Python numpy.fmin() Examples

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def optimize_transformation(self):
        """
        returns optimal transformation
        """
        x0 = [1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]
        self._add_params_trafo_stack_(x0)
        print("starts optimizing...")
        np.fmin(self._calc_min_func_, x0, full_output=1, maxiter=2000)
        # self.alpha1=self.multiplier_x*self.alpha1
        # self.beta1=self.multiplier_x*self.beta1
        # self.gamma1=self.multiplier_x*self.gamma1
        # self.alpha2=self.multiplier_y*self.alpha2
        # self.beta2=self.multiplier_y*self.beta2
        # self.gamma2=self.multiplier_y*self.gamma2
        self._set_transformation_to_all_axis_()
        # self._calc_bounding_box_()
        # self._trafo_to_paper_() 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def predict_movement(self, data, epsilon):
        rand_val = np.random.random(data.shape[0])
        # q_actions = self.model.predict(data)
        p_actions = self.model_policy.predict(data)
        opt_policy_orig = np.argmax(np.abs(p_actions), axis=-1)
        opt_policy = 1.0 * opt_policy_orig
        opt_policy[rand_val < epsilon] = np.random.randint(0, self.action_size, size=(np.sum(rand_val < epsilon)))

        # store the qvalue_evolution (lots of computation time maybe here)
        tmp = np.zeros((data.shape[0], self.action_size))
        tmp[np.arange(data.shape[0]), opt_policy_orig] = 1.0
        q_actions0 = self.model_Q.predict([data, tmp])
        q_actions2 = self.model_Q2.predict([data, tmp])
        q_actions = np.fmin(q_actions0, q_actions2).reshape(-1)
        self.qvalue_evolution = np.concatenate((self.qvalue_evolution, q_actions))
        # above is not mandatory for predicting a movement so, might need to be moved somewhere else...

        opt_policy = opt_policy.astype(np.int)
        return opt_policy, p_actions[:, opt_policy] 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def _fof_local(layout, pos, boxsize, ll, comm):
    from kdcount import cluster

    N = len(pos)

    pos = layout.exchange(pos)
    if boxsize is not None:
        pos %= boxsize
    data = cluster.dataset(pos, boxsize=boxsize)
    fof = cluster.fof(data, linking_length=ll, np=0)
    labels = fof.labels
    del fof

    PID = numpy.arange(N, dtype='intp')
    PID += numpy.sum(comm.allgather(N)[:comm.rank], dtype='intp')

    PID = layout.exchange(PID)
    # initialize global labels
    minid = equiv_class(labels, PID, op=numpy.fmin)[labels]

    return minid 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmin.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 0)
            assert_equal(func(tmp2), 0)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 1)
            assert_equal(func(tmp2), 1) 

def compute_corr_significance(r, N):
    """ Compute statistical significance for a pearson correlation between
        two xarray objects.

    Parameters
    ----------
    r : `xarray.DataArray` object
        correlation coefficient between two time series.

    N : int
        length of time series being correlated.

    Returns
    -------
    pval : float
        p value for pearson correlation.

    """
    df = N - 2
    t_squared = r ** 2 * (df / ((1.0 - r) * (1.0 + r)))
    # method used in scipy, where `np.fmin` constrains values to be
    # below 1 due to errors in floating point arithmetic.
    pval = special.betainc(0.5 * df, 0.5, np.fmin(df / (df + t_squared), 1.0))
    return xr.DataArray(pval, coords=t_squared.coords, dims=t_squared.dims) 

def __init__(self, func, constraint_weights, constraint_rhs, constraint_check=None, return_nan=True, verify=True):
        assert isinstance(func, TestFunction)
        assert len(constraint_weights) == len(constraint_rhs)
        super(Constrainer, self).__init__(func.dim, verify)
        self.bounds, self.min_loc, self.fmax, self.fmin = func.bounds, func.min_loc, func.fmax, func.fmin
        self.func = func
        self.constraint_weights = constraint_weights
        self.constraint_rhs = constraint_rhs
        self.return_nan = return_nan
        self.classifiers = list(set(self.classifiers) | set(['constraint']))
        if constraint_check is not None:
            self.constraint_check = constraint_check
        else:
            self.constraint_check = Constrainer.default_constraint_check 

def __init__(self, func, res, verify=True):
        assert isinstance(func, TestFunction)
        if res <= 0:
            raise ValueError('Resolution level must be positive, level={0}'.format(res))
        super(Discretizer, self).__init__(func.dim, verify)
        self.bounds, self.min_loc = func.bounds, func.min_loc
        self.res = res
        self.fmax = numpy.floor(self.res * func.fmax) / self.res
        self.fmin = numpy.floor(self.res * func.fmin) / self.res
        self.func = func
        self.classifiers = list(set(self.classifiers) | set(['discrete'])) 

def __init__(self, func, fail_indicator, return_nan=True, verify=True):
        assert isinstance(func, TestFunction)
        super(Failifier, self).__init__(func.dim, verify)
        self.bounds, self.min_loc, self.fmax, self.fmin = func.bounds, func.min_loc, func.fmax, func.fmin
        self.func = func
        self.fail_indicator = fail_indicator
        self.return_nan = return_nan
        self.classifiers = list(set(self.classifiers) | set(['failure'])) 

def IOU_numpy(rects1, rect2):
    #intersection = np.fmin(np.zeros((rects1.shape[0],1))
    (d, n) = rects1.shape
    x1s = np.fmax(rects1[:,0], rect2[0])
    x2s = np.fmin(rects1[:,2], rect2[2])
    y1s = np.fmax(rects1[:,1], rect2[1])
    y2s = np.fmin(rects1[:,3], rect2[3])
    ws = np.fmax(x2s - x1s, 0)
    hs = np.fmax(y2s - y1s, 0)
    intersection = ws * hs
    rects1Area = (rects1[:,2] - rects1[:,0]) * (rects1[:,3] - rects1[:,1])
    rect2Area = (rect2[2] - rect2[0]) * (rect2[3] - rect2[1])
    union = np.fmax(rects1Area + rect2Area - intersection, .00001)
    return intersection * 1.0 / union 

def run(self):
        """
        Compute the density proxy. This attaches the following attribute:

        - :attr:`density`

        Attributes
        ----------
        density : array_like, length: :attr:`size`
            a unit-less, proxy density value for each object on the local
            rank. This is computed as the inverse cube of the distance
            to the closest, nearest neighbor
        """

        # do the domain decomposition
        Np = split_size_3d(self.comm.size)
        edges = [numpy.linspace(0, self.attrs['BoxSize'][d], Np[d] + 1, endpoint=True) for d in range(3)]
        domain = GridND(comm=self.comm, periodic=True, edges=edges)

        # read all position and exchange
        pos = self._source.compute(self._source['Position'])
        layout = domain.decompose(pos, smoothing=self.attrs['margin'] * self.attrs['meansep'])
        xpos = layout.exchange(pos)

        # wait for scipy 0.19.1
        assert all(self.attrs['BoxSize'] == self.attrs['BoxSize'][0])
        xpos[...] /= self.attrs['BoxSize']
        xpos %= 1

        # KDTree
        tree = KDTree(xpos, boxsize=1.0)
        d, i = tree.query(xpos, k=[8])
        d = d[:, 0]

        # gather back to original root, taking the minimum distance
        d = layout.gather(d, mode=numpy.fmin)
        self.density = 1 / (d ** 3 * self.attrs['BoxSize'].prod()) 

def _fof_merge(layout, minid, comm):
    # generate global halo id

    while True:
        # merge, if a particle belongs to several ranks
        # use the global label of the minimal
        minid_new = layout.gather(minid, mode=numpy.fmin)
        minid_new = layout.exchange(minid_new)

        # on my rank, these particles have been merged
        merged = minid_new != minid
        # if no rank has merged any, we are done
        # gl is the global label (albeit with some holes)
        total = comm.allreduce(merged.sum())

        if total == 0:
            del minid_new
            break
        old = minid[merged]
        new = minid_new[merged]
        arg = old.argsort()
        new = new[arg]
        old = old[arg]
        replacesorted(minid, old, new, out=minid)

    minid = layout.gather(minid, mode=numpy.fmin)
    return minid 

def count_overlapping_boxes(rects1, rect2, overlapThresh=.001):
    if rects1.shape[1] == 0:
        return 0
    x1s = np.fmax(rects1[:,0], rect2[0])
    x2s = np.fmin(rects1[:,2], rect2[2])
    y1s = np.fmax(rects1[:,1], rect2[1])
    y2s = np.fmin(rects1[:,3], rect2[3])
    ws = np.fmax(x2s - x1s, 0)
    hs = np.fmax(y2s - y1s, 0)
    intersection = ws * hs
    rects1Area = (rects1[:,2] - rects1[:,0]) * (rects1[:,3] - rects1[:,1])
    rect2Area = (rect2[2] - rect2[0]) * (rect2[3] - rect2[1])
    union = np.fmax(rects1Area + rect2Area - intersection, .00001)
    ious = intersection * 1.0 / union
    return np.sum(ious > overlapThresh) 

def get_overlapping_boxes(rects1, rect2, overlapThresh=.001):
    x1s = np.fmax(rects1[:,0], rect2[0])
    x2s = np.fmin(rects1[:,2], rect2[2])
    y1s = np.fmax(rects1[:,1], rect2[1])
    y2s = np.fmin(rects1[:,3], rect2[3])
    ws = np.fmax(x2s - x1s, 0)
    hs = np.fmax(y2s - y1s, 0)
    intersection = ws * hs
    rects1Area = (rects1[:,2] - rects1[:,0]) * (rects1[:,3] - rects1[:,1])
    rect2Area = (rect2[2] - rect2[0]) * (rect2[3] - rect2[1])
    union = np.fmax(rects1Area + rect2Area - intersection, .00001)
    ious = intersection * 1.0 / union
    rects1[:,4] = ious
    rects1 = rects1[ious > overlapThresh, :]
    return rects1 

def IOU_lists(rects1, rects2):
    (d, n) = rects1.shape
    x1s = np.fmax(rects1[:,0], rects2[:,0])
    x2s = np.fmin(rects1[:,2], rects2[:,2])
    y1s = np.fmax(rects1[:,1], rects2[:,1])
    y2s = np.fmin(rects1[:,3], rects2[:,3])
    ws = np.fmax(x2s - x1s, 0)
    hs = np.fmax(y2s - y1s, 0)
    intersection = ws * hs
    rects1Area = (rects1[:,2] - rects1[:,0]) * (rects1[:,3] - rects1[:,1])
    rects2Area = (rects2[:,2] - rects2[:,0]) * (rects2[:,3] - rects2[:,1])
    union = np.fmax(rects1Area + rects2Area - intersection, .00001)
    return intersection * 1.0 / union

# Rectangles are [x1, y1, x2, y2] 

def test_reduce_complex(self):
        assert_equal(np.fmin.reduce([1, 2j]), 2j)
        assert_equal(np.fmin.reduce([1+3j, 2j]), 2j)