType(self.absc, np.mean(self.ordiMat, axis=0).A1)176    def _putInTimebase(self, testFun):177        ''' Makes sure signal type is correct and time basis is the same178            Args:179                testFun (MeasuredFunction): the new function, as encapsulated by an object180            Returns:181                array: the ordinate of the new function in the right timebase182            Raises:183                TypeError: if the type of ``testFun`` is different than the others in this FunctionalBasis184        '''185        if type(testFun).__name__ is not self.memberType.__name__:186            raise TypeError('This FunctionalBasis expects ' + str(self.memberType) +187                            ', but was given ' + str(type(testFun)) + '.')188        # Check time base189        if np.any(testFun.absc != self.absc):190            # logger.warning('Warning: Basis signal time abscissa are different. Interpolating...')191            return testFun(self.absc)192        else:193            return testFun.ordi194    def simplePlot(self, *args, **kwargs):195        '''196        '''197        for f in self:198            f.simplePlot(*args, **kwargs)199    def multiAxisPlot(self, *args, axList=None, titleRoot=None, **kwargs):200        ''' titleRoot must take one argument in its format method, which is given the index201            Returns:202                (list(axis)): The axes that were plotted upon203        '''204        if axList is None:205            _, axList = plt.subplots(nrows=len(self), figsize=(14, 14))206        if len(axList) != len(self):207            raise ValueError('Wrong number of axes. Got {}, need {}.'.format(208                len(axList), len(self)))209        for i, ax in enumerate(axList):210            plt.sca(ax)211            self[i].simplePlot(*args, **kwargs)212            if titleRoot is not None:213                plt.title(titleRoot.format(i + 1))214                # plt.xlabel('Time (s)')215            if i < len(self) - 1:216                ax.xaxis.set_ticklabels([])217                ax.set_xlabel('')218            # plt.ylabel('Intensity (a.u.)')219            # plt.xlim(0,2e-8)220        return axList221    def histogram(self):222        ''' Gives a MeasuredFunction of counts vs. ordinate values (typically voltage)223            Does not maintain any abscissa information224            At this point, does not allow caller to set the arguments passed to np.histogram225            This is mainly just for plotting226        '''227        hist, bins = np.histogram(self.ordiMat, bins='auto', density=False)228        histFun = MeasuredFunction([], [])229        for iBin, thisOrdi in enumerate(hist):230            thisAbsc = np.mean(bins[iBin:iBin + 1])231            histFun.addPoint((thisAbsc, thisOrdi))232        return histFun233    def weightedAddition(self, weiVec):234        ''' Calculates the weighted addition of the basis signals235            Args:236                weiVec (array): weights to be applied to the basis functions237            Returns:238                (MeasuredFunction): weighted addition of basis signals239        '''240        assert(len(weiVec) == self.nDims)241        weiVec = np.reshape(weiVec, (1, self.nDims))242        outOrdi = np.dot(weiVec, self.ordiMat)243        return self.memberType(self.absc, outOrdi.A1)244    def moment(self, order=2, allDims=True, relativeGauss=False):245        ''' The order'th moment of all the points in the bundle.246            Args:247                order (integer): the polynomial moment of inertia. Don't trust the normalization of > 2'th order.248                    order = 1: mean249                    order = 2: variance250                    order = 3: skew251                    order = 4: kurtosis252                allDims (bool): if true, collapses all signals, returning a scalar253            Returns:254                (ndarray or float): the specified moment(s)255        '''256        byDim = np.zeros(len(self))257        for iDim in range(len(self)):258            byDim[iDim] = self[iDim].moment(order, relativeGauss=relativeGauss)259        if allDims:260            return np.mean(byDim)261        else:262            return byDim263    def componentAnalysis(self, *args, pcaIca=True, lNorm=2, expectedComponents=None, **kwargs):264        ''' Gives the waveform representing the principal component of the order265            Args:266                pcaIca (bool): if True, does PCA; if False, does ICA267                lNorm (int): how to normalize weight vectors. L1 norm uses the maximum abs weight, while L2 norm (default) is vector unit268                expectedComponents (FunctionBundle or subclass): Used for flipping signs269                args, kwargs: Feed through to sklearn.decomposition.[PCA(), FastICA()]270            Returns:271                (FunctionBundle or subclass): principal component waveforms272        '''273        from sklearn.decomposition import PCA, FastICA274        compBuiltIn = PCA if pcaIca else FastICA275        compAnal = compBuiltIn(*args, **kwargs)276        compAnal.fit(self.ordiMat.T)277        pcBundle = type(self)()278        for c in compAnal.components_:279            if lNorm == 1:280                w = c / np.sqrt(np.max(c ** 2))281            elif lNorm == 2:282                w = c / np.sqrt(np.sum(c ** 2))283            else:284                raise Exception('Are you serious? Use a valid L-norm')285            pcWfm = self.weightedAddition(w)286            pcBundle.addDim(pcWfm)287        if expectedComponents is not None:288            pcBundle = pcBundle.correctSigns(expectedComponents, maintainOrder=pcaIca)289        return pcBundle290    def correctSigns(self, otherBundle, maintainOrder=True):291        ''' Goes through each component and flips the sign if correlation is negative292            ICA also has a permutation indeterminism.293        '''294        if len(self) != len(otherBundle):295            raise ValueError('Self and other bundle must be the same length')296        newBundle = type(self)()297        usedPermInds = []298        for iDim, oSig in enumerate(otherBundle):299            if maintainOrder:300                sSig = self[iDim]301                if (sSig * oSig).getMean() > 0:302                    newWfm = sSig303                else:304                    newWfm = -1 * sSig305            else:306                permCorrs = np.zeros(len(self))307                for jDim, sSig in enumerate(self):308                    if jDim not in usedPermInds:309                        permCorrs[jDim] = (sSig * oSig).getMean()310                permInd = np.argmax(np.abs(permCorrs))311                permSign = int(np.sign(permCorrs[permInd]))312                newWfm = permSign * self[permInd]313                usedPermInds.append(permInd)314            newBundle.addDim(newWfm)315        return newBundle316class FunctionalBasis(FunctionBundle):317    ''' A FunctionBundle that supports additional linear algebra methods318        Created for weighted addition, decomposition, and component analysis319    '''320    @classmethod321    def independentDefault(cls, nDims):322        ''' Gives a basis of non-overlapping pulses. Waveforms only323        '''324        newAbsc = np.linspace(0, 1, 1000)325        pWid = .5 / nDims326        newObj = cls()327        for iDim in range(nDims):328            on = (iDim + .25) / nDims329            sig = Waveform.pulse(newAbsc, tOn=on, tOff=on + pWid)330            newObj.addDim(sig)331        return newObj332    def innerProds(self, trial):333        ''' takes the inner products of the trial function onto this basis.334        '''335        tvec = self._putInTimebase(trial)336        tmat = np.tile(tvec, (self.nDims, 1))337        products = np.sum(np.multiply(tmat, self.ordiMat), axis=1).A1338        return products339    def magnitudes(self):340        ''' The inner product of the basis with itself341        '''342        return np.sum(np.multiply(self.ordiMat, self.ordiMat), axis=1).A1343    def project(self, trial):344        ''' Projects onto normalized basis345        If the basis is orthogonal, this is equivalent to weight decomposition346        '''347        return self.innerProds(trial) / self.magnitudes()348    def decompose(self, trial, moment=1):349        ''' Uses the Moore-Penrose pseudoinverse to get weight decomposition without orthogonality350            Args:351                trial (MeasuredFunction): signal to be decomposed352                moment (float): polynomial moment of the basis to use when decomposing353        '''354        tvec = self._putInTimebase(trial)355        momBasis = np.power(self.ordiMat, moment)356        basisInverse = np.linalg.pinv(momBasis)357        return np.dot(tvec, basisInverse).A1358    def matrixMultiply(self, weiMat):359        assert(weiMat.shape[1] == self.nDims)360        outBasis = type(self)()361        for weiVec in weiMat:362            weightedDim = self.weightedAddition(weiVec)363            outBasis.addDim(weightedDim)364        return outBasis365    def getMoment(self, weiVecs=None, order=2, relativeGauss=False):366        ''' This is actually the projected moment. Named for compatibility with bss package367            Make sure weiVecs is two dimensional368        '''369        moms = np.zeros(len(weiVecs))370        for iv, v in enumerate(weiVecs):371            weighted = self.weightedAddition(v)372            calcMom = weighted.moment(order, relativeGauss=relativeGauss)373            moms[iv] = calcMom374        return moms375    def remainder(self, trial):376        ''' Gives the remaining parts of the signal that are not explained by the minimum-squared-error decomposition377        '''378        explainedWeights = self.decompose(trial)379        explainedSignal = self.weightedAddition(explainedWeights)380        return trial - explainedSignal381    def covariance(self):382        ''' Returns covariance matrix of the basis, which is nDims x nDims '''383        return np.cov(self.ordiMat)384class MeasuredSurface(object):385    ''' Basically a two dimensional measured function: "z" vs. "x", "y"386        Useful trick when gathering data: build incrementally using :meth:`FunctionBundle.addDim`,387        then convert that to this class using :meth:`MeasuredSurface.fromFunctionBundle`.388    '''389    def __init__(self, absc, ordi):390        '''391            Args:392                absc (ndarray): same meaning as measured function393                ordi (ndarray): two-dimensional array or matrix394        '''395        if type(absc) == np.ndarray and absc.ndim != 1:396            raise Exception(397                'absc should be a 2-element list of arrays or an array of objects (which are arrays)')398        if len(absc) != 2:399            raise Exception('Wrong number of abscissas. Need two')400        abscshape = np.zeros(2)401        for iDim in range(2):402            abscshape[iDim] = len(absc[iDim])403        if not np.all(ordi.shape == abscshape):404            raise Exception('Dimensions of ordinate and abscissa do not match')405        self.absc = absc406        self.ordi = ordi407    @classmethod408    def fromFunctionBundle(cls, otherBund, addedAbsc=None):409        ''' gives back a MeasuredSurface from a function Bundle410            Args:411                otherBund (FunctionBundle): The source. The ordering of functions matters412                addedAbsc (np.ndarray): the second dimension abscissa array (default, integers)413            Returns:414                (:class:`MeasuredSurface`) new object415        '''416        existingAbsc = otherBund.absc417        if addedAbsc is None:418            addedAbsc = np.arange(otherBund.nDims)419        return cls([addedAbsc, existingAbsc], otherBund.ordiMat)420    def __call__(self, testAbscissaVec=None):421        f = interpolate.interp2d(*self.absc, z=self.ordi, kind='cubic')422        return f(*testAbscissaVec)423    def item(self, index, dim=None):424        if np.isscalar(index):425            assert(dim is not None)426            newAbsc = self.absc[dim]427            if dim == 0:428                newOrdi = self.ordi[index, :]429            else:430                newOrdi = self.ordi[:, index]431            return MeasuredFunction(newAbsc, newOrdi)432        else:433            assert(len(index) == 2)434            firstDimMf = self.item(index[0], dim=0)435            return firstDimMf[index[1]]436    def shape(self):437        return self.ordi.shape438    def simplePlot(self, *args, **kwargs):439        if 'cmap' not in kwargs.keys():440            kwargs['cmap'] = cm.inferno  # pylint: disable=no-member441        if 'shading' not in kwargs.keys():442            kwargs['shading'] = 'flat'443        YY, XX = np.meshgrid(self.absc[0], self.absc[1])444        plt.pcolormesh(XX, YY, np.array(self.ordi.T), *args, **kwargs)445        plt.autoscale(tight=True)446class Spectrogram(MeasuredSurface):447    pass448class MeasuredErrorField(object):449    ''' A field that hold two abscissa arrays and two ordinate matrices450        Error is the measuredGrid - nominalGrid, which is a vector field451    '''452    def __init__(self, nominalGrid, measuredGrid):453        assert(nominalGrid.ndim == 3)454        self.nomiGrid = nominalGrid455        if measuredGrid.ndim == 4:456            self.measGrid = np.mean(measuredGrid, axis=0)457        elif measuredGrid.ndim == 3:458            self.measGrid = measuredGrid459        else:460            raise Exception('measuredGrid must be dimension 3 (meaned) or 4 (trials)')461    def __call__(self, testVec=None):462        xVec = self.nomiGrid[:, :, 0]463        yVec = self.nomiGrid[:, :, 1]464        uVec = self.measGrid[:, :, 0]465        vVec = self.measGrid[:, :, 1]466        u = interpolate.interp2d(xVec, yVec, uVec, kind='linear')467        v = interpolate.interp2d(xVec, yVec, vVec, kind='linear')468        testU = u(*testVec)[0]469        testV = v(*testVec)[0]470        return np.array([testU, testV])471    def errorAt(self, testVec=None):472        return self(testVec) - testVec473    def invert(self, desiredVec):474        reflectedVec = desiredVec - self.errorAt(desiredVec)475        avgErr = (self.errorAt(desiredVec) + self.errorAt(reflectedVec)) / 2476        commandVec = desiredVec - avgErr477        return commandVec478    def zeroCenteredSquareSize(self):479        ''' Very stupid, just look at corner points480            Returns:481                (tuple(float)): square sides of nominal and measured grids482        '''483        def cornerInd(grid, minOrMax):484            score = np.sum(grid, axis=2)485            cornerFlatInd = np.argmin(score) if minOrMax else np.argmax(score)486            return np.unravel_index(cornerFlatInd, grid.shape[:2])487        def zcSz(grid, corner):488            cornerVec = grid[(corner + (slice(None), ))]489            sqSide = np.min(np.abs(cornerVec))490            return sqSide491        nomiCornerInds = [cornerInd(self.nomiGrid, m) for m in [True, False]]492        nomiSq = np.min([zcSz(self.nomiGrid, nomiCornerInds[i]) for i in range(2)])493        measSq = np.min([zcSz(self.measGrid, nomiCornerInds[i]) for i in range(2)])...

Guassianproblem.py

Source:Guassianproblem.py

1import pandas as pd2import numpy as np3from sres import SRES, _lib4import ctypes as ct5import tellurium as te6from math import pi as PI7import unittest8sres = SRES(2)9def generateData(mu, sigma):10    return np.random.normal(mu, sigma, 10)11EXP_DATA = generateData(5, 0.1)12class Test(unittest.TestCase):13    def setUp(self) -> None:14        pass15    @sres.COST_FUNCTION_CALLBACK16    def test_ctypes_callback_fn_example(self):17        from ctypes import cdll18        libc = cdll.msvcrt19        IntArray5 = ct.c_int * 520        ia = IntArray5(5, 1, 7, 33, 99)21        qsort = libc.qsort22        qsort.restype = None23        CALLBACK_FN = ct.CFUNCTYPE(ct.c_int, ct.POINTER(ct.c_int), ct.POINTER(ct.c_int))24        def py_cb(a, b):25            print("Pycb", a[0], b[0])26            return 027        qsort(ia, len(ia), ct.sizeof(ct.c_int), CALLBACK_FN(py_cb))28    def testPassingDoubleArray(self):29        lb = ct.pointer(sres.DoubleArrayLen2(0.1, 0.1))  # double *lb,30        sres.fakeFun(lb)31    def testPassingDoubleArrayUsingWrapperFn(self):32        lb = sres._makeDoubleArrayPtr([0.1, 0.1])  # double *lb,33        sres.fakeFun(lb)34    def testFunctionPointerInIsolation(self):35        import ctypes as ct36        lib = ct.CDLL("SRES")37        F1_CALLBACK = ct.CFUNCTYPE(None, ct.POINTER(ct.c_double * 2), ct.POINTER(ct.c_double), ct.POINTER(ct.c_double))38        lib.function_that_takes_a_function.argtypes = [39            F1_CALLBACK, ct.POINTER(ct.c_double * 2),40            ct.POINTER(ct.c_double),41            ct.POINTER(ct.c_double)42        ]43        lib.function_that_takes_a_function.restype = None44        def func_to_pass_in(d1, d2, d3):45            print("hello from Python: ")46        # function_that_takes_a_function(func_to_pass_in, sres._makeDoubleArrayPtr([0.1, 0.1]))47        lib.function_that_takes_a_function(F1_CALLBACK(func_to_pass_in), sres._makeDoubleArrayPtr([0.1, 1.2]),48                                           ct.pointer(ct.c_double(4.0)), ct.pointer(ct.c_double(6.0)))49    def testFunctionPointerInIsolationAndUpdateAValue(self):50        import ctypes as ct51        lib = ct.CDLL("SRES")52        F1_FUNCTION_PTR = ct.CFUNCTYPE(None, ct.POINTER(ct.c_double), ct.POINTER(ct.c_double))53        lib.function_that_takes_a_function.argtypes = [54            F1_FUNCTION_PTR, ct.POINTER(ct.c_double), ct.POINTER(ct.c_double)55        ]56        lib.function_that_takes_a_function.restype = None57        def func_to_pass_in(x, y):58            print("From Python: hello from Python: ")59            print("From Python: x, y: ", x.contents, y.contents)60            new_value = x.contents.value + y.contents.value61            new_value_double_ptr = ct.pointer(ct.c_double(new_value))62            ct.memmove(ct.cast(y, ct.c_void_p).value,63                       ct.cast(new_value_double_ptr, ct.c_void_p).value,64                       ct.sizeof(ct.c_double))65        # function_that_takes_a_function(func_to_pass_in, sres._makeDoubleArrayPtr([0.1, 0.1]))66        input = ct.c_double(4.0)67        output = ct.c_double(1.0)68        input_ptr = ct.pointer(input)69        output_ptr = ct.pointer(output)70        lib.function_that_takes_a_function(F1_FUNCTION_PTR(func_to_pass_in), input_ptr, output_ptr)71    def testFunctionPointerInIsolationAndUpdateAValue2(self):72        import ctypes as ct73        lib = ct.CDLL("SRES")74        ESFcnFG_FUNCTION_PTR = ct.CFUNCTYPE(None, ct.POINTER(ct.c_double * 2), ct.POINTER(ct.c_double),75                                            ct.POINTER(ct.c_double))76        lib.function_that_takes_f2.argtypes = [77            ESFcnFG_FUNCTION_PTR, ct.POINTER(ct.c_double), ct.POINTER(ct.c_double), ct.POINTER(ct.c_double)78        ]79        lib.function_that_takes_f2.restype = None80        def func_to_pass_in(x, y, z):81            print("hello from Python: ")82            print("x, y: ", x.contents[0], y.contents)83            new_value = x.contents[0] + x.contents[1] + y.contents.value84            new_value_double_ptr = ct.pointer(ct.c_double(new_value))85            ct.memmove(86                ct.cast(y, ct.c_void_p).value,87                ct.cast(new_value_double_ptr, ct.c_void_p).value,88                ct.sizeof(ct.c_double))89        # function_that_takes_a_function(func_to_pass_in, sres._makeDoubleArrayPtr([0.1, 0.1]))90        input = ct.pointer(ct.c_double(4.0))91        output = ct.pointer(ct.c_double(1.0))92        ignored = ct.pointer(ct.c_double(2.0))93        lib.function_that_takes_f2(ESFcnFG_FUNCTION_PTR(func_to_pass_in), input, output, ignored)94    def test_use_the_problematic_function_pointer_outside_context_of_SRES(self):95        import ctypes as ct96        lib = ct.CDLL("SRES")97        ESfcnFG_TYPE = ct.CFUNCTYPE(None, ct.POINTER(ct.c_double * 2), ct.POINTER(ct.c_double), ct.POINTER(ct.c_double))98        lib.function_that_takes_ESfcnFG.argtypes = [99            ESfcnFG_TYPE100        ]101        lib.function_that_takes_ESfcnFG.restype = None102        def cost_fun(x, f, g):103            print("hello from cost_fun")104            sim = generateData(x.contents[0], x.contents[1])105            cost = 0106            for i in range(10):107                cost += (EXP_DATA[i] - sim[i]) ** 2108            cost_dbl_ptr = ct.pointer(ct.c_double(cost))109            # copy the value from Python to C. If we don't do this, the value gets deleted.110            ct.memmove(ct.cast(f, ct.c_void_p).value, ct.cast(cost_dbl_ptr, ct.c_void_p).value, ct.sizeof(ct.c_double))111        lib.function_that_takes_ESfcnFG(ESfcnFG_TYPE(cost_fun))112    def test_sres_alg_using_dereferencing_pointer_functions(self):113        """114        We need double pointers as input to ESInit but since pointers in ESStep.115        This version attempts to create double pointers in make* functions and then116        dereference them in using deref* function calls. The actual dereferencing117        happens on the C end118        :return:119        """120        sres = SRES()121        param = sres._makeESParameter()122        stats = sres._makeESStatistics()123        population = sres._makeESPopulation()124        trsfm = sres._getTransformFun(2)125        # https://stackoverflow.com/questions/51131433/how-to-pass-lists-into-a-ctypes-function-on-python/51132594126        seed = ct.c_int32(0)127        gamma = ct.c_double(0.85)128        alpha = ct.c_double(0.2)129        retry = ct.c_int32(10)130        es = ct.c_int32(1)131        constraint = ct.c_int32(0)132        dim = ct.c_int32(2)133        varphi = ct.c_double(1.0)134        ngen = ct.c_int32(50)135        miu = 5136        lambda_ = 5137        # How to verify that this works?138        ub = ct.pointer(sres.DoubleArrayLen2(10.0, 10.0))  # double *ub,139        lb = ct.pointer(sres.DoubleArrayLen2(0.1, 0.1))  # double *lb,140        ESfcnFG_TYPE = ct.CFUNCTYPE(141            None,142            ct.POINTER(ct.c_double * 2),143            ct.POINTER(ct.c_double),144            ct.POINTER(ct.c_double)145        )146        def cost_fun(x, f, g):147            print("From Python : Hello")148            sim = generateData(x.contents[0], x.contents[1])149            cost = 0150            for i in range(10):151                cost += (EXP_DATA[i] - sim[i]) ** 2152            cost_dbl_ptr = ct.pointer(ct.c_double(cost))153            # copy the value from Python to C. If we don't do this, the value gets deleted.154            ct.memmove(ct.cast(f, ct.c_void_p).value, ct.cast(cost_dbl_ptr, ct.c_void_p).value, ct.sizeof(ct.c_double))155        print("From Python: Call to sres.ESInitial")156        sres.ESInitial(157            seed,158            param,159            trsfm,160            ESfcnFG_TYPE(cost_fun),161            es,162            constraint,163            dim,164            ub,165            lb,166            miu,167            lambda_,168            ngen,169            gamma,170            alpha,171            varphi,172            retry,173            population,174            stats175        )176        print("From Python: Call to sres.ESInitial has finished")177        curgen = 0178        while curgen < 10:179            print("Current gen: ", curgen)180            print("From Python: Call to sres.ESStep")181            sres._ESStep(182                sres.derefESPopulation(population),183                sres.derefESParameter(param),184                sres.derefESStatistics(stats),185                ct.c_double(0.45)186            )187            print("From Python: Call to sres.ESStep has finished")188            curgen += 1189        # sres.ESDeInitial(190        #     sres.derefESParameter(esparam),191        #     sres.derefESPopulation(pop),192        #     sres.derefESStatistics(stats)193        # )194        # sres.freeCostFunPtr(costFun)195        # sres.freeTransformFun(trsf)196    def test_sres_alg_using_version_of_ESStep_that_takes_double_pointers(self):197        """198        We need double pointers as input to ESInit but since pointers in ESStep. In attempt to debug the199        memory related issues in test_sres_alg_using_dereferencing_pointer_functions we rewrote the200        ESStep function in C so that it takes double pointers.201        :return:202        """203        sres = SRES()204        param = sres._makeESParameter()205        stats = sres._makeESStatistics()206        population = sres._makeESPopulation()207        trsfm = sres._getTransformFun(2)208        # https://stackoverflow.com/questions/51131433/how-to-pass-lists-into-a-ctypes-function-on-python/51132594209        seed = ct.c_int32(0)210        gamma = ct.c_double(0.85)211        alpha = ct.c_double(0.2)212        retry = ct.c_int32(10)213        es = ct.c_int32(1)214        constraint = ct.c_int32(0)215        dim = ct.c_int32(2)216        varphi = ct.c_double(1.0)217        ngen = ct.c_int32(50)218        miu = 5219        lambda_ = 5220        # How to verify that this works?221        ub = ct.pointer(sres.DoubleArrayLen2(10.0, 10.0))  # double *ub,222        lb = ct.pointer(sres.DoubleArrayLen2(0.01, 0.01))  # double *lb,223        ESfcnFG_TYPE = ct.CFUNCTYPE(224            None,225            ct.POINTER(ct.c_double * 2),226            ct.POINTER(ct.c_double),227            ct.POINTER(ct.c_double)228        )229        def cost_fun(x, f, g):230            sim = generateData(x.contents[0], x.contents[1])231            cost = 0232            for i in range(10):233                cost += (EXP_DATA[i] - sim[i]) ** 2234            cost_dbl_ptr = ct.pointer(ct.c_double(cost))235            # copy the value from Python to C. If we don't do this, the value gets deleted.236            ct.memmove(ct.cast(f, ct.c_void_p).value, ct.cast(cost_dbl_ptr, ct.c_void_p).value, ct.sizeof(ct.c_double))237        f = ESfcnFG_TYPE(cost_fun)238        sres.ESInitial(239            seed,240            param,241            trsfm,242            f,243            es,244            constraint,245            dim,246            ub,247            lb,248            miu,249            lambda_,250            ngen,251            gamma,252            alpha,253            varphi,254            retry,255            population,256            stats257        )258        curgen = 0259        while curgen < 1000:260            sres._ESStep(261                sres.derefESPopulation(population),262                sres.derefESParameter(param),263                sres.derefESStatistics(stats),264                0.45265            )266            curgen += 1267        # sres.ESDeInitial(268        #     sres.derefESParameter(esparam),269        #     sres.derefESPopulation(pop),270        #     sres.derefESStatistics(stats)271        # )272        # sres.freeCostFunPtr(costFun)273        # sres.freeTransformFun(trsf)274    def test_sres(self):275        sres = SRES(dim=2)276        @sres.COST_FUNCTION_CALLBACK277        def cost_fun(x, f, g):278            sim = generateData(x.contents[0], x.contents[1])279            cost = 0280            for i in range(10):281                cost += (EXP_DATA[i] - sim[i]) ** 2282            f.contents.value = cost283        sres.cost_function = cost_fun284if __name__ == "__main__":285    pass286    # check that passing in double array works287    # testPassingDoubleArray()288    # y = ct.POINTER(ct.c_double)289    # print(y, y())290    # do_sres()291    # ct callback example from docs292    # Use qsort from python:...

item.py

Source:item.py

...55            elif "id" in stage:56                name = stage["id"]57            stages.append("{:d}: {:s}".format(i + 1, name))58        # This needs to be a function or skipif breaks59        def fakefun():60            pass61        fakefun.__doc__ = self.name + ":\n" + "\n".join(stages)62        return fakefun63    @property64    def _obj(self):65        return self.obj66    def add_markers(self, pytest_marks):67        for pm in pytest_marks:68            if pm.name == "usefixtures":69                if (70                    not isinstance(pm.mark.args, (list, tuple))71                    or len(pm.mark.args) == 072                ):73                    logger.error(...

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