_loess.c

/* Generated by Pyrex 0.9.5.1a on Sun Mar 25 18:15:15 2007 */

#include "Python.h"
#include "structmember.h"
#ifndef PY_LONG_LONG
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#ifdef __cplusplus
#define __PYX_EXTERN_C extern "C"
#else
#define __PYX_EXTERN_C extern
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__PYX_EXTERN_C double pow(double, double);
#include "stdlib.h"
#include "numpy/arrayobject.h"
#include "loess.h"
#include "cloess.h"


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/* Declarations from c_python */


/* Declarations from c_numpy */

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static PyTypeObject *__pyx_ptype_7c_numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_7c_numpy_broadcast = 0;

/* Declarations from c_loess */


/* Declarations from _loess */


struct __pyx_obj_6_loess_loess_inputs {
  PyObject_HEAD
  loess_inputs (*_base);
  PyArrayObject *w_ndr;
  PyArrayObject *x;
  PyArrayObject *y;
  PyArrayObject *masked;
  PyArrayObject *x_eff;
  PyArrayObject *y_eff;
  int nobs;
  int npar;
};


struct __pyx_obj_6_loess_loess_control {
  PyObject_HEAD
  loess_control (*_base);
};


struct __pyx_obj_6_loess_loess_kd_tree {
  PyObject_HEAD
  loess_kd_tree (*_base);
};


struct __pyx_obj_6_loess_loess_model {
  PyObject_HEAD
  struct __pyx_vtabstruct_6_loess_loess_model *__pyx_vtab;
  loess_model (*_base);
  long npar;
};

struct __pyx_vtabstruct_6_loess_loess_model {
  PyObject *((*setup)(struct __pyx_obj_6_loess_loess_model *,loess_model (*),long ));
};
static struct __pyx_vtabstruct_6_loess_loess_model *__pyx_vtabptr_6_loess_loess_model;


struct __pyx_obj_6_loess_loess_outputs {
  PyObject_HEAD
  struct __pyx_vtabstruct_6_loess_loess_outputs *__pyx_vtab;
  loess_outputs (*_base);
  long nobs;
  long npar;
  int activated;
};

struct __pyx_vtabstruct_6_loess_loess_outputs {
  PyObject *((*setup)(struct __pyx_obj_6_loess_loess_outputs *,loess_outputs (*),long ,long ));
};
static struct __pyx_vtabstruct_6_loess_loess_outputs *__pyx_vtabptr_6_loess_loess_outputs;


struct __pyx_obj_6_loess_conf_intervals {
  PyObject_HEAD
  struct __pyx_vtabstruct_6_loess_conf_intervals *__pyx_vtab;
  conf_inv _base;
  PyArrayObject *lower;
  PyArrayObject *fit;
  PyArrayObject *upper;
};

struct __pyx_vtabstruct_6_loess_conf_intervals {
  PyObject *((*setup)(struct __pyx_obj_6_loess_conf_intervals *,conf_inv ,long ));
};
static struct __pyx_vtabstruct_6_loess_conf_intervals *__pyx_vtabptr_6_loess_conf_intervals;


struct __pyx_obj_6_loess_loess_predicted {
  PyObject_HEAD
  struct __pyx_vtabstruct_6_loess_loess_predicted *__pyx_vtab;
  prediction _base;
  long nest;
  struct __pyx_obj_6_loess_conf_intervals *confidence_intervals;
};

struct __pyx_vtabstruct_6_loess_loess_predicted {
  PyObject *((*setup)(struct __pyx_obj_6_loess_loess_predicted *,prediction ,long ));
};
static struct __pyx_vtabstruct_6_loess_loess_predicted *__pyx_vtabptr_6_loess_loess_predicted;


struct __pyx_obj_6_loess_loess {
  PyObject_HEAD
  loess _base;
  struct __pyx_obj_6_loess_loess_inputs *inputs;
  struct __pyx_obj_6_loess_loess_model *model;
  struct __pyx_obj_6_loess_loess_control *control;
  struct __pyx_obj_6_loess_loess_kd_tree *kd_tree;
  struct __pyx_obj_6_loess_loess_outputs *outputs;
  struct __pyx_obj_6_loess_loess_predicted *predicted;
};


struct __pyx_obj_6_loess_anova {
  PyObject_HEAD
  double dfn;
  double dfd;
  double F_value;
  double Pr_F;
};

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static PyTypeObject *__pyx_ptype_6_loess_loess_outputs = 0;
static PyTypeObject *__pyx_ptype_6_loess_conf_intervals = 0;
static PyTypeObject *__pyx_ptype_6_loess_loess_predicted = 0;
static PyTypeObject *__pyx_ptype_6_loess_loess = 0;
static PyTypeObject *__pyx_ptype_6_loess_anova = 0;
static PyObject *__pyx_k9;
static PyObject *__pyx_k10;
static PyObject *__pyx_k11;
static PyObject *(__pyx_f_6_loess_floatarray_from_data(int ,int ,double (*))); /*proto*/
static PyObject *(__pyx_f_6_loess_boolarray_from_data(int ,int ,int (*))); /*proto*/


/* Implementation of _loess */

static char (__pyx_k1[]) = "A (n,) ndarray of weights to be given to individual observations in the \n        sum of squared residuals that forms the local fitting criterion. If not\n        None, the weights should be non negative. If the different observations\n        have non-equal variances, the weights should be inversely proportional \n        to the variances.\n        By default, an unweighted fit is carried out (all the weights are one).\n        ";
static char (__pyx_k2[]) = "\n    surface : string [\"interpolate\"]\n        Determines whether the fitted surface is computed directly at all points\n        (\"direct\") or whether an interpolation method is used (\"interpolate\").\n        The default (\"interpolate\") is what most users should use unless special \n        circumstances warrant.\n        ";
static char (__pyx_k3[]) = "\n    statistics : string [\"approximate\"]\n        Determines whether the statistical quantities are computed exactly \n        (\"exact\") or approximately (\"approximate\"). \"exact\" should only be used \n        for testing the approximation in statistical development and is not meant \n        for routine usage because computation time can be horrendous.\n        ";
static char (__pyx_k4[]) = "\n    trace_hat : string [\"wait.to.decide\"]\n        Determines how the trace of the hat matrix should be computed. The hat\n        matrix is used in the computation of the statistical quantities. \n        If \"exact\", an exact computation is done; this could be slow when the\n        number of observations n becomes large. If \"wait.to.decide\" is selected, \n        then a default is \"exact\" for n < 500 and \"approximate\" otherwise. \n        This option is only useful when the fitted surface is interpolated. If  \n        surface is \"exact\", an exact computation is always done for the trace. \n        Setting trace_hat to \"approximate\" for large dataset will substantially \n        reduce the computation time.\n        ";
static char (__pyx_k5[]) = "\n    iterations : integer\n        Number of iterations of the robust fitting method. If the family is \n        \"gaussian\", the number of iterations is set to 0.\n        ";
static char (__pyx_k6[]) = "\n    cell : integer\n        Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),\n        where n is the number of observations, and span the smoothing parameter.\n        Then, a cell is further divided if the number of observations within it \n        is greater than or equal to k. This option is only used if the surface \n        is interpolated.\n        ";
static char (__pyx_k8[]) = "\n        \n\n    newdata : ndarray\n        The (m,p) array of independent variables where the surface must be estimated.\n    values : ndarray\n        The (m,) ndarray of loess values evaluated at newdata\n    stderr : ndarray\n        The (m,) ndarray of the estimates of the standard error on the estimated\n        values.\n    residual_scale : float\n        Estimate of the scale of the residuals\n    df : integer\n        Degrees of freedom of the t-distribution used to compute pointwise \n        confidence intervals for the evaluated surface.\n    nest : integer\n        Number of new observations.\n       \n        \n";

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  }
}

static PyObject *__pyx_getprop_6_loess_13loess_control_iterations(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_control_10iterations___get__(o);
}

static int __pyx_setprop_6_loess_13loess_control_iterations(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_13loess_control_10iterations___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_13loess_control_cell(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_control_4cell___get__(o);
}

static int __pyx_setprop_6_loess_13loess_control_cell(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_13loess_control_4cell___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static struct PyMethodDef __pyx_methods_6_loess_loess_control[] = {
  {"update", (PyCFunction)__pyx_f_6_loess_13loess_control_update, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_13loess_control_update},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_6_loess_loess_control[] = {
  {"surface", __pyx_getprop_6_loess_13loess_control_surface, __pyx_setprop_6_loess_13loess_control_surface, __pyx_k2, 0},
  {"statistics", __pyx_getprop_6_loess_13loess_control_statistics, __pyx_setprop_6_loess_13loess_control_statistics, __pyx_k3, 0},
  {"trace_hat", __pyx_getprop_6_loess_13loess_control_trace_hat, __pyx_setprop_6_loess_13loess_control_trace_hat, __pyx_k4, 0},
  {"iterations", __pyx_getprop_6_loess_13loess_control_iterations, __pyx_setprop_6_loess_13loess_control_iterations, __pyx_k5, 0},
  {"cell", __pyx_getprop_6_loess_13loess_control_cell, __pyx_setprop_6_loess_13loess_control_cell, __pyx_k6, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess_control = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess_control = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess_control = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess_control = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess_control = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess_control", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess_control), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess_control, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_loess_control, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess_control, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess_control, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_f_6_loess_13loess_control___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess_control, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Loess control parameters.\n    \n:IVariables:\n    surface : string [\"interpolate\"]\n        Determines whether the fitted surface is computed directly at all points\n        (\"direct\") or whether an interpolation method is used (\"interpolate\").\n        The default (\"interpolate\") is what most users should use unless special \n        circumstances warrant.\n    statistics : string [\"approximate\"]\n        Determines whether the statistical quantities are computed exactly \n        (\"exact\") or approximately (\"approximate\"). \"exact\" should only be used \n        for testing the approximation in statistical development and is not meant \n        for routine usage because computation time can be horrendous.\n    trace_hat : string [\"wait.to.decide\"]\n        Determines how the trace of the hat matrix should be computed. The hat\n        matrix is used in the computation of the statistical quantities. \n        If \"exact\", an exact computation is done; this could be slow when the\n        number of observations n becomes large. If \"wait.to.decide\" is selected, \n        then a default is \"exact\" for n < 500 and \"approximate\" otherwise. \n        This option is only useful when the fitted surface is interpolated. If  \n        surface is \"exact\", an exact computation is always done for the trace. \n        Setting trace_hat to \"approximate\" for large dataset will substantially \n        reduce the computation time.\n    iterations : integer\n        Number of iterations of the robust fitting method. If the family is \n        \"gaussian\", the number of iterations is set to 0.\n    cell : integer\n        Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),\n        where n is the number of observations, and span the smoothing parameter.\n        Then, a cell is further divided if the number of observations within it \n        is greater than or equal to k. This option is only used if the surface \n        is interpolated.\n    \n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess_control, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess_control, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess_control, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_6_loess_loess_control, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess_control, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};

static PyObject *__pyx_tp_new_6_loess_loess_kd_tree(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  return o;
}

static void __pyx_tp_dealloc_6_loess_loess_kd_tree(PyObject *o) {
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_loess_kd_tree(PyObject *o, visitproc v, void *a) {
  return 0;
}

static int __pyx_tp_clear_6_loess_loess_kd_tree(PyObject *o) {
  return 0;
}

static struct PyMethodDef __pyx_methods_6_loess_loess_kd_tree[] = {
  {0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess_kd_tree = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess_kd_tree = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess_kd_tree = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess_kd_tree = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess_kd_tree = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess_kd_tree", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess_kd_tree), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess_kd_tree, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_loess_kd_tree, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess_kd_tree, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess_kd_tree, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess_kd_tree, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess_kd_tree, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess_kd_tree, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess_kd_tree, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess_kd_tree, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};
static struct __pyx_vtabstruct_6_loess_loess_model __pyx_vtable_6_loess_loess_model;

static PyObject *__pyx_tp_new_6_loess_loess_model(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  struct __pyx_obj_6_loess_loess_model *p = (struct __pyx_obj_6_loess_loess_model *)o;
  *(struct __pyx_vtabstruct_6_loess_loess_model **)&p->__pyx_vtab = __pyx_vtabptr_6_loess_loess_model;
  return o;
}

static void __pyx_tp_dealloc_6_loess_loess_model(PyObject *o) {
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_loess_model(PyObject *o, visitproc v, void *a) {
  return 0;
}

static int __pyx_tp_clear_6_loess_loess_model(PyObject *o) {
  return 0;
}

static PyObject *__pyx_getprop_6_loess_11loess_model_normalize(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_9normalize___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_normalize(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_9normalize___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_11loess_model_span(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_4span___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_span(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_4span___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_11loess_model_degree(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_6degree___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_degree(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_6degree___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_11loess_model_family(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_6family___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_family(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_6family___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_11loess_model_parametric_flags(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_16parametric_flags___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_parametric_flags(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_16parametric_flags___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static PyObject *__pyx_getprop_6_loess_11loess_model_drop_square_flags(PyObject *o, void *x) {
  return __pyx_f_6_loess_11loess_model_17drop_square_flags___get__(o);
}

static int __pyx_setprop_6_loess_11loess_model_drop_square_flags(PyObject *o, PyObject *v, void *x) {
  if (v) {
    return __pyx_f_6_loess_11loess_model_17drop_square_flags___set__(o, v);
  }
  else {
    PyErr_SetString(PyExc_NotImplementedError, "__del__");
    return -1;
  }
}

static struct PyMethodDef __pyx_methods_6_loess_loess_model[] = {
  {"update", (PyCFunction)__pyx_f_6_loess_11loess_model_update, METH_VARARGS|METH_KEYWORDS, 0},
  {0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_6_loess_loess_model[] = {
  {"normalize", __pyx_getprop_6_loess_11loess_model_normalize, __pyx_setprop_6_loess_11loess_model_normalize, 0, 0},
  {"span", __pyx_getprop_6_loess_11loess_model_span, __pyx_setprop_6_loess_11loess_model_span, 0, 0},
  {"degree", __pyx_getprop_6_loess_11loess_model_degree, __pyx_setprop_6_loess_11loess_model_degree, 0, 0},
  {"family", __pyx_getprop_6_loess_11loess_model_family, __pyx_setprop_6_loess_11loess_model_family, 0, 0},
  {"parametric_flags", __pyx_getprop_6_loess_11loess_model_parametric_flags, __pyx_setprop_6_loess_11loess_model_parametric_flags, 0, 0},
  {"drop_square_flags", __pyx_getprop_6_loess_11loess_model_drop_square_flags, __pyx_setprop_6_loess_11loess_model_drop_square_flags, 0, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess_model = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess_model = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess_model = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess_model = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess_model = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess_model", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess_model), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess_model, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  __pyx_f_6_loess_11loess_model___repr__, /*tp_repr*/
  &__pyx_tp_as_number_loess_model, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess_model, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess_model, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_f_6_loess_11loess_model___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess_model, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "loess_model contains parameters required for a loess fit.\n    \n:IVariables:\n    normalize : boolean [True]\n        Determines whether the independent variables should be normalized.  \n        If True, the normalization is performed by setting the 10% trimmed \n        standard deviation to one. If False, no normalization is carried out. \n        This option is only useful for more than one variable. For spatial\n        coordinates predictors or variables with a common scale, it should be \n        set to False.\n    span : float [0.75]\n        Smoothing factor, as a fraction of the number of points to take into\n        account. \n    degree : integer [2]\n        Overall degree of locally-fitted polynomial. 1 is locally-linear \n        fitting and 2 is locally-quadratic fitting.  Degree should be 2 at most.\n    family : string [\"gaussian\"]\n        Determines the assumed distribution of the errors. The values are \n        \"gaussian\" or \"symmetric\". If \"gaussian\" is selected, the fit is \n        performed with least-squares. If \"symmetric\" is selected, the fit\n        is performed robustly by redescending M-estimators.\n    parametric_flags : sequence [ [False]*p ]\n        Indicates which independent variables should be conditionally-parametric\n        (if there are two or more independent variables). The argument should be\n        a sequence of booleans, with the same size as the number of independent \n        variables, specified in the order of the predictor group ordered in x. \n        Note: elements of the sequence cannot be modified individually: the whole\n        sequence must be given.\n    drop_square : sequence [ [False]* p]\n        When there are two or more independent variables and when a 2nd order\n        polynomial is used, \"drop_square_flags\" specifies those numeric predictors \n        whose squares should be dropped from the set of fitting variables. \n        The method of specification is the same as for parametric.  \n        Note: elements of the sequence cannot be modified individually: the whole\n        sequence must be given.\n\n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess_model, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess_model, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess_model, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_6_loess_loess_model, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess_model, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};
static struct __pyx_vtabstruct_6_loess_loess_outputs __pyx_vtable_6_loess_loess_outputs;

static PyObject *__pyx_tp_new_6_loess_loess_outputs(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  struct __pyx_obj_6_loess_loess_outputs *p = (struct __pyx_obj_6_loess_loess_outputs *)o;
  *(struct __pyx_vtabstruct_6_loess_loess_outputs **)&p->__pyx_vtab = __pyx_vtabptr_6_loess_loess_outputs;
  return o;
}

static void __pyx_tp_dealloc_6_loess_loess_outputs(PyObject *o) {
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_loess_outputs(PyObject *o, visitproc v, void *a) {
  return 0;
}

static int __pyx_tp_clear_6_loess_loess_outputs(PyObject *o) {
  return 0;
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_fitted_values(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_13fitted_values___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_fitted_residuals(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_16fitted_residuals___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_pseudovalues(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_12pseudovalues___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_diagonal(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_8diagonal___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_robust(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_6robust___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_divisor(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_7divisor___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_enp(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_3enp___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_s(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_1s___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_one_delta(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_9one_delta___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_two_delta(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_9two_delta___get__(o);
}

static PyObject *__pyx_getprop_6_loess_13loess_outputs_trace_hat(PyObject *o, void *x) {
  return __pyx_f_6_loess_13loess_outputs_9trace_hat___get__(o);
}

static struct PyMethodDef __pyx_methods_6_loess_loess_outputs[] = {
  {0, 0, 0, 0}
};

static struct PyMemberDef __pyx_members_6_loess_loess_outputs[] = {
  {"activated", T_INT, offsetof(struct __pyx_obj_6_loess_loess_outputs, activated), READONLY, 0},
  {0, 0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_6_loess_loess_outputs[] = {
  {"fitted_values", __pyx_getprop_6_loess_13loess_outputs_fitted_values, 0, 0, 0},
  {"fitted_residuals", __pyx_getprop_6_loess_13loess_outputs_fitted_residuals, 0, 0, 0},
  {"pseudovalues", __pyx_getprop_6_loess_13loess_outputs_pseudovalues, 0, 0, 0},
  {"diagonal", __pyx_getprop_6_loess_13loess_outputs_diagonal, 0, 0, 0},
  {"robust", __pyx_getprop_6_loess_13loess_outputs_robust, 0, 0, 0},
  {"divisor", __pyx_getprop_6_loess_13loess_outputs_divisor, 0, 0, 0},
  {"enp", __pyx_getprop_6_loess_13loess_outputs_enp, 0, 0, 0},
  {"s", __pyx_getprop_6_loess_13loess_outputs_s, 0, 0, 0},
  {"one_delta", __pyx_getprop_6_loess_13loess_outputs_one_delta, 0, 0, 0},
  {"two_delta", __pyx_getprop_6_loess_13loess_outputs_two_delta, 0, 0, 0},
  {"trace_hat", __pyx_getprop_6_loess_13loess_outputs_trace_hat, 0, 0, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess_outputs = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess_outputs = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess_outputs = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess_outputs = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess_outputs = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess_outputs", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess_outputs), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess_outputs, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_loess_outputs, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess_outputs, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess_outputs, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_f_6_loess_13loess_outputs___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess_outputs, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Outputs of a loess fit. This object is automatically created with empty\nvalues when a new loess object is instantiated. The object gets filled when the \nloess.fit() method is called.\n    \n:IVariables:\n    fitted_values : ndarray\n        The (n,) ndarray of fitted values.\n    fitted_residuals : ndarray\n        The (n,) ndarray of fitted residuals (observations - fitted values).\n    enp : float\n        Equivalent number of parameters.\n    s : float\n        Estimate of the scale of residuals.\n    one_delta: float\n        Statistical parameter used in the computation of standard errors.\n    two_delta : float\n        Statistical parameter used in the computation of standard errors.\n    pseudovalues : ndarray\n        The (n,) ndarray of adjusted values of the response when robust estimation \n        is used.\n    trace_hat : float    \n        Trace of the operator hat matrix.\n    diagonal : ndarray\n        Diagonal of the operator hat matrix.\n    robust : ndarray\n        The (n,) ndarray of robustness weights for robust fitting.\n    divisor : ndarray\n        The (p,) array of normalization divisors for numeric predictors.\n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess_outputs, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess_outputs, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess_outputs, /*tp_methods*/
  __pyx_members_6_loess_loess_outputs, /*tp_members*/
  __pyx_getsets_6_loess_loess_outputs, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess_outputs, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};
static struct __pyx_vtabstruct_6_loess_conf_intervals __pyx_vtable_6_loess_conf_intervals;

static PyObject *__pyx_tp_new_6_loess_conf_intervals(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  struct __pyx_obj_6_loess_conf_intervals *p = (struct __pyx_obj_6_loess_conf_intervals *)o;
  *(struct __pyx_vtabstruct_6_loess_conf_intervals **)&p->__pyx_vtab = __pyx_vtabptr_6_loess_conf_intervals;
  p->lower = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  p->fit = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  p->upper = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  return o;
}

static void __pyx_tp_dealloc_6_loess_conf_intervals(PyObject *o) {
  struct __pyx_obj_6_loess_conf_intervals *p = (struct __pyx_obj_6_loess_conf_intervals *)o;
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    ++o->ob_refcnt;
    __pyx_f_6_loess_14conf_intervals___dealloc__(o);
    if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
    --o->ob_refcnt;
    PyErr_Restore(etype, eval, etb);
  }
  Py_XDECREF(((PyObject *)p->lower));
  Py_XDECREF(((PyObject *)p->fit));
  Py_XDECREF(((PyObject *)p->upper));
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_conf_intervals(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_obj_6_loess_conf_intervals *p = (struct __pyx_obj_6_loess_conf_intervals *)o;
  if (p->lower) {
    e = (*v)(((PyObject*)p->lower), a); if (e) return e;
  }
  if (p->fit) {
    e = (*v)(((PyObject*)p->fit), a); if (e) return e;
  }
  if (p->upper) {
    e = (*v)(((PyObject*)p->upper), a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear_6_loess_conf_intervals(PyObject *o) {
  struct __pyx_obj_6_loess_conf_intervals *p = (struct __pyx_obj_6_loess_conf_intervals *)o;
  Py_XDECREF(((PyObject *)p->lower));
  p->lower = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->fit));
  p->fit = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->upper));
  p->upper = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
  return 0;
}

static struct PyMethodDef __pyx_methods_6_loess_conf_intervals[] = {
  {0, 0, 0, 0}
};

static struct PyMemberDef __pyx_members_6_loess_conf_intervals[] = {
  {"lower", T_OBJECT, offsetof(struct __pyx_obj_6_loess_conf_intervals, lower), READONLY, 0},
  {"fit", T_OBJECT, offsetof(struct __pyx_obj_6_loess_conf_intervals, fit), READONLY, 0},
  {"upper", T_OBJECT, offsetof(struct __pyx_obj_6_loess_conf_intervals, upper), READONLY, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_conf_intervals = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_conf_intervals = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_conf_intervals = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_conf_intervals = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_conf_intervals = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.conf_intervals", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_conf_intervals), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_conf_intervals, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_conf_intervals, /*tp_as_number*/
  &__pyx_tp_as_sequence_conf_intervals, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_conf_intervals, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_f_6_loess_14conf_intervals___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_conf_intervals, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Pointwise confidence intervals of a loess-predicted object:\n    \n:IVariables:\n    fit : ndarray\n        Predicted values.\n    lower : ndarray\n        Lower bounds of the confidence intervals.\n    upper : ndarray\n        Upper bounds of the confidence intervals.\n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_conf_intervals, /*tp_traverse*/
  __pyx_tp_clear_6_loess_conf_intervals, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_conf_intervals, /*tp_methods*/
  __pyx_members_6_loess_conf_intervals, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_conf_intervals, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};
static struct __pyx_vtabstruct_6_loess_loess_predicted __pyx_vtable_6_loess_loess_predicted;

static PyObject *__pyx_tp_new_6_loess_loess_predicted(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  struct __pyx_obj_6_loess_loess_predicted *p = (struct __pyx_obj_6_loess_loess_predicted *)o;
  *(struct __pyx_vtabstruct_6_loess_loess_predicted **)&p->__pyx_vtab = __pyx_vtabptr_6_loess_loess_predicted;
  p->confidence_intervals = ((struct __pyx_obj_6_loess_conf_intervals *)Py_None); Py_INCREF(Py_None);
  return o;
}

static void __pyx_tp_dealloc_6_loess_loess_predicted(PyObject *o) {
  struct __pyx_obj_6_loess_loess_predicted *p = (struct __pyx_obj_6_loess_loess_predicted *)o;
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    ++o->ob_refcnt;
    __pyx_f_6_loess_15loess_predicted___dealloc__(o);
    if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
    --o->ob_refcnt;
    PyErr_Restore(etype, eval, etb);
  }
  Py_XDECREF(((PyObject *)p->confidence_intervals));
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_loess_predicted(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_obj_6_loess_loess_predicted *p = (struct __pyx_obj_6_loess_loess_predicted *)o;
  if (p->confidence_intervals) {
    e = (*v)(((PyObject*)p->confidence_intervals), a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear_6_loess_loess_predicted(PyObject *o) {
  struct __pyx_obj_6_loess_loess_predicted *p = (struct __pyx_obj_6_loess_loess_predicted *)o;
  Py_XDECREF(((PyObject *)p->confidence_intervals));
  p->confidence_intervals = ((struct __pyx_obj_6_loess_conf_intervals *)Py_None); Py_INCREF(Py_None);
  return 0;
}

static PyObject *__pyx_getprop_6_loess_15loess_predicted_values(PyObject *o, void *x) {
  return __pyx_f_6_loess_15loess_predicted_6values___get__(o);
}

static PyObject *__pyx_getprop_6_loess_15loess_predicted_stderr(PyObject *o, void *x) {
  return __pyx_f_6_loess_15loess_predicted_6stderr___get__(o);
}

static PyObject *__pyx_getprop_6_loess_15loess_predicted_residual_scale(PyObject *o, void *x) {
  return __pyx_f_6_loess_15loess_predicted_14residual_scale___get__(o);
}

static PyObject *__pyx_getprop_6_loess_15loess_predicted_df(PyObject *o, void *x) {
  return __pyx_f_6_loess_15loess_predicted_2df___get__(o);
}

static struct PyMethodDef __pyx_methods_6_loess_loess_predicted[] = {
  {"confidence", (PyCFunction)__pyx_f_6_loess_15loess_predicted_confidence, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_15loess_predicted_confidence},
  {0, 0, 0, 0}
};

static struct PyMemberDef __pyx_members_6_loess_loess_predicted[] = {
  {"nest", T_LONG, offsetof(struct __pyx_obj_6_loess_loess_predicted, nest), READONLY, 0},
  {"confidence_intervals", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess_predicted, confidence_intervals), READONLY, 0},
  {0, 0, 0, 0, 0}
};

static struct PyGetSetDef __pyx_getsets_6_loess_loess_predicted[] = {
  {"values", __pyx_getprop_6_loess_15loess_predicted_values, 0, 0, 0},
  {"stderr", __pyx_getprop_6_loess_15loess_predicted_stderr, 0, 0, 0},
  {"residual_scale", __pyx_getprop_6_loess_15loess_predicted_residual_scale, 0, 0, 0},
  {"df", __pyx_getprop_6_loess_15loess_predicted_df, 0, 0, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess_predicted = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess_predicted = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess_predicted = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess_predicted = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess_predicted = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess_predicted", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess_predicted), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess_predicted, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_loess_predicted, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess_predicted, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess_predicted, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  __pyx_f_6_loess_15loess_predicted___str__, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess_predicted, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Predicted values and standard errors of a loess object\n\n:IVariables:\n    values : ndarray\n        The (m,) ndarray of loess values evaluated at newdata\n    stderr : ndarray\n        The (m,) ndarray of the estimates of the standard error on the estimated\n        values.\n    residual_scale : float\n        Estimate of the scale of the residuals\n    df : integer\n        Degrees of freedom of the t-distribution used to compute pointwise \n        confidence intervals for the evaluated surface.\n    nest : integer\n        Number of new observations.\n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess_predicted, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess_predicted, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess_predicted, /*tp_methods*/
  __pyx_members_6_loess_loess_predicted, /*tp_members*/
  __pyx_getsets_6_loess_loess_predicted, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess_predicted, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};

static PyObject *__pyx_tp_new_6_loess_loess(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  struct __pyx_obj_6_loess_loess *p = (struct __pyx_obj_6_loess_loess *)o;
  p->inputs = ((struct __pyx_obj_6_loess_loess_inputs *)Py_None); Py_INCREF(Py_None);
  p->model = ((struct __pyx_obj_6_loess_loess_model *)Py_None); Py_INCREF(Py_None);
  p->control = ((struct __pyx_obj_6_loess_loess_control *)Py_None); Py_INCREF(Py_None);
  p->kd_tree = ((struct __pyx_obj_6_loess_loess_kd_tree *)Py_None); Py_INCREF(Py_None);
  p->outputs = ((struct __pyx_obj_6_loess_loess_outputs *)Py_None); Py_INCREF(Py_None);
  p->predicted = ((struct __pyx_obj_6_loess_loess_predicted *)Py_None); Py_INCREF(Py_None);
  return o;
}

static void __pyx_tp_dealloc_6_loess_loess(PyObject *o) {
  struct __pyx_obj_6_loess_loess *p = (struct __pyx_obj_6_loess_loess *)o;
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    ++o->ob_refcnt;
    __pyx_f_6_loess_5loess___dealloc__(o);
    if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
    --o->ob_refcnt;
    PyErr_Restore(etype, eval, etb);
  }
  Py_XDECREF(((PyObject *)p->inputs));
  Py_XDECREF(((PyObject *)p->model));
  Py_XDECREF(((PyObject *)p->control));
  Py_XDECREF(((PyObject *)p->kd_tree));
  Py_XDECREF(((PyObject *)p->outputs));
  Py_XDECREF(((PyObject *)p->predicted));
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_loess(PyObject *o, visitproc v, void *a) {
  int e;
  struct __pyx_obj_6_loess_loess *p = (struct __pyx_obj_6_loess_loess *)o;
  if (p->inputs) {
    e = (*v)(((PyObject*)p->inputs), a); if (e) return e;
  }
  if (p->model) {
    e = (*v)(((PyObject*)p->model), a); if (e) return e;
  }
  if (p->control) {
    e = (*v)(((PyObject*)p->control), a); if (e) return e;
  }
  if (p->kd_tree) {
    e = (*v)(((PyObject*)p->kd_tree), a); if (e) return e;
  }
  if (p->outputs) {
    e = (*v)(((PyObject*)p->outputs), a); if (e) return e;
  }
  if (p->predicted) {
    e = (*v)(((PyObject*)p->predicted), a); if (e) return e;
  }
  return 0;
}

static int __pyx_tp_clear_6_loess_loess(PyObject *o) {
  struct __pyx_obj_6_loess_loess *p = (struct __pyx_obj_6_loess_loess *)o;
  Py_XDECREF(((PyObject *)p->inputs));
  p->inputs = ((struct __pyx_obj_6_loess_loess_inputs *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->model));
  p->model = ((struct __pyx_obj_6_loess_loess_model *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->control));
  p->control = ((struct __pyx_obj_6_loess_loess_control *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->kd_tree));
  p->kd_tree = ((struct __pyx_obj_6_loess_loess_kd_tree *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->outputs));
  p->outputs = ((struct __pyx_obj_6_loess_loess_outputs *)Py_None); Py_INCREF(Py_None);
  Py_XDECREF(((PyObject *)p->predicted));
  p->predicted = ((struct __pyx_obj_6_loess_loess_predicted *)Py_None); Py_INCREF(Py_None);
  return 0;
}

static struct PyMethodDef __pyx_methods_6_loess_loess[] = {
  {"fit", (PyCFunction)__pyx_f_6_loess_5loess_fit, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_5loess_fit},
  {"input_summary", (PyCFunction)__pyx_f_6_loess_5loess_input_summary, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_5loess_input_summary},
  {"output_summary", (PyCFunction)__pyx_f_6_loess_5loess_output_summary, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_5loess_output_summary},
  {"predict", (PyCFunction)__pyx_f_6_loess_5loess_predict, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6_loess_5loess_predict},
  {0, 0, 0, 0}
};

static struct PyMemberDef __pyx_members_6_loess_loess[] = {
  {"inputs", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, inputs), READONLY, 0},
  {"model", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, model), READONLY, 0},
  {"control", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, control), READONLY, 0},
  {"kd_tree", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, kd_tree), READONLY, 0},
  {"outputs", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, outputs), READONLY, 0},
  {"predicted", T_OBJECT, offsetof(struct __pyx_obj_6_loess_loess, predicted), READONLY, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_loess = {
  0, /*nb_add*/
  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
  0, /*nb_long*/
  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_loess = {
  0, /*sq_length*/
  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_loess = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_loess = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_loess = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.loess", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_loess), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_loess, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_loess, /*tp_as_number*/
  &__pyx_tp_as_sequence_loess, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_loess, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_loess, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "\n    \n:Keywords:\n    x : ndarray\n        A (n,p) ndarray of independent variables, with n the number of observations\n        and p the number of variables.\n    y : ndarray\n        A (n,) ndarray of observations\n    weights : ndarray\n        A (n,) ndarray of weights to be given to individual observations in the \n        sum of squared residuals that forms the local fitting criterion. If not\n        None, the weights should be non negative. If the different observations\n        have non-equal variances, the weights should be inversely proportional \n        to the variances.\n        By default, an unweighted fit is carried out (all the weights are one).\n    surface : string [\"interpolate\"]\n        Determines whether the fitted surface is computed directly at all points\n        (\"direct\") or whether an interpolation method is used (\"interpolate\").\n        The default (\"interpolate\") is what most users should use unless special \n        circumstances warrant.\n    statistics : string [\"approximate\"]\n        Determines whether the statistical quantities are computed exactly \n        (\"exact\") or approximately (\"approximate\"). \"exact\" should only be used \n        for testing the approximation in statistical development and is not meant \n        for routine usage because computation time can be horrendous.\n    trace_hat : string [\"wait.to.decide\"]\n        Determines how the trace of the hat matrix should be computed. The hat\n        matrix is used in the computation of the statistical quantities. \n        If \"exact\", an exact computation is done; this could be slow when the\n        number of observations n becomes large. If \"wait.to.decide\" is selected, \n        then a default is \"exact\" for n < 500 and \"approximate\" otherwise. \n        This option is only useful when the fitted surface is interpolated. If  \n        surface is \"exact\", an exact computation is always done for the trace. \n        Setting trace_hat to \"approximate\" for large dataset will substantially \n        reduce the computation time.\n    iterations : integer\n        Number of iterations of the robust fitting method. If the family is \n        \"gaussian\", the number of iterations is set to 0.\n    cell : integer\n        Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),\n        where n is the number of observations, and span the smoothing parameter.\n        Then, a cell is further divided if the number of observations within it \n        is greater than or equal to k. This option is only used if the surface \n        is interpolated.\n    span : float [0.75]\n        Smoothing factor, as a fraction of the number of points to take into\n        account. \n    degree : integer [2]\n        Overall degree of locally-fitted polynomial. 1 is locally-linear \n        fitting and 2 is locally-quadratic fitting.  Degree should be 2 at most.\n    normalize : boolean [True]\n        Determines whether the independent variables should be normalized.  \n        If True, the normalization is performed by setting the 10% trimmed \n        standard deviation to one. If False, no normalization is carried out. \n        This option is only useful for more than one variable. For spatial\n        coordinates predictors or variables with a common scale, it should be \n        set to False.\n    family : string [\"gaussian\"]\n        Determines the assumed distribution of the errors. The values are \n        \"gaussian\" or \"symmetric\". If \"gaussian\" is selected, the fit is \n        performed with least-squares. If \"symmetric\" is selected, the fit\n        is performed robustly by redescending M-estimators.\n    parametric_flags : sequence [ [False]*p ]\n        Indicates which independent variables should be conditionally-parametric\n       (if there are two or more independent variables). The argument should \n       be a sequence of booleans, with the same size as the number of independent \n       variables, specified in the order of the predictor group ordered in x. \n    drop_square : sequence [ [False]* p]\n        When there are two or more independent variables and when a 2nd order\n        polynomial is used, \"drop_square_flags\" specifies those numeric predictors \n        whose squares should be dropped from the set of fitting variables. \n        The method of specification is the same as for parametric.  \n        \n:Outputs:\n    fitted_values : ndarray\n        The (n,) ndarray of fitted values.\n    fitted_residuals : ndarray\n        The (n,) ndarray of fitted residuals (observations - fitted values).\n    enp : float\n        Equivalent number of parameters.\n    s : float\n        Estimate of the scale of residuals.\n    one_delta: float\n        Statistical parameter used in the computation of standard errors.\n    two_delta : float\n        Statistical parameter used in the computation of standard errors.\n    pseudovalues : ndarray\n        The (n,) ndarray of adjusted values of the response when robust estimation \n        is used.\n    trace_hat : float    \n        Trace of the operator hat matrix.\n    diagonal :\n        Diagonal of the operator hat matrix.\n    robust : ndarray\n        The (n,) ndarray of robustness weights for robust fitting.\n    divisor : ndarray\n        The (p,) array of normalization divisors for numeric predictors.\n\n    ", /*tp_doc*/
  __pyx_tp_traverse_6_loess_loess, /*tp_traverse*/
  __pyx_tp_clear_6_loess_loess, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_loess, /*tp_methods*/
  __pyx_members_6_loess_loess, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  __pyx_f_6_loess_5loess___init__, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_loess, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};

static PyObject *__pyx_tp_new_6_loess_anova(PyTypeObject *t, PyObject *a, PyObject *k) {
  PyObject *o = (*t->tp_alloc)(t, 0);
  return o;
}

static void __pyx_tp_dealloc_6_loess_anova(PyObject *o) {
  (*o->ob_type->tp_free)(o);
}

static int __pyx_tp_traverse_6_loess_anova(PyObject *o, visitproc v, void *a) {
  return 0;
}

static int __pyx_tp_clear_6_loess_anova(PyObject *o) {
  return 0;
}

static struct PyMethodDef __pyx_methods_6_loess_anova[] = {
  {0, 0, 0, 0}
};

static struct PyMemberDef __pyx_members_6_loess_anova[] = {
  {"dfn", T_DOUBLE, offsetof(struct __pyx_obj_6_loess_anova, dfn), READONLY, 0},
  {"dfd", T_DOUBLE, offsetof(struct __pyx_obj_6_loess_anova, dfd), READONLY, 0},
  {"F_value", T_DOUBLE, offsetof(struct __pyx_obj_6_loess_anova, F_value), READONLY, 0},
  {"Pr_F", T_DOUBLE, offsetof(struct __pyx_obj_6_loess_anova, Pr_F), READONLY, 0},
  {0, 0, 0, 0, 0}
};

static PyNumberMethods __pyx_tp_as_number_anova = {
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  0, /*nb_subtract*/
  0, /*nb_multiply*/
  0, /*nb_divide*/
  0, /*nb_remainder*/
  0, /*nb_divmod*/
  0, /*nb_power*/
  0, /*nb_negative*/
  0, /*nb_positive*/
  0, /*nb_absolute*/
  0, /*nb_nonzero*/
  0, /*nb_invert*/
  0, /*nb_lshift*/
  0, /*nb_rshift*/
  0, /*nb_and*/
  0, /*nb_xor*/
  0, /*nb_or*/
  0, /*nb_coerce*/
  0, /*nb_int*/
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  0, /*nb_float*/
  0, /*nb_oct*/
  0, /*nb_hex*/
  0, /*nb_inplace_add*/
  0, /*nb_inplace_subtract*/
  0, /*nb_inplace_multiply*/
  0, /*nb_inplace_divide*/
  0, /*nb_inplace_remainder*/
  0, /*nb_inplace_power*/
  0, /*nb_inplace_lshift*/
  0, /*nb_inplace_rshift*/
  0, /*nb_inplace_and*/
  0, /*nb_inplace_xor*/
  0, /*nb_inplace_or*/
  0, /*nb_floor_divide*/
  0, /*nb_true_divide*/
  0, /*nb_inplace_floor_divide*/
  0, /*nb_inplace_true_divide*/
};

static PySequenceMethods __pyx_tp_as_sequence_anova = {
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  0, /*sq_concat*/
  0, /*sq_repeat*/
  0, /*sq_item*/
  0, /*sq_slice*/
  0, /*sq_ass_item*/
  0, /*sq_ass_slice*/
  0, /*sq_contains*/
  0, /*sq_inplace_concat*/
  0, /*sq_inplace_repeat*/
};

static PyMappingMethods __pyx_tp_as_mapping_anova = {
  0, /*mp_length*/
  0, /*mp_subscript*/
  0, /*mp_ass_subscript*/
};

static PyBufferProcs __pyx_tp_as_buffer_anova = {
  0, /*bf_getreadbuffer*/
  0, /*bf_getwritebuffer*/
  0, /*bf_getsegcount*/
  0, /*bf_getcharbuffer*/
};

PyTypeObject __pyx_type_6_loess_anova = {
  PyObject_HEAD_INIT(0)
  0, /*ob_size*/
  "_loess.anova", /*tp_name*/
  sizeof(struct __pyx_obj_6_loess_anova), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_6_loess_anova, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  0, /*tp_compare*/
  0, /*tp_repr*/
  &__pyx_tp_as_number_anova, /*tp_as_number*/
  &__pyx_tp_as_sequence_anova, /*tp_as_sequence*/
  &__pyx_tp_as_mapping_anova, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  &__pyx_tp_as_buffer_anova, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_6_loess_anova, /*tp_traverse*/
  __pyx_tp_clear_6_loess_anova, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_6_loess_anova, /*tp_methods*/
  __pyx_members_6_loess_anova, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  __pyx_f_6_loess_5anova___init__, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_6_loess_anova, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
};

static struct PyMethodDef __pyx_methods[] = {
  {0, 0, 0, 0}
};

static void __pyx_init_filenames(void); /*proto*/

PyMODINIT_FUNC init_loess(void); /*proto*/
PyMODINIT_FUNC init_loess(void) {
  PyObject *__pyx_1 = 0;
  PyObject *__pyx_2 = 0;
  __pyx_init_filenames();
  __pyx_m = Py_InitModule4("_loess", __pyx_methods, 0, 0, PYTHON_API_VERSION);
  if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; goto __pyx_L1;};
  __pyx_b = PyImport_AddModule("__builtin__");
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  if (__Pyx_InternStrings(__pyx_intern_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; goto __pyx_L1;};
  if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; goto __pyx_L1;};
  __pyx_type_6_loess_loess_inputs.tp_free = _PyObject_GC_Del;
  if (PyType_Ready(&__pyx_type_6_loess_loess_inputs) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 61; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "loess_inputs", (PyObject *)&__pyx_type_6_loess_loess_inputs) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 61; goto __pyx_L1;}
  __pyx_ptype_6_loess_loess_inputs = &__pyx_type_6_loess_loess_inputs;
  if (PyType_Ready(&__pyx_type_6_loess_loess_control) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 128; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "loess_control", (PyObject *)&__pyx_type_6_loess_loess_control) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 128; goto __pyx_L1;}
  __pyx_ptype_6_loess_loess_control = &__pyx_type_6_loess_loess_control;
  if (PyType_Ready(&__pyx_type_6_loess_loess_kd_tree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 286; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "loess_kd_tree", (PyObject *)&__pyx_type_6_loess_loess_kd_tree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 286; goto __pyx_L1;}
  __pyx_ptype_6_loess_loess_kd_tree = &__pyx_type_6_loess_loess_kd_tree;
  __pyx_vtabptr_6_loess_loess_model = &__pyx_vtable_6_loess_loess_model;
  *(void(**)())&__pyx_vtable_6_loess_loess_model.setup = (void(*)())__pyx_f_6_loess_11loess_model_setup;
  if (PyType_Ready(&__pyx_type_6_loess_loess_model) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 292; goto __pyx_L1;}
  if (__Pyx_SetVtable(__pyx_type_6_loess_loess_model.tp_dict, __pyx_vtabptr_6_loess_loess_model) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 292; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "loess_model", (PyObject *)&__pyx_type_6_loess_loess_model) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 292; goto __pyx_L1;}
  __pyx_ptype_6_loess_loess_model = &__pyx_type_6_loess_loess_model;
  __pyx_vtabptr_6_loess_loess_outputs = &__pyx_vtable_6_loess_loess_outputs;
  *(void(**)())&__pyx_vtable_6_loess_loess_outputs.setup = (void(*)())__pyx_f_6_loess_13loess_outputs_setup;
  if (PyType_Ready(&__pyx_type_6_loess_loess_outputs) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 434; goto __pyx_L1;}
  if (__Pyx_SetVtable(__pyx_type_6_loess_loess_outputs.tp_dict, __pyx_vtabptr_6_loess_loess_outputs) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 434; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "loess_outputs", (PyObject *)&__pyx_type_6_loess_loess_outputs) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 434; goto __pyx_L1;}
  __pyx_ptype_6_loess_loess_outputs = &__pyx_type_6_loess_loess_outputs;
  __pyx_vtabptr_6_loess_conf_intervals = &__pyx_vtable_6_loess_conf_intervals;
  *(void(**)())&__pyx_vtable_6_loess_conf_intervals.setup = (void(*)())__pyx_f_6_loess_14conf_intervals_setup;
  __pyx_type_6_loess_conf_intervals.tp_free = _PyObject_GC_Del;
  if (PyType_Ready(&__pyx_type_6_loess_conf_intervals) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 532; goto __pyx_L1;}
  if (__Pyx_SetVtable(__pyx_type_6_loess_conf_intervals.tp_dict, __pyx_vtabptr_6_loess_conf_intervals) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 532; goto __pyx_L1;}
  if (PyObject_SetAttrString(__pyx_m, "conf_intervals", (PyObject *)&__pyx_type_6_loess_conf_intervals) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 532; goto __pyx_L1;}
  __pyx_ptype_6_loess_conf_intervals = &__pyx_type_6_loess_conf_intervals;
  __pyx_vtabptr_6_loess_loess_predicted = &__pyx_vtable_6_loess_loess_predicted;
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  __pyx_type_6_loess_loess_predicted.tp_free = _PyObject_GC_Del;
  if (PyType_Ready(&__pyx_type_6_loess_loess_predicted) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 564; goto __pyx_L1;}
  if (__Pyx_SetVtable(__pyx_type_6_loess_loess_predicted.tp_dict, __pyx_vtabptr_6_loess_loess_predicted) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 564; goto __pyx_L1;}
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  __pyx_type_6_loess_loess.tp_free = _PyObject_GC_Del;
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  if (PyObject_SetAttrString(__pyx_m, "loess", (PyObject *)&__pyx_type_6_loess_loess) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 650; goto __pyx_L1;}
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  __pyx_ptype_6_loess_anova = &__pyx_type_6_loess_anova;
  __pyx_ptype_7c_numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr)); if (!__pyx_ptype_7c_numpy_dtype) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 76; goto __pyx_L1;}
  __pyx_ptype_7c_numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (!__pyx_ptype_7c_numpy_ndarray) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 81; goto __pyx_L1;}
  __pyx_ptype_7c_numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject)); if (!__pyx_ptype_7c_numpy_flatiter) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 90; goto __pyx_L1;}
  __pyx_ptype_7c_numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject)); if (!__pyx_ptype_7c_numpy_broadcast) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 96; goto __pyx_L1;}

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":8 */
  __pyx_1 = __Pyx_Import(__pyx_n_numpy, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; goto __pyx_L1;}
  if (PyObject_SetAttr(__pyx_m, __pyx_n_numpy, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; goto __pyx_L1;}
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  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":9 */
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  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_array); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; goto __pyx_L1;}
  Py_DECREF(__pyx_1); __pyx_1 = 0;
  if (PyObject_SetAttr(__pyx_m, __pyx_n_narray, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; goto __pyx_L1;}
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  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":10 */
  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n_numpy); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; goto __pyx_L1;}
  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_float_); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; goto __pyx_L1;}
  Py_DECREF(__pyx_1); __pyx_1 = 0;
  if (PyObject_SetAttr(__pyx_m, __pyx_n_float_, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; goto __pyx_L1;}
  Py_DECREF(__pyx_2); __pyx_2 = 0;

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":13 */
  import_array();

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":36 */
  __pyx_k8;

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":609 */
  __pyx_1 = PyFloat_FromDouble(0.95); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 609; goto __pyx_L1;}
  __pyx_k9 = __pyx_1;
  __pyx_1 = 0;

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":758 */
  Py_INCREF(Py_None);
  __pyx_k10 = Py_None;

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":821 */
  __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_False); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 821; goto __pyx_L1;}
  __pyx_k11 = __pyx_2;
  __pyx_2 = 0;

  /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":889 */
  return;
  __pyx_L1:;
  Py_XDECREF(__pyx_1);
  Py_XDECREF(__pyx_2);
  __Pyx_AddTraceback("_loess");
}

static char *__pyx_filenames[] = {
  "_loess.pyx",
  "c_numpy.pxd",
};

/* Runtime support code */

static void __pyx_init_filenames(void) {
  __pyx_f = __pyx_filenames;
}

static int __Pyx_GetStarArgs(
    PyObject **args, 
    PyObject **kwds,
    char *kwd_list[], 
    int nargs,
    PyObject **args2, 
    PyObject **kwds2)
{
    PyObject *x = 0, *args1 = 0, *kwds1 = 0;
    
    if (args2)
        *args2 = 0;
    if (kwds2)
        *kwds2 = 0;
    
    if (args2) {
        args1 = PyTuple_GetSlice(*args, 0, nargs);
        if (!args1)
            goto bad;
        *args2 = PyTuple_GetSlice(*args, nargs, PyTuple_Size(*args));
        if (!*args2)
            goto bad;
    }
    else {
        args1 = *args;
        Py_INCREF(args1);
    }
    
    if (kwds2) {
        if (*kwds) {
            char **p;
            kwds1 = PyDict_New();
            if (!kwds)
                goto bad;
            *kwds2 = PyDict_Copy(*kwds);
            if (!*kwds2)
                goto bad;
            for (p = kwd_list; *p; p++) {
                x = PyDict_GetItemString(*kwds, *p);
                if (x) {
                    if (PyDict_SetItemString(kwds1, *p, x) < 0)
                        goto bad;
                    if (PyDict_DelItemString(*kwds2, *p) < 0)
                        goto bad;
                }
            }
        }
        else {
            *kwds2 = PyDict_New();
            if (!*kwds2)
                goto bad;
        }
    }
    else {
        kwds1 = *kwds;
        Py_XINCREF(kwds1);
    }
    
    *args = args1;
    *kwds = kwds1;
    return 0;
bad:
    Py_XDECREF(args1);
    Py_XDECREF(kwds1);
    if (*args2) {
        Py_XDECREF(*args2);
    }
    if (*kwds2) {
        Py_XDECREF(*kwds2);
    }
    return -1;
}

static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
    PyObject *__import__ = 0;
    PyObject *empty_list = 0;
    PyObject *module = 0;
    PyObject *global_dict = 0;
    PyObject *empty_dict = 0;
    PyObject *list;
    __import__ = PyObject_GetAttrString(__pyx_b, "__import__");
    if (!__import__)
        goto bad;
    if (from_list)
        list = from_list;
    else {
        empty_list = PyList_New(0);
        if (!empty_list)
            goto bad;
        list = empty_list;
    }
    global_dict = PyModule_GetDict(__pyx_m);
    if (!global_dict)
        goto bad;
    empty_dict = PyDict_New();
    if (!empty_dict)
        goto bad;
    module = PyObject_CallFunction(__import__, "OOOO",
        name, global_dict, empty_dict, list);
bad:
    Py_XDECREF(empty_list);
    Py_XDECREF(__import__);
    Py_XDECREF(empty_dict);
    return module;
}

static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
    PyObject *result;
    result = PyObject_GetAttr(dict, name);
    if (!result)
        PyErr_SetObject(PyExc_NameError, name);
    return result;
}

static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
    if (!type) {
        PyErr_Format(PyExc_SystemError, "Missing type object");
        return 0;
    }
    if (obj == Py_None || PyObject_TypeCheck(obj, type))
        return 1;
    PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
        obj->ob_type->tp_name, type->tp_name);
    return 0;
}

static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
    Py_XINCREF(type);
    Py_XINCREF(value);
    Py_XINCREF(tb);
    /* First, check the traceback argument, replacing None with NULL. */
    if (tb == Py_None) {
        Py_DECREF(tb);
        tb = 0;
    }
    else if (tb != NULL && !PyTraceBack_Check(tb)) {
        PyErr_SetString(PyExc_TypeError,
            "raise: arg 3 must be a traceback or None");
        goto raise_error;
    }
    /* Next, replace a missing value with None */
    if (value == NULL) {
        value = Py_None;
        Py_INCREF(value);
    }
    /* Next, repeatedly, replace a tuple exception with its first item */
    while (PyTuple_Check(type) && PyTuple_Size(type) > 0) {
        PyObject *tmp = type;
        type = PyTuple_GET_ITEM(type, 0);
        Py_INCREF(type);
        Py_DECREF(tmp);
    }
    if (PyString_Check(type)) {
        if (PyErr_Warn(PyExc_DeprecationWarning,
                "raising a string exception is deprecated"))
            goto raise_error;
    }
    else if (PyType_Check(type) || PyClass_Check(type))
        ; /*PyErr_NormalizeException(&type, &value, &tb);*/
    else {
        /* Raising an instance.  The value should be a dummy. */
        if (value != Py_None) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto raise_error;
        }
        /* Normalize to raise <class>, <instance> */
        Py_DECREF(value);
        value = type;
        if (PyInstance_Check(type))
            type = (PyObject*) ((PyInstanceObject*)type)->in_class;
        else
            type = (PyObject*) type->ob_type;
        Py_INCREF(type);
    }
    PyErr_Restore(type, value, tb);
    return;
raise_error:
    Py_XDECREF(value);
    Py_XDECREF(type);
    Py_XDECREF(tb);
    return;
}

static void __Pyx_UnpackError(void) {
    PyErr_SetString(PyExc_ValueError, "unpack sequence of wrong size");
}

static PyObject *__Pyx_UnpackItem(PyObject *iter) {
    PyObject *item;
    if (!(item = PyIter_Next(iter))) {
        if (!PyErr_Occurred())
            __Pyx_UnpackError();
    }
    return item;
}

static int __Pyx_EndUnpack(PyObject *iter) {
    PyObject *item;
    if ((item = PyIter_Next(iter))) {
        Py_DECREF(item);
        __Pyx_UnpackError();
        return -1;
    }
    else if (!PyErr_Occurred())
        return 0;
    else
        return -1;
}

static PyObject *__Pyx_GetStdout(void) {
    PyObject *f = PySys_GetObject("stdout");
    if (!f) {
        PyErr_SetString(PyExc_RuntimeError, "lost sys.stdout");
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    return f;
}

static int __Pyx_PrintItem(PyObject *v) {
    PyObject *f;
    
    if (!(f = __Pyx_GetStdout()))
        return -1;
    if (PyFile_SoftSpace(f, 1)) {
        if (PyFile_WriteString(" ", f) < 0)
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    }
    if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0)
        return -1;
    if (PyString_Check(v)) {
        char *s = PyString_AsString(v);
        int len = PyString_Size(v);
        if (len > 0 &&
            isspace(Py_CHARMASK(s[len-1])) &&
            s[len-1] != ' ')
                PyFile_SoftSpace(f, 0);
    }
    return 0;
}

static int __Pyx_PrintNewline(void) {
    PyObject *f;
    
    if (!(f = __Pyx_GetStdout()))
        return -1;
    if (PyFile_WriteString("\n", f) < 0)
        return -1;
    PyFile_SoftSpace(f, 0);
    return 0;
}

static int __Pyx_InternStrings(__Pyx_InternTabEntry *t) {
    while (t->p) {
        *t->p = PyString_InternFromString(t->s);
        if (!*t->p)
            return -1;
        ++t;
    }
    return 0;
}

static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
    while (t->p) {
        *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
        if (!*t->p)
            return -1;
        ++t;
    }
    return 0;
}

static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
    PyObject *pycobj = 0;
    int result;
    
    pycobj = PyCObject_FromVoidPtr(vtable, 0);
    if (!pycobj)
        goto bad;
    if (PyDict_SetItemString(dict, "__pyx_vtable__", pycobj) < 0)
        goto bad;
    result = 0;
    goto done;

bad:
    result = -1;
done:
    Py_XDECREF(pycobj);
    return result;
}

static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, 
    long size) 
{
    PyObject *py_module_name = 0;
    PyObject *py_class_name = 0;
    PyObject *py_name_list = 0;
    PyObject *py_module = 0;
    PyObject *result = 0;
    
    py_module_name = PyString_FromString(module_name);
    if (!py_module_name)
        goto bad;
    py_class_name = PyString_FromString(class_name);
    if (!py_class_name)
        goto bad;
    py_name_list = PyList_New(1);
    if (!py_name_list)
        goto bad;
    Py_INCREF(py_class_name);
    if (PyList_SetItem(py_name_list, 0, py_class_name) < 0)
        goto bad;
    py_module = __Pyx_Import(py_module_name, py_name_list);
    if (!py_module)
        goto bad;
    result = PyObject_GetAttr(py_module, py_class_name);
    if (!result)
        goto bad;
    if (!PyType_Check(result)) {
        PyErr_Format(PyExc_TypeError, 
            "%s.%s is not a type object",
            module_name, class_name);
        goto bad;
    }
    if (((PyTypeObject *)result)->tp_basicsize != size) {
        PyErr_Format(PyExc_ValueError, 
            "%s.%s does not appear to be the correct type object",
            module_name, class_name);
        goto bad;
    }
    goto done;
bad:
    Py_XDECREF(result);
    result = 0;
done:
    Py_XDECREF(py_module_name);
    Py_XDECREF(py_class_name);
    Py_XDECREF(py_name_list);
    return (PyTypeObject *)result;
}

#include "compile.h"
#include "frameobject.h"
#include "traceback.h"

static void __Pyx_AddTraceback(char *funcname) {
    PyObject *py_srcfile = 0;
    PyObject *py_funcname = 0;
    PyObject *py_globals = 0;
    PyObject *empty_tuple = 0;
    PyObject *empty_string = 0;
    PyCodeObject *py_code = 0;
    PyFrameObject *py_frame = 0;
    
    py_srcfile = PyString_FromString(__pyx_filename);
    if (!py_srcfile) goto bad;
    py_funcname = PyString_FromString(funcname);
    if (!py_funcname) goto bad;
    py_globals = PyModule_GetDict(__pyx_m);
    if (!py_globals) goto bad;
    empty_tuple = PyTuple_New(0);
    if (!empty_tuple) goto bad;
    empty_string = PyString_FromString("");
    if (!empty_string) goto bad;
    py_code = PyCode_New(
        0,            /*int argcount,*/
        0,            /*int nlocals,*/
        0,            /*int stacksize,*/
        0,            /*int flags,*/
        empty_string, /*PyObject *code,*/
        empty_tuple,  /*PyObject *consts,*/
        empty_tuple,  /*PyObject *names,*/
        empty_tuple,  /*PyObject *varnames,*/
        empty_tuple,  /*PyObject *freevars,*/
        empty_tuple,  /*PyObject *cellvars,*/
        py_srcfile,   /*PyObject *filename,*/
        py_funcname,  /*PyObject *name,*/
        __pyx_lineno,   /*int firstlineno,*/
        empty_string  /*PyObject *lnotab*/
    );
    if (!py_code) goto bad;
    py_frame = PyFrame_New(
        PyThreadState_Get(), /*PyThreadState *tstate,*/
        py_code,             /*PyCodeObject *code,*/
        py_globals,          /*PyObject *globals,*/
        0                    /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    py_frame->f_lineno = __pyx_lineno;
    PyTraceBack_Here(py_frame);
bad:
    Py_XDECREF(py_srcfile);
    Py_XDECREF(py_funcname);
    Py_XDECREF(empty_tuple);
    Py_XDECREF(empty_string);
    Py_XDECREF(py_code);
    Py_XDECREF(py_frame);
}