diff --git a/docs/conf.py b/docs/conf.py
index 56fc545..b447708 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -10,6 +10,7 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
+import rainforest
 import os
 import sys
 sys.path.insert(0, os.path.abspath('.'))
@@ -31,7 +32,7 @@
 # ones.
 extensions = ['sphinx.ext.napoleon']
 master_doc = 'index'
-
+version = rainforest.__version__
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
diff --git a/rainforest/__init__.py b/rainforest/__init__.py
index 99b7be3..18422a0 100644
--- a/rainforest/__init__.py
+++ b/rainforest/__init__.py
@@ -2,4 +2,6 @@
 from . import common
 from . import database
 from . import ml
-from . import performance
\ No newline at end of file
+from . import performance
+
+__version__ = common.utils.get_version()
diff --git a/rainforest/common/add_at.c b/rainforest/common/add_at.c
index 59bffb4..04be795 100644
--- a/rainforest/common/add_at.c
+++ b/rainforest/common/add_at.c
@@ -1,17 +1,17 @@
-/* Generated by Cython 3.0.0 */
+/* Generated by Cython 3.0.10 */
 
 /* BEGIN: Cython Metadata
 {
     "distutils": {
         "depends": [
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h",
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h",
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h",
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h",
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h"
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include/numpy/arrayobject.h",
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include/numpy/arrayscalars.h",
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include/numpy/ndarrayobject.h",
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include/numpy/ndarraytypes.h",
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include/numpy/ufuncobject.h"
         ],
         "include_dirs": [
-            "/store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/core/include"
+            "/tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/core/include"
         ],
         "name": "rainforest.common.add_at",
         "sources": [
@@ -41,10 +41,15 @@ END: Cython Metadata */
 #elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
     #error Cython requires Python 2.7+ or Python 3.3+.
 #else
-#define CYTHON_ABI "3_0_0"
+#if defined(CYTHON_LIMITED_API) && CYTHON_LIMITED_API
+#define __PYX_EXTRA_ABI_MODULE_NAME "limited"
+#else
+#define __PYX_EXTRA_ABI_MODULE_NAME ""
+#endif
+#define CYTHON_ABI "3_0_10" __PYX_EXTRA_ABI_MODULE_NAME
 #define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
 #define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000F0
+#define CYTHON_HEX_VERSION 0x03000AF0
 #define CYTHON_FUTURE_DIVISION 1
 #include <stddef.h>
 #ifndef offsetof
@@ -77,6 +82,7 @@ END: Cython Metadata */
 #ifndef Py_HUGE_VAL
   #define Py_HUGE_VAL HUGE_VAL
 #endif
+#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX
 #if defined(GRAALVM_PYTHON)
   /* For very preliminary testing purposes. Most variables are set the same as PyPy.
      The existence of this section does not imply that anything works or is even tested */
@@ -135,6 +141,8 @@ END: Cython Metadata */
   #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
     #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
   #endif
+  #undef CYTHON_USE_FREELISTS
+  #define CYTHON_USE_FREELISTS 0
 #elif defined(PYPY_VERSION)
   #define CYTHON_COMPILING_IN_PYPY 1
   #define CYTHON_COMPILING_IN_CPYTHON 0
@@ -143,8 +151,9 @@ END: Cython Metadata */
   #define CYTHON_COMPILING_IN_NOGIL 0
   #undef CYTHON_USE_TYPE_SLOTS
   #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
+  #ifndef CYTHON_USE_TYPE_SPECS
+    #define CYTHON_USE_TYPE_SPECS 0
+  #endif
   #undef CYTHON_USE_PYTYPE_LOOKUP
   #define CYTHON_USE_PYTYPE_LOOKUP 0
   #if PY_VERSION_HEX < 0x03050000
@@ -195,7 +204,13 @@ END: Cython Metadata */
   #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
     #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
   #endif
+  #undef CYTHON_USE_FREELISTS
+  #define CYTHON_USE_FREELISTS 0
 #elif defined(CYTHON_LIMITED_API)
+  #ifdef Py_LIMITED_API
+    #undef __PYX_LIMITED_VERSION_HEX
+    #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API
+  #endif
   #define CYTHON_COMPILING_IN_PYPY 0
   #define CYTHON_COMPILING_IN_CPYTHON 0
   #define CYTHON_COMPILING_IN_LIMITED_API 1
@@ -243,7 +258,7 @@ END: Cython Metadata */
   #undef CYTHON_USE_MODULE_STATE
   #define CYTHON_USE_MODULE_STATE 1
   #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
+    #define CYTHON_USE_TP_FINALIZE 0
   #endif
   #undef CYTHON_USE_DICT_VERSIONS
   #define CYTHON_USE_DICT_VERSIONS 0
@@ -252,7 +267,9 @@ END: Cython Metadata */
   #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
     #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
   #endif
-#elif defined(PY_NOGIL)
+  #undef CYTHON_USE_FREELISTS
+  #define CYTHON_USE_FREELISTS 0
+#elif defined(Py_GIL_DISABLED) || defined(Py_NOGIL)
   #define CYTHON_COMPILING_IN_PYPY 0
   #define CYTHON_COMPILING_IN_CPYTHON 0
   #define CYTHON_COMPILING_IN_LIMITED_API 0
@@ -261,11 +278,17 @@ END: Cython Metadata */
   #ifndef CYTHON_USE_TYPE_SLOTS
     #define CYTHON_USE_TYPE_SLOTS 1
   #endif
+  #ifndef CYTHON_USE_TYPE_SPECS
+    #define CYTHON_USE_TYPE_SPECS 0
+  #endif
   #undef CYTHON_USE_PYTYPE_LOOKUP
   #define CYTHON_USE_PYTYPE_LOOKUP 0
   #ifndef CYTHON_USE_ASYNC_SLOTS
     #define CYTHON_USE_ASYNC_SLOTS 1
   #endif
+  #ifndef CYTHON_USE_PYLONG_INTERNALS
+    #define CYTHON_USE_PYLONG_INTERNALS 0
+  #endif
   #undef CYTHON_USE_PYLIST_INTERNALS
   #define CYTHON_USE_PYLIST_INTERNALS 0
   #ifndef CYTHON_USE_UNICODE_INTERNALS
@@ -273,8 +296,6 @@ END: Cython Metadata */
   #endif
   #undef CYTHON_USE_UNICODE_WRITER
   #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
   #ifndef CYTHON_AVOID_BORROWED_REFS
     #define CYTHON_AVOID_BORROWED_REFS 0
   #endif
@@ -286,11 +307,22 @@ END: Cython Metadata */
   #endif
   #undef CYTHON_FAST_THREAD_STATE
   #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_GIL
+  #define CYTHON_FAST_GIL 0
+  #ifndef CYTHON_METH_FASTCALL
+    #define CYTHON_METH_FASTCALL 1
+  #endif
   #undef CYTHON_FAST_PYCALL
   #define CYTHON_FAST_PYCALL 0
+  #ifndef CYTHON_PEP487_INIT_SUBCLASS
+    #define CYTHON_PEP487_INIT_SUBCLASS 1
+  #endif
   #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
     #define CYTHON_PEP489_MULTI_PHASE_INIT 1
   #endif
+  #ifndef CYTHON_USE_MODULE_STATE
+    #define CYTHON_USE_MODULE_STATE 0
+  #endif
   #ifndef CYTHON_USE_TP_FINALIZE
     #define CYTHON_USE_TP_FINALIZE 1
   #endif
@@ -298,6 +330,12 @@ END: Cython Metadata */
   #define CYTHON_USE_DICT_VERSIONS 0
   #undef CYTHON_USE_EXC_INFO_STACK
   #define CYTHON_USE_EXC_INFO_STACK 0
+  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
+    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
+  #endif
+  #ifndef CYTHON_USE_FREELISTS
+    #define CYTHON_USE_FREELISTS 0
+  #endif
 #else
   #define CYTHON_COMPILING_IN_PYPY 0
   #define CYTHON_COMPILING_IN_CPYTHON 1
@@ -388,6 +426,9 @@ END: Cython Metadata */
   #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
     #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
   #endif
+  #ifndef CYTHON_USE_FREELISTS
+    #define CYTHON_USE_FREELISTS 1
+  #endif
 #endif
 #if !defined(CYTHON_FAST_PYCCALL)
 #define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
@@ -465,6 +506,14 @@ END: Cython Metadata */
 #  define CYTHON_NCP_UNUSED CYTHON_UNUSED
 # endif
 #endif
+#ifndef CYTHON_USE_CPP_STD_MOVE
+  #if defined(__cplusplus) && (\
+    __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600))
+    #define CYTHON_USE_CPP_STD_MOVE 1
+  #else
+    #define CYTHON_USE_CPP_STD_MOVE 0
+  #endif
+#endif
 #define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
 #ifdef _MSC_VER
     #ifndef _MSC_STDINT_H_
@@ -564,59 +613,91 @@ END: Cython Metadata */
 #else
   #define __Pyx_BUILTIN_MODULE_NAME "builtins"
   #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
+#if CYTHON_COMPILING_IN_LIMITED_API
+    static CYTHON_INLINE PyObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
                                                     PyObject *code, PyObject *c, PyObject* n, PyObject *v,
                                                     PyObject *fv, PyObject *cell, PyObject* fn,
                                                     PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
+        PyObject *exception_table = NULL;
+        PyObject *types_module=NULL, *code_type=NULL, *result=NULL;
+        #if __PYX_LIMITED_VERSION_HEX < 0x030B0000
+        PyObject *version_info;
+        PyObject *py_minor_version = NULL;
+        #endif
+        long minor_version = 0;
         PyObject *type, *value, *traceback;
         PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
+        #if __PYX_LIMITED_VERSION_HEX >= 0x030B0000
+        minor_version = 11;
+        #else
+        if (!(version_info = PySys_GetObject("version_info"))) goto end;
+        if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end;
+        minor_version = PyLong_AsLong(py_minor_version);
+        Py_DECREF(py_minor_version);
+        if (minor_version == -1 && PyErr_Occurred()) goto end;
+        #endif
+        if (!(types_module = PyImport_ImportModule("types"))) goto end;
+        if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end;
+        if (minor_version <= 7) {
+            (void)p;
+            result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOO", a, k, l, s, f, code,
+                          c, n, v, fn, name, fline, lnos, fv, cell);
+        } else if (minor_version <= 10) {
+            result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOO", a,p, k, l, s, f, code,
+                          c, n, v, fn, name, fline, lnos, fv, cell);
+        } else {
+            if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end;
+            result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOO", a,p, k, l, s, f, code,
+                          c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell);
+        }
     end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
+        Py_XDECREF(code_type);
+        Py_XDECREF(exception_table);
+        Py_XDECREF(types_module);
         if (type) {
             PyErr_Restore(type, value, traceback);
         }
         return result;
     }
+    #ifndef CO_OPTIMIZED
+    #define CO_OPTIMIZED 0x0001
+    #endif
+    #ifndef CO_NEWLOCALS
+    #define CO_NEWLOCALS 0x0002
+    #endif
+    #ifndef CO_VARARGS
+    #define CO_VARARGS 0x0004
+    #endif
+    #ifndef CO_VARKEYWORDS
+    #define CO_VARKEYWORDS 0x0008
+    #endif
+    #ifndef CO_ASYNC_GENERATOR
+    #define CO_ASYNC_GENERATOR 0x0200
+    #endif
+    #ifndef CO_GENERATOR
+    #define CO_GENERATOR 0x0020
+    #endif
+    #ifndef CO_COROUTINE
+    #define CO_COROUTINE 0x0080
+    #endif
+#elif PY_VERSION_HEX >= 0x030B0000
+  static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
+                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
+                                                    PyObject *fv, PyObject *cell, PyObject* fn,
+                                                    PyObject *name, int fline, PyObject *lnos) {
+    PyCodeObject *result;
+    PyObject *empty_bytes = PyBytes_FromStringAndSize("", 0);
+    if (!empty_bytes) return NULL;
+    result =
+      #if PY_VERSION_HEX >= 0x030C0000
+        PyUnstable_Code_NewWithPosOnlyArgs
+      #else
+        PyCode_NewWithPosOnlyArgs
+      #endif
+        (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, empty_bytes);
+    Py_DECREF(empty_bytes);
+    return result;
+  }
 #elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
   #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
           PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
@@ -691,8 +772,13 @@ END: Cython Metadata */
   typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
                                                           Py_ssize_t nargs, PyObject *kwnames);
 #else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
+  #if PY_VERSION_HEX >= 0x030d00A4
+  #  define __Pyx_PyCFunctionFast PyCFunctionFast
+  #  define __Pyx_PyCFunctionFastWithKeywords PyCFunctionFastWithKeywords
+  #else
+  #  define __Pyx_PyCFunctionFast _PyCFunctionFast
+  #  define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
+  #endif
 #endif
 #if CYTHON_METH_FASTCALL
   #define __Pyx_METH_FASTCALL METH_FASTCALL
@@ -716,7 +802,32 @@ END: Cython Metadata */
   #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  0
   #define __Pyx_PyVectorcall_NARGS(n)  ((Py_ssize_t)(n))
 #endif
-#if PY_VERSION_HEX < 0x030900B1
+#if PY_MAJOR_VERSION >= 0x030900B1
+#define __Pyx_PyCFunction_CheckExact(func)  PyCFunction_CheckExact(func)
+#else
+#define __Pyx_PyCFunction_CheckExact(func)  PyCFunction_Check(func)
+#endif
+#define __Pyx_CyOrPyCFunction_Check(func)  PyCFunction_Check(func)
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func)  (((PyCFunctionObject*)(func))->m_ml->ml_meth)
+#elif !CYTHON_COMPILING_IN_LIMITED_API
+#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func)  PyCFunction_GET_FUNCTION(func)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_CyOrPyCFunction_GET_FLAGS(func)  (((PyCFunctionObject*)(func))->m_ml->ml_flags)
+static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) {
+    return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self;
+}
+#endif
+static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void *cfunc) {
+#if CYTHON_COMPILING_IN_LIMITED_API
+    return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc;
+#else
+    return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc;
+#endif
+}
+#define __Pyx_IsSameCFunction(func, cfunc)   __Pyx__IsSameCFunction(func, cfunc)
+#if __PYX_LIMITED_VERSION_HEX < 0x030900B1
   #define __Pyx_PyType_FromModuleAndSpec(m, s, b)  ((void)m, PyType_FromSpecWithBases(s, b))
   typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *);
 #else
@@ -742,6 +853,8 @@ END: Cython Metadata */
   #define __Pyx_PyThreadState_Current PyThreadState_Get()
 #elif !CYTHON_FAST_THREAD_STATE
   #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#elif PY_VERSION_HEX >= 0x030d00A1
+  #define __Pyx_PyThreadState_Current PyThreadState_GetUnchecked()
 #elif PY_VERSION_HEX >= 0x03060000
   #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
 #elif PY_VERSION_HEX >= 0x03000000
@@ -817,7 +930,7 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
         }
     #endif
 #endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030d0000 || defined(_PyDict_NewPresized)
 #define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
 #else
 #define __Pyx_PyDict_NewPresized(n)  PyDict_New()
@@ -829,7 +942,7 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
   #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
   #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
 #endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && PY_VERSION_HEX < 0x030d0000 && CYTHON_USE_UNICODE_INTERNALS
 #define __Pyx_PyDict_GetItemStrWithError(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
 static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) {
     PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name);
@@ -866,9 +979,14 @@ static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict,
   #define __Pyx_PyType_HasFeature(type, feature)  PyType_HasFeature(type, feature)
   #define __Pyx_PyObject_GetIterNextFunc(obj)  PyIter_Next
 #endif
+#if CYTHON_COMPILING_IN_LIMITED_API
+  #define __Pyx_SetItemOnTypeDict(tp, k, v) PyObject_GenericSetAttr((PyObject*)tp, k, v)
+#else
+  #define __Pyx_SetItemOnTypeDict(tp, k, v) PyDict_SetItem(tp->tp_dict, k, v)
+#endif
 #if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000
 #define __Pyx_PyHeapTypeObject_GC_Del(obj)  {\
-    PyTypeObject *type = Py_TYPE(obj);\
+    PyTypeObject *type = Py_TYPE((PyObject*)obj);\
     assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\
     PyObject_GC_Del(obj);\
     Py_DECREF(type);\
@@ -992,9 +1110,34 @@ static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict,
   #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
 #endif
 #if CYTHON_ASSUME_SAFE_MACROS
+  #define __Pyx_PySequence_ITEM(o, i) PySequence_ITEM(o, i)
   #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
-#else
+  #define __Pyx_PyTuple_SET_ITEM(o, i, v) (PyTuple_SET_ITEM(o, i, v), (0))
+  #define __Pyx_PyList_SET_ITEM(o, i, v) (PyList_SET_ITEM(o, i, v), (0))
+  #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_GET_SIZE(o)
+  #define __Pyx_PyList_GET_SIZE(o) PyList_GET_SIZE(o)
+  #define __Pyx_PySet_GET_SIZE(o) PySet_GET_SIZE(o)
+  #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_GET_SIZE(o)
+  #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_GET_SIZE(o)
+#else
+  #define __Pyx_PySequence_ITEM(o, i) PySequence_GetItem(o, i)
   #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
+  #define __Pyx_PyTuple_SET_ITEM(o, i, v) PyTuple_SetItem(o, i, v)
+  #define __Pyx_PyList_SET_ITEM(o, i, v) PyList_SetItem(o, i, v)
+  #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_Size(o)
+  #define __Pyx_PyList_GET_SIZE(o) PyList_Size(o)
+  #define __Pyx_PySet_GET_SIZE(o) PySet_Size(o)
+  #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_Size(o)
+  #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_Size(o)
+#endif
+#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1
+  #define __Pyx_PyImport_AddModuleRef(name) PyImport_AddModuleRef(name)
+#else
+  static CYTHON_INLINE PyObject *__Pyx_PyImport_AddModuleRef(const char *name) {
+      PyObject *module = PyImport_AddModule(name);
+      Py_XINCREF(module);
+      return module;
+  }
 #endif
 #if PY_MAJOR_VERSION >= 3
   #define PyIntObject                  PyLongObject
@@ -1074,7 +1217,7 @@ static CYTHON_INLINE float __PYX_NAN() {
 #endif
 
 #define __PYX_MARK_ERR_POS(f_index, lineno) \
-    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
+    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__;  (void)__pyx_clineno; }
 #define __PYX_ERR(f_index, lineno, Ln_error) \
     { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
 
@@ -1156,9 +1299,10 @@ static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
 #else
     #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
 #endif
+static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s);
 static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
 static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char*);
 #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
 #define __Pyx_PyBytes_FromString        PyBytes_FromString
 #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
@@ -1186,24 +1330,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
 #define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
 #define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
 #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u)
-{
-    const wchar_t *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#else
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#endif
 #define __Pyx_PyUnicode_FromOrdinal(o)       PyUnicode_FromOrdinal((int)o)
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
 #define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
 #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
 #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
@@ -1253,7 +1380,7 @@ static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
   #endif
   typedef Py_ssize_t  __Pyx_compact_pylong;
   typedef size_t  __Pyx_compact_upylong;
-  #else  // Py < 3.12
+  #else
   #define __Pyx_PyLong_IsNeg(x)  (Py_SIZE(x) < 0)
   #define __Pyx_PyLong_IsNonNeg(x)  (Py_SIZE(x) >= 0)
   #define __Pyx_PyLong_IsZero(x)  (Py_SIZE(x) == 0)
@@ -1274,6 +1401,7 @@ static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
   #endif
 #endif
 #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+#include <string.h>
 static int __Pyx_sys_getdefaultencoding_not_ascii;
 static int __Pyx_init_sys_getdefaultencoding_params(void) {
     PyObject* sys;
@@ -1324,6 +1452,7 @@ static int __Pyx_init_sys_getdefaultencoding_params(void) {
 #else
 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
 #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+#include <string.h>
 static char* __PYX_DEFAULT_STRING_ENCODING;
 static int __Pyx_init_sys_getdefaultencoding_params(void) {
     PyObject* sys;
@@ -1371,7 +1500,7 @@ static const char *__pyx_filename;
 #if !defined(CYTHON_CCOMPLEX)
   #if defined(__cplusplus)
     #define CYTHON_CCOMPLEX 1
-  #elif (defined(_Complex_I) && !defined(_MSC_VER)) || ((defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) && !defined(__STDC_NO_COMPLEX__))
+  #elif (defined(_Complex_I) && !defined(_MSC_VER)) || ((defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) && !defined(__STDC_NO_COMPLEX__) && !defined(_MSC_VER))
     #define CYTHON_CCOMPLEX 1
   #else
     #define CYTHON_CCOMPLEX 0
@@ -1397,6 +1526,11 @@ static const char *__pyx_f[] = {
   "type.pxd",
 };
 /* #### Code section: utility_code_proto_before_types ### */
+/* ForceInitThreads.proto */
+#ifndef __PYX_FORCE_INIT_THREADS
+  #define __PYX_FORCE_INIT_THREADS 0
+#endif
+
 /* BufferFormatStructs.proto */
 struct __Pyx_StructField_;
 #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
@@ -1434,7 +1568,7 @@ typedef struct {
 
 /* #### Code section: numeric_typedefs ### */
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":731
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":730
  * # in Cython to enable them only on the right systems.
  * 
  * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
@@ -1443,7 +1577,7 @@ typedef struct {
  */
 typedef npy_int8 __pyx_t_5numpy_int8_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":732
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":731
  * 
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
@@ -1452,7 +1586,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t;
  */
 typedef npy_int16 __pyx_t_5numpy_int16_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":733
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":732
  * ctypedef npy_int8       int8_t
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
@@ -1461,7 +1595,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t;
  */
 typedef npy_int32 __pyx_t_5numpy_int32_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":734
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":733
  * ctypedef npy_int16      int16_t
  * ctypedef npy_int32      int32_t
  * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
@@ -1470,7 +1604,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t;
  */
 typedef npy_int64 __pyx_t_5numpy_int64_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":738
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":737
  * #ctypedef npy_int128     int128_t
  * 
  * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
@@ -1479,7 +1613,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t;
  */
 typedef npy_uint8 __pyx_t_5numpy_uint8_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":739
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":738
  * 
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
@@ -1488,7 +1622,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t;
  */
 typedef npy_uint16 __pyx_t_5numpy_uint16_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":740
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":739
  * ctypedef npy_uint8      uint8_t
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
@@ -1497,7 +1631,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t;
  */
 typedef npy_uint32 __pyx_t_5numpy_uint32_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":741
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":740
  * ctypedef npy_uint16     uint16_t
  * ctypedef npy_uint32     uint32_t
  * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
@@ -1506,7 +1640,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t;
  */
 typedef npy_uint64 __pyx_t_5numpy_uint64_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":745
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":744
  * #ctypedef npy_uint128    uint128_t
  * 
  * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
@@ -1515,7 +1649,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t;
  */
 typedef npy_float32 __pyx_t_5numpy_float32_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":746
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":745
  * 
  * ctypedef npy_float32    float32_t
  * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
@@ -1524,61 +1658,43 @@ typedef npy_float32 __pyx_t_5numpy_float32_t;
  */
 typedef npy_float64 __pyx_t_5numpy_float64_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":755
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":754
  * # The int types are mapped a bit surprising --
  * # numpy.int corresponds to 'l' and numpy.long to 'q'
  * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   long_t
  * ctypedef npy_longlong   longlong_t
+ * 
  */
 typedef npy_long __pyx_t_5numpy_int_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":756
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":755
  * # numpy.int corresponds to 'l' and numpy.long to 'q'
  * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   longlong_t
- * 
- */
-typedef npy_longlong __pyx_t_5numpy_long_t;
-
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":757
- * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t
  * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
  * 
  * ctypedef npy_ulong      uint_t
  */
 typedef npy_longlong __pyx_t_5numpy_longlong_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":759
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":757
  * ctypedef npy_longlong   longlong_t
  * 
  * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulong_t
  * ctypedef npy_ulonglong  ulonglong_t
+ * 
  */
 typedef npy_ulong __pyx_t_5numpy_uint_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":760
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":758
  * 
  * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulonglong_t
- * 
- */
-typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
-
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":761
- * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t
  * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
  * 
  * ctypedef npy_intp       intp_t
  */
 typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":763
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":760
  * ctypedef npy_ulonglong  ulonglong_t
  * 
  * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
@@ -1587,7 +1703,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
  */
 typedef npy_intp __pyx_t_5numpy_intp_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":764
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":761
  * 
  * ctypedef npy_intp       intp_t
  * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
@@ -1596,7 +1712,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t;
  */
 typedef npy_uintp __pyx_t_5numpy_uintp_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":766
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":763
  * ctypedef npy_uintp      uintp_t
  * 
  * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
@@ -1605,7 +1721,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t;
  */
 typedef npy_double __pyx_t_5numpy_float_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":767
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":764
  * 
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
@@ -1614,7 +1730,7 @@ typedef npy_double __pyx_t_5numpy_float_t;
  */
 typedef npy_double __pyx_t_5numpy_double_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":768
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":765
  * ctypedef npy_double     float_t
  * ctypedef npy_double     double_t
  * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
@@ -1678,7 +1794,7 @@ static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(do
 
 /*--- Type declarations ---*/
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":770
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":767
  * ctypedef npy_longdouble longdouble_t
  * 
  * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
@@ -1687,7 +1803,7 @@ static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(do
  */
 typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":771
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":768
  * 
  * ctypedef npy_cfloat      cfloat_t
  * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
@@ -1696,7 +1812,7 @@ typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
  */
 typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":772
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":769
  * ctypedef npy_cfloat      cfloat_t
  * ctypedef npy_cdouble     cdouble_t
  * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
@@ -1705,7 +1821,7 @@ typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
  */
 typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":774
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":771
  * ctypedef npy_clongdouble clongdouble_t
  * 
  * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
@@ -1909,7 +2025,20 @@ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int eq
 static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
 
 /* fastcall.proto */
-#define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i)
+#if CYTHON_AVOID_BORROWED_REFS
+    #define __Pyx_Arg_VARARGS(args, i) PySequence_GetItem(args, i)
+#elif CYTHON_ASSUME_SAFE_MACROS
+    #define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i)
+#else
+    #define __Pyx_Arg_VARARGS(args, i) PyTuple_GetItem(args, i)
+#endif
+#if CYTHON_AVOID_BORROWED_REFS
+    #define __Pyx_Arg_NewRef_VARARGS(arg) __Pyx_NewRef(arg)
+    #define __Pyx_Arg_XDECREF_VARARGS(arg) Py_XDECREF(arg)
+#else
+    #define __Pyx_Arg_NewRef_VARARGS(arg) arg
+    #define __Pyx_Arg_XDECREF_VARARGS(arg)
+#endif
 #define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds)
 #define __Pyx_KwValues_VARARGS(args, nargs) NULL
 #define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s)
@@ -1919,15 +2048,24 @@ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int
     #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds)
     #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs))
     static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s);
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000
+    CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues);
+  #else
     #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw)
+  #endif
+    #define __Pyx_Arg_NewRef_FASTCALL(arg) arg  /* no-op, __Pyx_Arg_FASTCALL is direct and this needs
+                                                   to have the same reference counting */
+    #define __Pyx_Arg_XDECREF_FASTCALL(arg)
 #else
     #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS
     #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS
     #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS
     #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS
     #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS
+    #define __Pyx_Arg_NewRef_FASTCALL(arg) __Pyx_Arg_NewRef_VARARGS(arg)
+    #define __Pyx_Arg_XDECREF_FASTCALL(arg) __Pyx_Arg_XDECREF_VARARGS(arg)
 #endif
-#if CYTHON_COMPILING_IN_CPYTHON
+#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
 #define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start)
 #define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start)
 #else
@@ -1981,22 +2119,22 @@ static Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 };
 static void __Pyx_RaiseBufferIndexError(int axis);
 
 /* TypeImport.proto */
-#ifndef __PYX_HAVE_RT_ImportType_proto_3_0_0
-#define __PYX_HAVE_RT_ImportType_proto_3_0_0
+#ifndef __PYX_HAVE_RT_ImportType_proto_3_0_10
+#define __PYX_HAVE_RT_ImportType_proto_3_0_10
 #if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L
 #include <stdalign.h>
 #endif
 #if (defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) || __cplusplus >= 201103L
-#define __PYX_GET_STRUCT_ALIGNMENT_3_0_0(s) alignof(s)
+#define __PYX_GET_STRUCT_ALIGNMENT_3_0_10(s) alignof(s)
 #else
-#define __PYX_GET_STRUCT_ALIGNMENT_3_0_0(s) sizeof(void*)
+#define __PYX_GET_STRUCT_ALIGNMENT_3_0_10(s) sizeof(void*)
 #endif
-enum __Pyx_ImportType_CheckSize_3_0_0 {
-   __Pyx_ImportType_CheckSize_Error_3_0_0 = 0,
-   __Pyx_ImportType_CheckSize_Warn_3_0_0 = 1,
-   __Pyx_ImportType_CheckSize_Ignore_3_0_0 = 2
+enum __Pyx_ImportType_CheckSize_3_0_10 {
+   __Pyx_ImportType_CheckSize_Error_3_0_10 = 0,
+   __Pyx_ImportType_CheckSize_Warn_3_0_10 = 1,
+   __Pyx_ImportType_CheckSize_Ignore_3_0_10 = 2
 };
-static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_0 check_size);
+static PyTypeObject *__Pyx_ImportType_3_0_10(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_10 check_size);
 #endif
 
 /* Import.proto */
@@ -2027,7 +2165,22 @@ static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec
 #endif
 
 /* PyMethodNew.proto */
-#if PY_MAJOR_VERSION >= 3
+#if CYTHON_COMPILING_IN_LIMITED_API
+static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
+    PyObject *typesModule=NULL, *methodType=NULL, *result=NULL;
+    CYTHON_UNUSED_VAR(typ);
+    if (!self)
+        return __Pyx_NewRef(func);
+    typesModule = PyImport_ImportModule("types");
+    if (!typesModule) return NULL;
+    methodType = PyObject_GetAttrString(typesModule, "MethodType");
+    Py_DECREF(typesModule);
+    if (!methodType) return NULL;
+    result = PyObject_CallFunctionObjArgs(methodType, func, self, NULL);
+    Py_DECREF(methodType);
+    return result;
+}
+#elif PY_MAJOR_VERSION >= 3
 static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
     CYTHON_UNUSED_VAR(typ);
     if (!self)
@@ -2051,7 +2204,7 @@ static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, _
 #define __Pyx_CYFUNCTION_COROUTINE     0x08
 #define __Pyx_CyFunction_GetClosure(f)\
     (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1
+#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
   #define __Pyx_CyFunction_GetClassObj(f)\
       (((__pyx_CyFunctionObject *) (f))->func_classobj)
 #else
@@ -2065,7 +2218,10 @@ static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, _
 #define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
     ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
 typedef struct {
-#if PY_VERSION_HEX < 0x030900B1
+#if CYTHON_COMPILING_IN_LIMITED_API
+    PyObject_HEAD
+    PyObject *func;
+#elif PY_VERSION_HEX < 0x030900B1
     PyCFunctionObject func;
 #else
     PyCMethodObject func;
@@ -2073,7 +2229,7 @@ typedef struct {
 #if CYTHON_BACKPORT_VECTORCALL
     __pyx_vectorcallfunc func_vectorcall;
 #endif
-#if PY_VERSION_HEX < 0x030500A0
+#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
     PyObject *func_weakreflist;
 #endif
     PyObject *func_dict;
@@ -2083,12 +2239,12 @@ typedef struct {
     PyObject *func_globals;
     PyObject *func_code;
     PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1
+#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
     PyObject *func_classobj;
 #endif
     void *defaults;
     int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
+    size_t defaults_size;
     int flags;
     PyObject *defaults_tuple;
     PyObject *defaults_kwdict;
@@ -2096,9 +2252,13 @@ typedef struct {
     PyObject *func_annotations;
     PyObject *func_is_coroutine;
 } __pyx_CyFunctionObject;
+#undef __Pyx_CyOrPyCFunction_Check
 #define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
+#define __Pyx_CyOrPyCFunction_Check(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
 #define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
+static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc);
+#undef __Pyx_IsSameCFunction
+#define __Pyx_IsSameCFunction(func, cfunc)   __Pyx__IsSameCyOrCFunction(func, cfunc)
 static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
                                       int flags, PyObject* qualname,
                                       PyObject *closure,
@@ -2357,7 +2517,8 @@ static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObj
 #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
 
 /* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
+static unsigned long __Pyx_get_runtime_version(void);
+static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer);
 
 /* InitStrings.proto */
 static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
@@ -2791,7 +2952,7 @@ static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
 #define __pyx_codeobj__7 __pyx_mstate_global->__pyx_codeobj__7
 /* #### Code section: module_code ### */
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":245
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":245
  * 
  *         @property
  *         cdef inline PyObject* base(self) nogil:             # <<<<<<<<<<<<<<
@@ -2802,7 +2963,7 @@ static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
 static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject *__pyx_v_self) {
   PyObject *__pyx_r;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":248
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":248
  *             """Returns a borrowed reference to the object owning the data/memory.
  *             """
  *             return PyArray_BASE(self)             # <<<<<<<<<<<<<<
@@ -2812,7 +2973,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   __pyx_r = PyArray_BASE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":245
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":245
  * 
  *         @property
  *         cdef inline PyObject* base(self) nogil:             # <<<<<<<<<<<<<<
@@ -2825,7 +2986,7 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":251
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":251
  * 
  *         @property
  *         cdef inline dtype descr(self):             # <<<<<<<<<<<<<<
@@ -2837,9 +2998,9 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   PyArray_Descr *__pyx_r = NULL;
   __Pyx_RefNannyDeclarations
   PyArray_Descr *__pyx_t_1;
-  __Pyx_RefNannySetupContext("descr", 0);
+  __Pyx_RefNannySetupContext("descr", 1);
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":254
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":254
  *             """Returns an owned reference to the dtype of the array.
  *             """
  *             return <dtype>PyArray_DESCR(self)             # <<<<<<<<<<<<<<
@@ -2852,7 +3013,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   __pyx_r = ((PyArray_Descr *)__pyx_t_1);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":251
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":251
  * 
  *         @property
  *         cdef inline dtype descr(self):             # <<<<<<<<<<<<<<
@@ -2867,7 +3028,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":257
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":257
  * 
  *         @property
  *         cdef inline int ndim(self) nogil:             # <<<<<<<<<<<<<<
@@ -2878,7 +3039,7 @@ static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArray
 static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx_v_self) {
   int __pyx_r;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":260
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":260
  *             """Returns the number of dimensions in the array.
  *             """
  *             return PyArray_NDIM(self)             # <<<<<<<<<<<<<<
@@ -2888,7 +3049,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   __pyx_r = PyArray_NDIM(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":257
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":257
  * 
  *         @property
  *         cdef inline int ndim(self) nogil:             # <<<<<<<<<<<<<<
@@ -2901,7 +3062,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":263
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":263
  * 
  *         @property
  *         cdef inline npy_intp *shape(self) nogil:             # <<<<<<<<<<<<<<
@@ -2912,7 +3073,7 @@ static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":268
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":268
  *             Can return NULL for 0-dimensional arrays.
  *             """
  *             return PyArray_DIMS(self)             # <<<<<<<<<<<<<<
@@ -2922,7 +3083,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   __pyx_r = PyArray_DIMS(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":263
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":263
  * 
  *         @property
  *         cdef inline npy_intp *shape(self) nogil:             # <<<<<<<<<<<<<<
@@ -2935,7 +3096,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":271
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":271
  * 
  *         @property
  *         cdef inline npy_intp *strides(self) nogil:             # <<<<<<<<<<<<<<
@@ -2946,7 +3107,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObjec
 static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayObject *__pyx_v_self) {
   npy_intp *__pyx_r;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":275
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":275
  *             The number of elements matches the number of dimensions of the array (ndim).
  *             """
  *             return PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
@@ -2956,7 +3117,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   __pyx_r = PyArray_STRIDES(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":271
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":271
  * 
  *         @property
  *         cdef inline npy_intp *strides(self) nogil:             # <<<<<<<<<<<<<<
@@ -2969,7 +3130,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":278
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":278
  * 
  *         @property
  *         cdef inline npy_intp size(self) nogil:             # <<<<<<<<<<<<<<
@@ -2980,7 +3141,7 @@ static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayO
 static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *__pyx_v_self) {
   npy_intp __pyx_r;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":281
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":281
  *             """Returns the total size (in number of elements) of the array.
  *             """
  *             return PyArray_SIZE(self)             # <<<<<<<<<<<<<<
@@ -2990,7 +3151,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   __pyx_r = PyArray_SIZE(__pyx_v_self);
   goto __pyx_L0;
 
-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":278
+  /* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":278
  * 
  *         @property
  *         cdef inline npy_intp size(self) nogil:             # <<<<<<<<<<<<<<
@@ -3003,7 +3164,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
   return __pyx_r;
 }
 
-/* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":284
+/* "../../../tmp/pip-build-env-c4ocyr9s/overlay/lib/python3.12/site-packages/numpy/__init__.cython-30.pxd":284
  * 
  *         @property
  *         cdef inline char* data(self) nogil:             # <<<<<<<<<<<<<<
@@ -3014,7 +3175,7 @@ static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *
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-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":976
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-    /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":977
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-    /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":976
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-      /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":984
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-      /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":990
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-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":988
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-      /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":996
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-      /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":995
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-  /* "../../../store/msrad/utils/anaconda3-wolfensb/envs/rainforest_tests/lib/python3.10/site-packages/numpy/__init__.cython-30.pxd":1038
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+  __pyx_ptype_5numpy_number = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "number", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_number) __PYX_ERR(1, 811, __pyx_L1_error)
+  __pyx_ptype_5numpy_integer = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "integer", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_integer) __PYX_ERR(1, 813, __pyx_L1_error)
+  __pyx_ptype_5numpy_signedinteger = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "signedinteger", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_signedinteger) __PYX_ERR(1, 815, __pyx_L1_error)
+  __pyx_ptype_5numpy_unsignedinteger = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "unsignedinteger", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_unsignedinteger) __PYX_ERR(1, 817, __pyx_L1_error)
+  __pyx_ptype_5numpy_inexact = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "inexact", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_inexact) __PYX_ERR(1, 819, __pyx_L1_error)
+  __pyx_ptype_5numpy_floating = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "floating", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_floating) __PYX_ERR(1, 821, __pyx_L1_error)
+  __pyx_ptype_5numpy_complexfloating = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "complexfloating", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_complexfloating) __PYX_ERR(1, 823, __pyx_L1_error)
+  __pyx_ptype_5numpy_flexible = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "flexible", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_flexible) __PYX_ERR(1, 825, __pyx_L1_error)
+  __pyx_ptype_5numpy_character = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "character", sizeof(PyObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyObject),__Pyx_ImportType_CheckSize_Warn_3_0_10); if (!__pyx_ptype_5numpy_character) __PYX_ERR(1, 827, __pyx_L1_error)
+  __pyx_ptype_5numpy_ufunc = __Pyx_ImportType_3_0_10(__pyx_t_1, "numpy", "ufunc", sizeof(PyUFuncObject), __PYX_GET_STRUCT_ALIGNMENT_3_0_10(PyUFuncObject),__Pyx_ImportType_CheckSize_Ignore_3_0_10); if (!__pyx_ptype_5numpy_ufunc) __PYX_ERR(1, 866, __pyx_L1_error)
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
   __Pyx_RefNannyFinishContext();
   return 0;
@@ -5444,7 +5697,7 @@ static CYTHON_SMALL_CODE int __pyx_pymod_exec_add_at(PyObject *__pyx_pyinit_modu
   __pyx_t_1 = PyModule_Create(&__pyx_moduledef); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)
   {
     int add_module_result = PyState_AddModule(__pyx_t_1, &__pyx_moduledef);
-    __pyx_t_1 = 0; /* transfer ownership from __pyx_t_1 to add_at pseudovariable */
+    __pyx_t_1 = 0; /* transfer ownership from __pyx_t_1 to "add_at" pseudovariable */
     if (unlikely((add_module_result < 0))) __PYX_ERR(0, 1, __pyx_L1_error)
     pystate_addmodule_run = 1;
   }
@@ -5456,10 +5709,8 @@ static CYTHON_SMALL_CODE int __pyx_pymod_exec_add_at(PyObject *__pyx_pyinit_modu
   CYTHON_UNUSED_VAR(__pyx_t_1);
   __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error)
   Py_INCREF(__pyx_d);
-  __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error)
-  Py_INCREF(__pyx_b);
-  __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error)
-  Py_INCREF(__pyx_cython_runtime);
+  __pyx_b = __Pyx_PyImport_AddModuleRef(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error)
+  __pyx_cython_runtime = __Pyx_PyImport_AddModuleRef((const char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error)
   if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error)
   #if CYTHON_REFNANNY
 __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
@@ -5471,7 +5722,7 @@ if (!__Pyx_RefNanny) {
 }
 #endif
   __Pyx_RefNannySetupContext("__Pyx_PyMODINIT_FUNC PyInit_add_at(void)", 0);
-  if (__Pyx_check_binary_version() < 0) __PYX_ERR(0, 1, __pyx_L1_error)
+  if (__Pyx_check_binary_version(__PYX_LIMITED_VERSION_HEX, __Pyx_get_runtime_version(), CYTHON_COMPILING_IN_LIMITED_API) < 0) __PYX_ERR(0, 1, __pyx_L1_error)
   #ifdef __Pxy_PyFrame_Initialize_Offsets
   __Pxy_PyFrame_Initialize_Offsets();
   #endif
@@ -5718,6 +5969,8 @@ static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObjec
     tmp_value = tstate->current_exception;
     tstate->current_exception = value;
     Py_XDECREF(tmp_value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
 #else
     PyObject *tmp_type, *tmp_value, *tmp_tb;
     tmp_type = tstate->curexc_type;
@@ -5775,14 +6028,20 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject
 #endif
 
 /* PyObjectGetAttrStrNoError */
+#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1
 static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
     __Pyx_PyThreadState_declare
     __Pyx_PyThreadState_assign
     if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
         __Pyx_PyErr_Clear();
 }
+#endif
 static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
     PyObject *result;
+#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1
+    (void) PyObject_GetOptionalAttr(obj, attr_name, &result);
+    return result;
+#else
 #if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
     PyTypeObject* tp = Py_TYPE(obj);
     if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
@@ -5794,6 +6053,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, P
         __Pyx_PyObject_GetAttrStr_ClearAttributeError();
     }
     return result;
+#endif
 }
 
 /* GetBuiltinName */
@@ -5994,8 +6254,13 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg
     ternaryfunc call = Py_TYPE(func)->tp_call;
     if (unlikely(!call))
         return PyObject_Call(func, arg, kw);
+    #if PY_MAJOR_VERSION < 3
     if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
         return NULL;
+    #else
+    if (unlikely(Py_EnterRecursiveCall(" while calling a Python object")))
+        return NULL;
+    #endif
     result = (*call)(func, arg, kw);
     Py_LeaveRecursiveCall();
     if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
@@ -6367,12 +6632,30 @@ static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyO
     {
         int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ);
         if (unlikely(eq != 0)) {
-            if (unlikely(eq < 0)) return NULL;  // error
+            if (unlikely(eq < 0)) return NULL;
             return kwvalues[i];
         }
     }
-    return NULL;  // not found (no exception set)
+    return NULL;
 }
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000
+CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues) {
+    Py_ssize_t i, nkwargs = PyTuple_GET_SIZE(kwnames);
+    PyObject *dict;
+    dict = PyDict_New();
+    if (unlikely(!dict))
+        return NULL;
+    for (i=0; i<nkwargs; i++) {
+        PyObject *key = PyTuple_GET_ITEM(kwnames, i);
+        if (unlikely(PyDict_SetItem(dict, key, kwvalues[i]) < 0))
+            goto bad;
+    }
+    return dict;
+bad:
+    Py_DECREF(dict);
+    return NULL;
+}
+#endif
 #endif
 
 /* RaiseArgTupleInvalid */
@@ -6431,22 +6714,52 @@ static int __Pyx_ParseOptionalKeywords(
     PyObject*** first_kw_arg = argnames + num_pos_args;
     int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
     while (1) {
+        Py_XDECREF(key); key = NULL;
+        Py_XDECREF(value); value = NULL;
         if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
+            Py_ssize_t size;
+#if CYTHON_ASSUME_SAFE_MACROS
+            size = PyTuple_GET_SIZE(kwds);
+#else
+            size = PyTuple_Size(kwds);
+            if (size < 0) goto bad;
+#endif
+            if (pos >= size) break;
+#if CYTHON_AVOID_BORROWED_REFS
+            key = __Pyx_PySequence_ITEM(kwds, pos);
+            if (!key) goto bad;
+#elif CYTHON_ASSUME_SAFE_MACROS
             key = PyTuple_GET_ITEM(kwds, pos);
+#else
+            key = PyTuple_GetItem(kwds, pos);
+            if (!key) goto bad;
+#endif
             value = kwvalues[pos];
             pos++;
         }
         else
         {
             if (!PyDict_Next(kwds, &pos, &key, &value)) break;
+#if CYTHON_AVOID_BORROWED_REFS
+            Py_INCREF(key);
+#endif
         }
         name = first_kw_arg;
         while (*name && (**name != key)) name++;
         if (*name) {
             values[name-argnames] = value;
+#if CYTHON_AVOID_BORROWED_REFS
+            Py_INCREF(value);
+            Py_DECREF(key);
+#endif
+            key = NULL;
+            value = NULL;
             continue;
         }
+#if !CYTHON_AVOID_BORROWED_REFS
+        Py_INCREF(key);
+#endif
+        Py_INCREF(value);
         name = first_kw_arg;
         #if PY_MAJOR_VERSION < 3
         if (likely(PyString_Check(key))) {
@@ -6454,6 +6767,9 @@ static int __Pyx_ParseOptionalKeywords(
                 if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
                         && _PyString_Eq(**name, key)) {
                     values[name-argnames] = value;
+#if CYTHON_AVOID_BORROWED_REFS
+                    value = NULL;
+#endif
                     break;
                 }
                 name++;
@@ -6483,6 +6799,9 @@ static int __Pyx_ParseOptionalKeywords(
                 if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
                 if (cmp == 0) {
                     values[name-argnames] = value;
+#if CYTHON_AVOID_BORROWED_REFS
+                    value = NULL;
+#endif
                     break;
                 }
                 name++;
@@ -6509,6 +6828,8 @@ static int __Pyx_ParseOptionalKeywords(
             goto invalid_keyword;
         }
     }
+    Py_XDECREF(key);
+    Py_XDECREF(value);
     return 0;
 arg_passed_twice:
     __Pyx_RaiseDoubleKeywordsError(function_name, key);
@@ -6528,6 +6849,8 @@ static int __Pyx_ParseOptionalKeywords(
         function_name, key);
     #endif
 bad:
+    Py_XDECREF(key);
+    Py_XDECREF(value);
     return -1;
 }
 
@@ -6894,7 +7217,7 @@ static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
   ctx->is_complex = 0;
   return 0;
 }
-static PyObject *
+static int
 __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
 {
     const char *ts = *tsp;
@@ -6903,9 +7226,9 @@ __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
     if (ctx->new_count != 1) {
         PyErr_SetString(PyExc_ValueError,
                         "Cannot handle repeated arrays in format string");
-        return NULL;
+        return -1;
     }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return -1;
     ndim = ctx->head->field->type->ndim;
     while (*ts && *ts != ')') {
         switch (*ts) {
@@ -6913,29 +7236,35 @@ __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
             default:  break;
         }
         number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
+        if (number == -1) return -1;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) {
+            PyErr_Format(PyExc_ValueError,
                         "Expected a dimension of size %zu, got %d",
                         ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
+            return -1;
+        }
+        if (*ts != ',' && *ts != ')') {
+            PyErr_Format(PyExc_ValueError,
                                 "Expected a comma in format string, got '%c'", *ts);
+            return -1;
+        }
         if (*ts == ',') ts++;
         i++;
     }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+    if (i != ndim) {
+        PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
                             ctx->head->field->type->ndim, i);
+        return -1;
+    }
     if (!*ts) {
         PyErr_SetString(PyExc_ValueError,
                         "Unexpected end of format string, expected ')'");
-        return NULL;
+        return -1;
     }
     ctx->is_valid_array = 1;
     ctx->new_count = 1;
     *tsp = ++ts;
-    return Py_None;
+    return 0;
 }
 static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
   int got_Z = 0;
@@ -7061,7 +7390,7 @@ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const cha
         ++ts;
         break;
       case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        if (__pyx_buffmt_parse_array(ctx, &ts) < 0) return NULL;
         break;
       default:
         {
@@ -7127,10 +7456,10 @@ fail:;
 }
 
 /* TypeImport */
-  #ifndef __PYX_HAVE_RT_ImportType_3_0_0
-#define __PYX_HAVE_RT_ImportType_3_0_0
-static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject *module, const char *module_name, const char *class_name,
-    size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_0 check_size)
+  #ifndef __PYX_HAVE_RT_ImportType_3_0_10
+#define __PYX_HAVE_RT_ImportType_3_0_10
+static PyTypeObject *__Pyx_ImportType_3_0_10(PyObject *module, const char *module_name, const char *class_name,
+    size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_10 check_size)
 {
     PyObject *result = 0;
     char warning[200];
@@ -7184,7 +7513,7 @@ static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject *module, const char *module
             module_name, class_name, size, basicsize+itemsize);
         goto bad;
     }
-    if (check_size == __Pyx_ImportType_CheckSize_Error_3_0_0 &&
+    if (check_size == __Pyx_ImportType_CheckSize_Error_3_0_10 &&
             ((size_t)basicsize > size || (size_t)(basicsize + itemsize) < size)) {
         PyErr_Format(PyExc_ValueError,
             "%.200s.%.200s size changed, may indicate binary incompatibility. "
@@ -7192,7 +7521,7 @@ static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject *module, const char *module
             module_name, class_name, size, basicsize, basicsize+itemsize);
         goto bad;
     }
-    else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_0_0 && (size_t)basicsize > size) {
+    else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_0_10 && (size_t)basicsize > size) {
         PyOS_snprintf(warning, sizeof(warning),
             "%s.%s size changed, may indicate binary incompatibility. "
             "Expected %zd from C header, got %zd from PyObject",
@@ -7229,14 +7558,9 @@ static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject *module, const char *module
     {
         #if PY_MAJOR_VERSION >= 3
         if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
+            if (strchr(__Pyx_MODULE_NAME, '.') != NULL) {
                 module = PyImport_ImportModuleLevelObject(
                     name, __pyx_d, empty_dict, from_list, 1);
-                #endif
                 if (unlikely(!module)) {
                     if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
                         goto bad;
@@ -7255,14 +7579,9 @@ static PyTypeObject *__Pyx_ImportType_3_0_0(PyObject *module, const char *module
                 name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
             Py_DECREF(py_level);
             #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
             module = PyImport_ImportModuleLevelObject(
                 name, __pyx_d, empty_dict, from_list, level);
             #endif
-            #endif
         }
     }
 bad:
@@ -7477,10 +7796,7 @@ static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject
 
 /* FetchSharedCythonModule */
   static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
+    return __Pyx_PyImport_AddModuleRef((char*) __PYX_ABI_MODULE_NAME);
 }
 
 /* FetchCommonType */
@@ -7641,8 +7957,22 @@ static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, _
 #endif
 
 /* CythonFunctionShared */
-  static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
+  #if CYTHON_COMPILING_IN_LIMITED_API
+static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) {
+    if (__Pyx_CyFunction_Check(func)) {
+        return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc;
+    } else if (PyCFunction_Check(func)) {
+        return PyCFunction_GetFunction(func) == (PyCFunction) cfunc;
+    }
+    return 0;
+}
+#else
+static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) {
+    return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc;
+}
+#endif
+static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
+#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
     __Pyx_Py_XDECREF_SET(
         __Pyx_CyFunction_GetClassObj(f),
             ((classobj) ? __Pyx_NewRef(classobj) : NULL));
@@ -7657,6 +7987,10 @@ __Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
 {
     CYTHON_UNUSED_VAR(closure);
     if (unlikely(op->func_doc == NULL)) {
+#if CYTHON_COMPILING_IN_LIMITED_API
+        op->func_doc = PyObject_GetAttrString(op->func, "__doc__");
+        if (unlikely(!op->func_doc)) return NULL;
+#else
         if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
 #if PY_MAJOR_VERSION >= 3
             op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
@@ -7669,6 +8003,7 @@ __Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
             Py_INCREF(Py_None);
             return Py_None;
         }
+#endif
     }
     Py_INCREF(op->func_doc);
     return op->func_doc;
@@ -7689,7 +8024,9 @@ __Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
 {
     CYTHON_UNUSED_VAR(context);
     if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
+#if CYTHON_COMPILING_IN_LIMITED_API
+        op->func_name = PyObject_GetAttrString(op->func, "__name__");
+#elif PY_MAJOR_VERSION >= 3
         op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
 #else
         op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
@@ -7808,10 +8145,10 @@ __Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
     op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
     Py_INCREF(op->defaults_kwdict);
     #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
+    op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0);
     if (unlikely(!op->defaults_tuple)) result = -1;
     else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
+        op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1);
         if (unlikely(!op->defaults_kwdict)) result = -1;
     }
     #endif
@@ -7920,7 +8257,15 @@ __Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
         fromlist = PyList_New(1);
         if (unlikely(!fromlist)) return NULL;
         Py_INCREF(marker);
+#if CYTHON_ASSUME_SAFE_MACROS
         PyList_SET_ITEM(fromlist, 0, marker);
+#else
+        if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) {
+            Py_DECREF(marker);
+            Py_DECREF(fromlist);
+            return NULL;
+        }
+#endif
         module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
         Py_DECREF(fromlist);
         if (unlikely(!module)) goto ignore;
@@ -7936,6 +8281,18 @@ __Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
     op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
     return __Pyx_NewRef(op->func_is_coroutine);
 }
+#if CYTHON_COMPILING_IN_LIMITED_API
+static PyObject *
+__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) {
+    CYTHON_UNUSED_VAR(context);
+    return PyObject_GetAttrString(op->func, "__module__");
+}
+static int
+__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
+    CYTHON_UNUSED_VAR(context);
+    return PyObject_SetAttrString(op->func, "__module__", value);
+}
+#endif
 static PyGetSetDef __pyx_CyFunction_getsets[] = {
     {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
     {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
@@ -7955,20 +8312,27 @@ static PyGetSetDef __pyx_CyFunction_getsets[] = {
     {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
     {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
     {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
+#if CYTHON_COMPILING_IN_LIMITED_API
+    {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0},
+#endif
     {0, 0, 0, 0, 0}
 };
 static PyMemberDef __pyx_CyFunction_members[] = {
+#if !CYTHON_COMPILING_IN_LIMITED_API
     {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
+#endif
 #if CYTHON_USE_TYPE_SPECS
     {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
 #if CYTHON_METH_FASTCALL
 #if CYTHON_BACKPORT_VECTORCALL
     {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
 #else
+#if !CYTHON_COMPILING_IN_LIMITED_API
     {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
 #endif
 #endif
-#if PY_VERSION_HEX < 0x030500A0
+#endif
+#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
     {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
 #else
     {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
@@ -7991,30 +8355,40 @@ static PyMethodDef __pyx_CyFunction_methods[] = {
     {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
     {0, 0, 0, 0}
 };
-#if PY_VERSION_HEX < 0x030500A0
+#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API
 #define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
 #else
 #define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
 #endif
 static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
                                        PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
+#if !CYTHON_COMPILING_IN_LIMITED_API
     PyCFunctionObject *cf = (PyCFunctionObject*) op;
+#endif
     if (unlikely(op == NULL))
         return NULL;
+#if CYTHON_COMPILING_IN_LIMITED_API
+    op->func = PyCFunction_NewEx(ml, (PyObject*)op, module);
+    if (unlikely(!op->func)) return NULL;
+#endif
     op->flags = flags;
     __Pyx_CyFunction_weakreflist(op) = NULL;
+#if !CYTHON_COMPILING_IN_LIMITED_API
     cf->m_ml = ml;
     cf->m_self = (PyObject *) op;
+#endif
     Py_XINCREF(closure);
     op->func_closure = closure;
+#if !CYTHON_COMPILING_IN_LIMITED_API
     Py_XINCREF(module);
     cf->m_module = module;
+#endif
     op->func_dict = NULL;
     op->func_name = NULL;
     Py_INCREF(qualname);
     op->func_qualname = qualname;
     op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
+#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
     op->func_classobj = NULL;
 #else
     ((PyCMethodObject*)op)->mm_class = NULL;
@@ -8060,13 +8434,18 @@ static int
 __Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
 {
     Py_CLEAR(m->func_closure);
+#if CYTHON_COMPILING_IN_LIMITED_API
+    Py_CLEAR(m->func);
+#else
     Py_CLEAR(((PyCFunctionObject*)m)->m_module);
+#endif
     Py_CLEAR(m->func_dict);
     Py_CLEAR(m->func_name);
     Py_CLEAR(m->func_qualname);
     Py_CLEAR(m->func_doc);
     Py_CLEAR(m->func_globals);
     Py_CLEAR(m->func_code);
+#if !CYTHON_COMPILING_IN_LIMITED_API
 #if PY_VERSION_HEX < 0x030900B1
     Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
 #else
@@ -8075,6 +8454,7 @@ __Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
         ((PyCMethodObject *) (m))->mm_class = NULL;
         Py_XDECREF(cls);
     }
+#endif
 #endif
     Py_CLEAR(m->defaults_tuple);
     Py_CLEAR(m->defaults_kwdict);
@@ -8105,14 +8485,20 @@ static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
 static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
 {
     Py_VISIT(m->func_closure);
+#if CYTHON_COMPILING_IN_LIMITED_API
+    Py_VISIT(m->func);
+#else
     Py_VISIT(((PyCFunctionObject*)m)->m_module);
+#endif
     Py_VISIT(m->func_dict);
     Py_VISIT(m->func_name);
     Py_VISIT(m->func_qualname);
     Py_VISIT(m->func_doc);
     Py_VISIT(m->func_globals);
     Py_VISIT(m->func_code);
+#if !CYTHON_COMPILING_IN_LIMITED_API
     Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
+#endif
     Py_VISIT(m->defaults_tuple);
     Py_VISIT(m->defaults_kwdict);
     Py_VISIT(m->func_is_coroutine);
@@ -8136,10 +8522,22 @@ __Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
 #endif
 }
 static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
+#if CYTHON_COMPILING_IN_LIMITED_API
+    PyObject *f = ((__pyx_CyFunctionObject*)func)->func;
+    PyObject *py_name = NULL;
+    PyCFunction meth;
+    int flags;
+    meth = PyCFunction_GetFunction(f);
+    if (unlikely(!meth)) return NULL;
+    flags = PyCFunction_GetFlags(f);
+    if (unlikely(flags < 0)) return NULL;
+#else
     PyCFunctionObject* f = (PyCFunctionObject*)func;
     PyCFunction meth = f->m_ml->ml_meth;
+    int flags = f->m_ml->ml_flags;
+#endif
     Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
+    switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
     case METH_VARARGS:
         if (likely(kw == NULL || PyDict_Size(kw) == 0))
             return (*meth)(self, arg);
@@ -8148,24 +8546,43 @@ static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, Py
         return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
     case METH_NOARGS:
         if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+#if CYTHON_ASSUME_SAFE_MACROS
             size = PyTuple_GET_SIZE(arg);
+#else
+            size = PyTuple_Size(arg);
+            if (unlikely(size < 0)) return NULL;
+#endif
             if (likely(size == 0))
                 return (*meth)(self, NULL);
+#if CYTHON_COMPILING_IN_LIMITED_API
+            py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
+            if (!py_name) return NULL;
+            PyErr_Format(PyExc_TypeError,
+                "%.200S() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                py_name, size);
+            Py_DECREF(py_name);
+#else
             PyErr_Format(PyExc_TypeError,
                 "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
                 f->m_ml->ml_name, size);
+#endif
             return NULL;
         }
         break;
     case METH_O:
         if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+#if CYTHON_ASSUME_SAFE_MACROS
             size = PyTuple_GET_SIZE(arg);
+#else
+            size = PyTuple_Size(arg);
+            if (unlikely(size < 0)) return NULL;
+#endif
             if (likely(size == 1)) {
                 PyObject *result, *arg0;
                 #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
                 arg0 = PyTuple_GET_ITEM(arg, 0);
                 #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
+                arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
                 #endif
                 result = (*meth)(self, arg0);
                 #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
@@ -8173,9 +8590,18 @@ static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, Py
                 #endif
                 return result;
             }
+#if CYTHON_COMPILING_IN_LIMITED_API
+            py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
+            if (!py_name) return NULL;
+            PyErr_Format(PyExc_TypeError,
+                "%.200S() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                py_name, size);
+            Py_DECREF(py_name);
+#else
             PyErr_Format(PyExc_TypeError,
                 "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
                 f->m_ml->ml_name, size);
+#endif
             return NULL;
         }
         break;
@@ -8183,12 +8609,28 @@ static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, Py
         PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
         return NULL;
     }
+#if CYTHON_COMPILING_IN_LIMITED_API
+    py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL);
+    if (!py_name) return NULL;
+    PyErr_Format(PyExc_TypeError, "%.200S() takes no keyword arguments",
+                 py_name);
+    Py_DECREF(py_name);
+#else
     PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
                  f->m_ml->ml_name);
+#endif
     return NULL;
 }
 static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
+    PyObject *self, *result;
+#if CYTHON_COMPILING_IN_LIMITED_API
+    self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func);
+    if (unlikely(!self) && PyErr_Occurred()) return NULL;
+#else
+    self = ((PyCFunctionObject*)func)->m_self;
+#endif
+    result = __Pyx_CyFunction_CallMethod(func, self, arg, kw);
+    return result;
 }
 static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
     PyObject *result;
@@ -8208,7 +8650,12 @@ static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, P
         Py_ssize_t argc;
         PyObject *new_args;
         PyObject *self;
+#if CYTHON_ASSUME_SAFE_MACROS
         argc = PyTuple_GET_SIZE(args);
+#else
+        argc = PyTuple_Size(args);
+        if (unlikely(!argc) < 0) return NULL;
+#endif
         new_args = PyTuple_GetSlice(args, 1, argc);
         if (unlikely(!new_args))
             return NULL;
@@ -8333,7 +8780,7 @@ static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func,
     default:
         return NULL;
     }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
+    return ((__Pyx_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
 }
 static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
 {
@@ -8421,7 +8868,7 @@ static PyTypeObject __pyx_CyFunctionType_type = {
 #ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
     Py_TPFLAGS_METHOD_DESCRIPTOR |
 #endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
+#if defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL
     _Py_TPFLAGS_HAVE_VECTORCALL |
 #endif
     Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
@@ -8679,20 +9126,93 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
   #include "compile.h"
 #include "frameobject.h"
 #include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
+#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API
   #ifndef Py_BUILD_CORE
     #define Py_BUILD_CORE 1
   #endif
   #include "internal/pycore_frame.h"
 #endif
 #if CYTHON_COMPILING_IN_LIMITED_API
+static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict,
+                                                       PyObject *firstlineno, PyObject *name) {
+    PyObject *replace = NULL;
+    if (unlikely(PyDict_SetItemString(scratch_dict, "co_firstlineno", firstlineno))) return NULL;
+    if (unlikely(PyDict_SetItemString(scratch_dict, "co_name", name))) return NULL;
+    replace = PyObject_GetAttrString(code, "replace");
+    if (likely(replace)) {
+        PyObject *result;
+        result = PyObject_Call(replace, __pyx_empty_tuple, scratch_dict);
+        Py_DECREF(replace);
+        return result;
+    }
+    PyErr_Clear();
+    #if __PYX_LIMITED_VERSION_HEX < 0x030780000
+    {
+        PyObject *compiled = NULL, *result = NULL;
+        if (unlikely(PyDict_SetItemString(scratch_dict, "code", code))) return NULL;
+        if (unlikely(PyDict_SetItemString(scratch_dict, "type", (PyObject*)(&PyType_Type)))) return NULL;
+        compiled = Py_CompileString(
+            "out = type(code)(\n"
+            "  code.co_argcount, code.co_kwonlyargcount, code.co_nlocals, code.co_stacksize,\n"
+            "  code.co_flags, code.co_code, code.co_consts, code.co_names,\n"
+            "  code.co_varnames, code.co_filename, co_name, co_firstlineno,\n"
+            "  code.co_lnotab)\n", "<dummy>", Py_file_input);
+        if (!compiled) return NULL;
+        result = PyEval_EvalCode(compiled, scratch_dict, scratch_dict);
+        Py_DECREF(compiled);
+        if (!result) PyErr_Print();
+        Py_DECREF(result);
+        result = PyDict_GetItemString(scratch_dict, "out");
+        if (result) Py_INCREF(result);
+        return result;
+    }
+    #else
+    return NULL;
+    #endif
+}
 static void __Pyx_AddTraceback(const char *funcname, int c_line,
                                int py_line, const char *filename) {
+    PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL;
+    PyObject *replace = NULL, *getframe = NULL, *frame = NULL;
+    PyObject *exc_type, *exc_value, *exc_traceback;
+    int success = 0;
     if (c_line) {
         (void) __pyx_cfilenm;
         (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
     }
-    _PyTraceback_Add(funcname, filename, py_line);
+    PyErr_Fetch(&exc_type, &exc_value, &exc_traceback);
+    code_object = Py_CompileString("_getframe()", filename, Py_eval_input);
+    if (unlikely(!code_object)) goto bad;
+    py_py_line = PyLong_FromLong(py_line);
+    if (unlikely(!py_py_line)) goto bad;
+    py_funcname = PyUnicode_FromString(funcname);
+    if (unlikely(!py_funcname)) goto bad;
+    dict = PyDict_New();
+    if (unlikely(!dict)) goto bad;
+    {
+        PyObject *old_code_object = code_object;
+        code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname);
+        Py_DECREF(old_code_object);
+    }
+    if (unlikely(!code_object)) goto bad;
+    getframe = PySys_GetObject("_getframe");
+    if (unlikely(!getframe)) goto bad;
+    if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad;
+    frame = PyEval_EvalCode(code_object, dict, dict);
+    if (unlikely(!frame) || frame == Py_None) goto bad;
+    success = 1;
+  bad:
+    PyErr_Restore(exc_type, exc_value, exc_traceback);
+    Py_XDECREF(code_object);
+    Py_XDECREF(py_py_line);
+    Py_XDECREF(py_funcname);
+    Py_XDECREF(dict);
+    Py_XDECREF(replace);
+    if (success) {
+        PyTraceBack_Here(
+            (struct _frame*)frame);
+    }
+    Py_XDECREF(frame);
 }
 #else
 static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
@@ -8745,7 +9265,7 @@ static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
     #else
     py_code = PyCode_NewEmpty(filename, funcname, py_line);
     #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
+    Py_XDECREF(py_funcname);
     return py_code;
 bad:
     Py_XDECREF(py_funcname);
@@ -9182,8 +9702,34 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) {
     {
         int one = 1; int little = (int)*(unsigned char *)&one;
         unsigned char *bytes = (unsigned char *)&value;
+#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000
         return _PyLong_FromByteArray(bytes, sizeof(int),
                                      little, !is_unsigned);
+#else
+        PyObject *from_bytes, *result = NULL;
+        PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL;
+        from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes");
+        if (!from_bytes) return NULL;
+        py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int));
+        if (!py_bytes) goto limited_bad;
+        order_str = PyUnicode_FromString(little ? "little" : "big");
+        if (!order_str) goto limited_bad;
+        arg_tuple = PyTuple_Pack(2, py_bytes, order_str);
+        if (!arg_tuple) goto limited_bad;
+        if (!is_unsigned) {
+            kwds = PyDict_New();
+            if (!kwds) goto limited_bad;
+            if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad;
+        }
+        result = PyObject_Call(from_bytes, arg_tuple, kwds);
+        limited_bad:
+        Py_XDECREF(kwds);
+        Py_XDECREF(arg_tuple);
+        Py_XDECREF(order_str);
+        Py_XDECREF(py_bytes);
+        Py_XDECREF(from_bytes);
+        return result;
+#endif
     }
 }
 
@@ -9357,7 +9903,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) {
 #endif
             if (likely(v)) {
                 int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
+#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
                 int one = 1; int is_little = (int)*(unsigned char *)&one;
                 unsigned char *bytes = (unsigned char *)&val;
                 ret = _PyLong_AsByteArray((PyLongObject *)v,
@@ -9469,7 +10015,8 @@ __Pyx_PyType_GetName(PyTypeObject* tp)
                                                __pyx_n_s_name);
     if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
         PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__8));
+        Py_XDECREF(name);
+        name = __Pyx_NewRef(__pyx_n_s__8);
     }
     return name;
 }
@@ -9508,8 +10055,34 @@ __Pyx_PyType_GetName(PyTypeObject* tp)
     {
         int one = 1; int little = (int)*(unsigned char *)&one;
         unsigned char *bytes = (unsigned char *)&value;
+#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000
         return _PyLong_FromByteArray(bytes, sizeof(long),
                                      little, !is_unsigned);
+#else
+        PyObject *from_bytes, *result = NULL;
+        PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL;
+        from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes");
+        if (!from_bytes) return NULL;
+        py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long));
+        if (!py_bytes) goto limited_bad;
+        order_str = PyUnicode_FromString(little ? "little" : "big");
+        if (!order_str) goto limited_bad;
+        arg_tuple = PyTuple_Pack(2, py_bytes, order_str);
+        if (!arg_tuple) goto limited_bad;
+        if (!is_unsigned) {
+            kwds = PyDict_New();
+            if (!kwds) goto limited_bad;
+            if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad;
+        }
+        result = PyObject_Call(from_bytes, arg_tuple, kwds);
+        limited_bad:
+        Py_XDECREF(kwds);
+        Py_XDECREF(arg_tuple);
+        Py_XDECREF(order_str);
+        Py_XDECREF(py_bytes);
+        Py_XDECREF(from_bytes);
+        return result;
+#endif
     }
 }
 
@@ -9683,7 +10256,7 @@ __Pyx_PyType_GetName(PyTypeObject* tp)
 #endif
             if (likely(v)) {
                 int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
+#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
                 int one = 1; int is_little = (int)*(unsigned char *)&one;
                 unsigned char *bytes = (unsigned char *)&val;
                 ret = _PyLong_AsByteArray((PyLongObject *)v,
@@ -9903,41 +10476,50 @@ static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObj
 #endif
 
 /* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
+  static unsigned long __Pyx_get_runtime_version(void) {
+#if __PYX_LIMITED_VERSION_HEX >= 0x030B00A4
+    return Py_Version & ~0xFFUL;
+#else
+    const char* rt_version = Py_GetVersion();
+    unsigned long version = 0;
+    unsigned long factor = 0x01000000UL;
+    unsigned int digit = 0;
+    int i = 0;
+    while (factor) {
+        while ('0' <= rt_version[i] && rt_version[i] <= '9') {
+            digit = digit * 10 + (unsigned int) (rt_version[i] - '0');
+            ++i;
         }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
+        version += factor * digit;
+        if (rt_version[i] != '.')
             break;
-        }
+        digit = 0;
+        factor >>= 8;
+        ++i;
     }
-    if (!same) {
-        char rtversion[5] = {'\0'};
+    return version;
+#endif
+}
+static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) {
+    const unsigned long MAJOR_MINOR = 0xFFFF0000UL;
+    if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR))
+        return 0;
+    if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR)))
+        return 1;
+    {
         char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
         PyOS_snprintf(message, sizeof(message),
-                      "compile time version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
+                      "compile time Python version %d.%d "
+                      "of module '%.100s' "
+                      "%s "
+                      "runtime version %d.%d",
+                       (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF),
+                       __Pyx_MODULE_NAME,
+                       (allow_newer) ? "was newer than" : "does not match",
+                       (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF)
+       );
         return PyErr_WarnEx(NULL, message, 1);
     }
-    return 0;
 }
 
 /* InitStrings */
@@ -9983,8 +10565,24 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
     return 0;
 }
 
+#include <string.h>
+static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) {
+    size_t len = strlen(s);
+    if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) {
+        PyErr_SetString(PyExc_OverflowError, "byte string is too long");
+        return -1;
+    }
+    return (Py_ssize_t) len;
+}
 static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+    Py_ssize_t len = __Pyx_ssize_strlen(c_str);
+    if (unlikely(len < 0)) return NULL;
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, len);
+}
+static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) {
+    Py_ssize_t len = __Pyx_ssize_strlen(c_str);
+    if (unlikely(len < 0)) return NULL;
+    return PyByteArray_FromStringAndSize(c_str, len);
 }
 static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
     Py_ssize_t ignore;
diff --git a/rainforest/common/graphics.py b/rainforest/common/graphics.py
index e3c6de2..bedc0fa 100644
--- a/rainforest/common/graphics.py
+++ b/rainforest/common/graphics.py
@@ -169,7 +169,7 @@ def qpe_plot(data, subplots = None, figsize = None,
               np.min(x),np.max(x)]
 
     for i,dd in enumerate(data):
-        m = ax[i].imshow(dd,vmin = vmin, vmax = vmax, extent = extent,
+        m = ax[i].imshow(dd, extent = extent,
               cmap = cmap_qpe,  norm = norm, **kwargs)
         
         if ch_border:
diff --git a/rainforest/common/radarprocessing.py b/rainforest/common/radarprocessing.py
index 18f32c0..7391b4b 100644
--- a/rainforest/common/radarprocessing.py
+++ b/rainforest/common/radarprocessing.py
@@ -36,7 +36,7 @@ class Radar(object):
     single pyart radar instance as this was found to be faster in practice
     '''
     def __init__(self, radname, polfiles, statusfile=None, vprfile=None,
-                temp_ref='TAIR', metranet_reader = 'python'):
+                temp_ref='TAIR', metranet_reader = 'C'):
         """
         Creates an Radar class instance
         
diff --git a/rainforest/common/retrieve_data.py b/rainforest/common/retrieve_data.py
index d4b9632..6b6121a 100644
--- a/rainforest/common/retrieve_data.py
+++ b/rainforest/common/retrieve_data.py
@@ -124,7 +124,9 @@ def retrieve_hzt_RT(tstep):
         # Sort filelist to most recent prediction
         content_filt = np.array([c for c in content_zip if c.endswith('800')])
         times_filt = np.array([datetime.datetime.strptime(c[3:12],
-                            '%y%j%H%M')+datetime.timedelta(hours=int(c[-2::])) for c in content_filt])
+                                '%y%j%H%M').replace(tzinfo=datetime.timezone.utc)+\
+                                datetime.timedelta(hours=int(c[-2::])) \
+                                for c in content_filt])
         conditions = np.array([np.logical_and((t >= start_time), (t <= end_time)) for t in times_filt])
         
         content_filt = content_filt[conditions]
@@ -139,7 +141,8 @@ def retrieve_hzt_RT(tstep):
     
     if len(all_hours) != len(times_filt):
         content_times = np.array([datetime.datetime.strptime(c[3:12],
-                '%y%j%H%M')+datetime.timedelta(hours=int(c[-2::])) for c in content_zip])
+                    '%y%j%H%M').replace(tzinfo=datetime.timezone.utc)+\
+                    datetime.timedelta(hours=int(c[-2::])) for c in content_zip])
         # Find time that is missing:
         for hh in all_hours:
             if not hh in times_filt:
@@ -571,7 +574,8 @@ def retrieve_prod_RT(time, product_name,
 
     # Derive datetime of each file
     times_zip = np.array([datetime.datetime.strptime(c[3:12],
-                '%y%j%H%M') for c in content_zip])
+                '%y%j%H%M').replace(tzinfo=datetime.timezone.utc)
+                    for c in content_zip])
 
     # Get a list of all files to retrieve
     conditions = (times_zip == time)
diff --git a/rainforest/common/utils.py b/rainforest/common/utils.py
index 0f14cf5..6004266 100644
--- a/rainforest/common/utils.py
+++ b/rainforest/common/utils.py
@@ -12,6 +12,7 @@
 # Global imports
 import datetime
 import io
+import importlib.metadata as _importlib_metadata
 import os
 from collections import OrderedDict
 import numpy as np
@@ -19,14 +20,27 @@
 from dateutil import parser
 import glob
 import yaml
+import dask
+dask.config.set({'dataframe.query-planning': False})
 import  dask.dataframe as dd
 import re
 import numbers
+
 # Local imports
 from .logger import logger
 from .wgs84_ch1903 import GPSConverter
 from . import constants
 
+def get_version():
+    # Get the version
+    try:
+        version = _importlib_metadata.version("rainforest_mch")
+    except _importlib_metadata.PackageNotFoundError:
+        # package is not installed
+        version = "0.0.0"
+    return version
+
+
 def hex_to_rgb(value):
     value = value.lstrip('#')
     lv = len(value)
diff --git a/rainforest/database/retrieve_radar_data.py b/rainforest/database/retrieve_radar_data.py
index 0334307..fdc443e 100644
--- a/rainforest/database/retrieve_radar_data.py
+++ b/rainforest/database/retrieve_radar_data.py
@@ -14,6 +14,8 @@
 
 import numpy as np
 import pandas as pd
+import dask
+dask.config.set({'dataframe.query-planning': False})
 import dask.dataframe as dd
 import datetime
 import logging
diff --git a/rainforest/database/retrieve_reference_data.py b/rainforest/database/retrieve_reference_data.py
index 988ba5b..7f5ac80 100644
--- a/rainforest/database/retrieve_reference_data.py
+++ b/rainforest/database/retrieve_reference_data.py
@@ -18,6 +18,8 @@
 import pandas as pd
 import datetime
 import logging
+import pyart
+dask.config.set({'dataframe.query-planning': False})
 import dask.dataframe as dd
 
 logging.basicConfig(level=logging.INFO)
diff --git a/rainforest/ml/rf.py b/rainforest/ml/rf.py
index 2f07f66..2047e66 100755
--- a/rainforest/ml/rf.py
+++ b/rainforest/ml/rf.py
@@ -5,6 +5,9 @@
 """
 
 # Global imports
+from audioop import cross
+import logging
+logging.getLogger().setLevel(logging.INFO)
 import os
 import pickle
 import glob
@@ -17,168 +20,14 @@
 
 # Local imports
 from ..common import constants
-from ..ml.utils import vert_aggregation, split_event, split_years
-from ..ml.rfdefinitions import RandomForestRegressorBC, QuantileRandomForestRegressorBC, read_rf
+from .utils import vert_aggregation, split_event, split_years
+from .rfdefinitions import RandomForestRegressorBC
 from ..common.utils import perfscores, envyaml
 from ..common.graphics import plot_crossval_stats
-from ..common.logger import logger
-
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
 FOLDER_MODELS = Path(os.environ['RAINFOREST_DATAPATH'], 'rf_models')
 
-def readInputData(input_location, tstart, tend, datalist=['gauge', 'radar', 'refer']):
-    
-    if 'gauge' in datalist:
-        gaugetab = pd.read_parquet(str(Path(input_location, 'gauge.parquet')))
-    if 'radar' in datalist:
-        radartab = pd.read_parquet(str(Path(input_location, 'radar_x0y0.parquet')))
-    if 'refer' in datalist:
-        refertab = pd.read_parquet(str(Path(input_location, 'reference_x0y0.parquet')))
-    
-    grp = pickle.load(open(str(Path(input_location, 'grouping_idx_x0y0.p')),'rb'))
-    grp_hourly = grp['grp_hourly']; grp_vertical = grp['grp_vertical']
-
-    if tstart != None:
-        try:
-            tstart = datetime.datetime.strptime(tstart,
-                    '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
-        except:
-            tstart = gaugetab['TIMESTAMP'].min()
-            logger.info('The format of tstart was wrong, taking the earliest date')
-    if tend != None:
-        try:
-            tend = datetime.datetime.strptime(tend,
-                    '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
-        except:
-            tend = gaugetab['TIMESTAMP'].max()
-            logger.info('The format of tend was wrong, taking the earliest date')
-
-    timevalid = gaugetab['TIMESTAMP'].copy().astype(bool)
-    vertvalid = radartab['TIMESTAMP'].copy().astype(bool)
-
-    if (tstart != None):
-        timevalid[(gaugetab['TIMESTAMP'] < tstart)] = False
-        vertvalid[(radartab['TIMESTAMP'] < tstart)] = False
-    if (tend != None):
-        timevalid[(gaugetab['TIMESTAMP'] > tend)] = False
-        vertvalid[(radartab['TIMESTAMP'] > tend)] = False
-
-    gaugetab = gaugetab[timevalid]
-    grp_hourly = grp_hourly[timevalid]
-    radartab = radartab[vertvalid]
-    grp_vertical = grp_vertical[vertvalid]    
-
-    if 'refer' in datalist:
-        refertab = refertab[timevalid]
-        return gaugetab, radartab, refertab, grp_hourly, grp_vertical
-    else:
-        return gaugetab, radartab, grp_hourly, grp_vertical
-
-def processFeatures(features_dic, radartab):
-    # currently the only supported additional features is zh (refl in linear units)
-    # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars)
-    # Get list of unique features names
-    if type(features_dic) == dict :
-        features = np.unique([item for sub in list(features_dic.values())
-                            for item in sub])
-    else:
-        features = features_dic
-        
-    for f in features:
-        if 'zh' in f:
-            logger.info('Converting reflectivity {:s} from log [dBZ] to linear [mm^6 m^-3]'.format(f))
-            try:
-                radartab[str(f)] = 10**(0.1 * radartab[f.replace('zh','ZH')+'_mean'].copy())
-            except:
-                radartab[str(f)] = 10**(0.1 * radartab[f.replace('zh','ZH')].copy())                        
-        elif 'zv' in f:
-            logger.info('Computing derived variable {:s}'.format(f))
-            try:
-                radartab[str(f)] = 10**(0.1 * radartab[f.replace('zv','ZV')+'_mean'].copy())
-            except:
-                radartab[str(f)] = 10**(0.1 * radartab[f.replace('zv','ZV')].copy())      
-        if 'DIST_TO_RAD' in f:
-            info_radar = constants.RADARS
-            vals = np.unique(radartab['RADAR'])
-            for val in vals:
-                dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+
-                        (radartab['Y'] - info_radar['Y'][val])**2) / 1000.
-                radartab['DIST_TO_RAD' + str(val)] = dist
-
-    features = [str(f) for f in features]
-
-    return radartab, features
-
-def getTempIDX(TempOBS, grp_hourly, test, train) : 
-
-    # Get reference values
-    T_test_60 = np.squeeze(np.array(pd.DataFrame(TempOBS[test])
-                    .groupby(grp_hourly[test]).mean()))
-    
-    T_train_60 = np.squeeze(np.array(pd.DataFrame(TempOBS[train])
-                    .groupby(grp_hourly[train]).mean()))
-    
-    # Dictionnary with all indices:
-    IDX = {}
-
-    for tagg in ['10min', '60min']:
-        IDX[tagg] = {}
-        for data_type in ['test', 'train']:
-            IDX[tagg][data_type] = {}
-            for precip_type in ['all', 'liquid', 'solid']:
-                IDX[tagg][data_type][precip_type] = {}
-
-    IDX['10min']['train']['liquid'] = TempOBS[train] >= constants.THRESHOLD_SOLID
-    IDX['10min']['train']['solid'] = TempOBS[train] < constants.THRESHOLD_SOLID
-
-    IDX['60min']['train']['liquid'] = T_train_60 >= constants.THRESHOLD_SOLID
-    IDX['60min']['train']['solid'] = T_train_60 < constants.THRESHOLD_SOLID
-
-    IDX['10min']['test']['liquid'] = TempOBS[test] >= constants.THRESHOLD_SOLID
-    IDX['10min']['test']['solid'] = TempOBS[test] < constants.THRESHOLD_SOLID
-
-    IDX['60min']['test']['liquid'] = T_test_60 >= constants.THRESHOLD_SOLID
-    IDX['60min']['test']['solid'] = T_test_60 < constants.THRESHOLD_SOLID
-
-    return IDX
-
-
-def prepareScoreDic(modelnames, station_scores=True):
-
-    all_scores = {'10min':{},'60min':{}}
-    all_stats = {'10min':{},'60min':{}}
-    
-    if station_scores == True:
-        all_station_scores = {'10min': {}, '60min': {}}
-        all_station_stats = {'10min': {}, '60min': {}}
-
-    for tagg in ['10min', '60min']:
-        for model in modelnames:
-            all_scores[tagg][model] = {'train': {'solid':[],'liquid':[],'all':[]},
-                                    'test': {'solid':[],'liquid':[],'all':[]}}
-            all_stats[tagg][model] = {'train': {'solid':{},'liquid':{},'all':{}},
-                                    'test': {'solid':{},'liquid':{},'all':{}}}
-            if station_scores == True:
-                all_station_scores[tagg][model] = {'solid':{},'liquid':{},'all':{}}
-                all_station_stats[tagg][model] = {'solid':{},'liquid':{},'all':{}}
-
-    if station_scores == False:
-        return all_scores, all_stats
-    else:
-        return all_scores, all_stats, all_station_scores, all_station_stats
-
-def calcScore(all_scores, obs, pred, idxTemp, bounds, 
-                tagg = '10min', model='RFO', data_type='test'):
-
-    for pp_type in all_scores[tagg][model][data_type].keys():
-        idx = idxTemp[tagg][data_type][pp_type]
-        scores = perfscores(pred[idx], obs[idx], bounds=bounds)
-        all_scores[tagg][model][data_type][pp_type].append(scores)
-
-    return all_scores
-
-
 class RFTraining(object):
     '''
     This is the main class that allows to preparate data for random forest
@@ -227,9 +76,9 @@ def __init__(self, db_location, input_location=None,
         self.db_location = db_location
         
         if not valid :
-            logger.info('Could not find valid input data from the folder {:s}'.format(input_location))
+            logging.info('Could not find valid input data from the folder {:s}'.format(input_location))
         # if force_regenerate_input or not valid:
-        #     logger.info('The program will now compute this input data from the database, this takes quite some time')
+        #     logging.info('The program will now compute this input data from the database, this takes quite some time')
         #     self.prepare_input()
     
     def prepare_input(self, only_center=True, foldername_radar='radar'):
@@ -265,7 +114,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'):
         """
         
         if not os.path.exists(Path(self.db_location, foldername_radar)):
-            logger.error('Invalid foldername for radar data, please check')
+            logging.error('Invalid foldername for radar data, please check')
 
         if only_center:
             nx = [0]
@@ -282,7 +131,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'):
         gauge = gauge.replace(-9999,np.nan)
         for x in nx:
             for y in ny:
-                logger.info('Processing neighbour {:d}{:d}'.format(x, y))
+                logging.info('Processing neighbour {:d}{:d}'.format(x, y))
                 radar = dd.read_parquet(str(Path(self.db_location, foldername_radar,
                                                   '*.parquet')))
                 refer = dd.read_parquet(str(Path(self.db_location, 'reference', 
@@ -389,7 +238,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'):
                 
                 # Save all to file
                 # Save all to file
-                logger.info('Saving files to {}'.format(self.input_location))
+                logging.info('Saving files to {}'.format(self.input_location))
                 refer.to_parquet(str(Path(self.input_location, 
                                           'reference_x{:d}y{:d}.parquet'.format(x,y))),
                                  compression = 'gzip', index = False)
@@ -412,102 +261,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'):
                     gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')),
                                  compression = 'gzip', index = False)
         
-    def prepare_input_vert_agg(self, aggregation_params, features=None, 
-                        tstart = None, tend = None,
-                        output_folder = None):
-        """
-            Calculates the vertical aggregation of the input features dic and saves
-            it to a file to run tests faster
-
-        Parameters
-        ----------
-        aggregation_params : dict
-            Dict with two keywords: beta and visib_weighting 
-            If not given, the default is used: {'beta' : -0.5 , 'visib_weighting: True}
-        features : list
-            A list with features that will be aggregated to the ground,
-            e.g., ['RADAR', 'zh_VISIB_mean',
-            'zv_VISIB_mean','KDP_mean','RHOHV_mean','T', 'HEIGHT','VISIB_mean']}
-        tstart : str
-            the starting time of the training time interval, default is to start
-            at the beginning of the time interval covered by the database
-        tend : str
-            the end time of the training time interval, default is to end
-            at the end of the time interval covered by the database   
-        output_folder : str
-            Location where to store the trained models in pickle format,
-            if not provided it will store them in the standard location 
-            str(Path(db_location, 'rf_input_data'))
-        """
-        
-        if output_folder == None:
-            output_folder =  self.input_location
             
-        if aggregation_params == None:
-            vert_agg_params = {'beta' : -0.5, 'visib_weighting' : 1}
-        else:
-            vert_agg_params = aggregation_params
-  
-        ###############################################################################
-        # Read and filter data
-        ###############################################################################
-        _, radartab, _, grp_vertical = \
-            readInputData(self.input_location, tstart, tend, 
-                        datalist=['gauge', 'radar'])
-
-        if features == None:
-            features = radartab.columns
-
-        ###############################################################################
-        # Compute vertical aggregation and initialize model
-        ###############################################################################
-        radartab, features = processFeatures(features, radartab)
-                    
-        ###############################################################################
-        # Compute data filter for each model
-        ###############################################################################
-        beta = vert_agg_params['beta']
-        visib_weigh = vert_agg_params['visib_weighting']
-
-        vweights = 10**(beta * (radartab['HEIGHT']/1000.)) # vert. weights
-
-        ###############################################################################
-        # Prepare training dataset
-        ###############################################################################        
-        logger.info('Performing vertical aggregation of input features')                
-        features_VERT_AGG = vert_aggregation(radartab[features], 
-                                vweights, grp_vertical,visib_weigh,
-                                radartab['VISIB_mean'])
-                    
-        ###############################################################################
-        # Fit
-        ###############################################################################
-        # create name of variables used in the model
-        features = []
-        for f in features_VERT_AGG.columns:
-            if '_max' in f:
-                f = f.replace('_max','')
-            elif '_min' in f:
-                f = f.replace('_min','')
-            elif '_mean' in f:
-                f = f.replace('_mean','')
-            features.append(f)
-
-        features_VERT_AGG['TIMESTAMP'] = radartab['TIMESTAMP'].groupby(grp_vertical).first()
-        features_VERT_AGG['STATION'] = radartab['STATION'].groupby(grp_vertical).first()
-
-        features_VERT_AGG.to_parquet(str(Path(self.input_location, 
-                'feat_vert_agg_BETA_{:1.1f}_VisibWeigh_{:d}.parquet'.format(beta,visib_weigh))),
-                compression = 'gzip', index = False)
-
-        filename = str(Path(self.input_location, \
-                    'feat_vert_agg_BETA_{:1.1f}_VisibWeigh_{:d}_README.txt'.\
-                    format(beta,visib_weigh)))
-        with open(filename, 'w') as f:
-                f.write('Colnames\n'+ (';').join(list(features_VERT_AGG.columns)) + '\n')
-                f.write('Names in model\n' + (';').join(features) + '\n')
-
-
     def fit_models(self, config_file, features_dic, tstart = None, tend = None,
                    output_folder = None):
         """
@@ -526,10 +280,10 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None,
             'zv_VISIB_mean','KDP_mean','RHOHV_mean','T', 'HEIGHT','VISIB_mean']}
             will train a model with all these features that will then be stored
             under the name RF_dualpol_BC_<type of BC>.p in the ml/rf_models dir
-        tstart : str
+        tstart : datetime
             the starting time of the training time interval, default is to start
             at the beginning of the time interval covered by the database
-        tend : str
+        tend : datetime
             the end time of the training time interval, default is to end
             at the end of the time interval covered by the database   
         output_folder : str
@@ -544,40 +298,62 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None,
         try:
             config = envyaml(config_file)
         except:
-            logger.warning('Using default config as no valid config file was provided')
+            logging.warning('Using default config as no valid config file was provided')
             config_file = dir_path + '/default_config.yml'
             
         config = envyaml(config_file)
   
+        #######################################################################
+        # Read data
+        #######################################################################
+        
+        logging.info('Loading input data')
+        radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet')))
+        gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet')))
+        grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb'))
+        grp_vertical = grp['grp_vertical']
+        
         ###############################################################################
-        # Read and filter data
+        # Compute additional data if needed
         ###############################################################################
-        gaugetab, radartab, _, grp_vertical = \
-            readInputData(self.input_location, tstart, tend, 
-                        datalist=['gauge', 'radar'])
+    
+        # currently the only supported additional features is zh (refl in linear units)
+        # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars)
+        # Get list of unique features names
+        features = np.unique([item for sub in list(features_dic.values())
+                            for item in sub])
 
-        ###############################################################################
-        # Compute vertical aggregation and initialize model
-        ###############################################################################
-        radartab, features = processFeatures(features_dic, radartab)
+        for f in features:
+            if 'zh' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')])
+            elif 'zv' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')])        
+            if 'DIST_TO_RAD' in f:
+                info_radar = constants.RADARS
+                vals = np.unique(radartab['RADAR'])
+                for val in vals:
+                    dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+
+                           (radartab['Y'] - info_radar['Y'][val])**2) / 1000.
+                    radartab['DIST_TO_RAD' + str(val)] = dist
                     
         ###############################################################################
         # Compute data filter for each model
         ###############################################################################
 
         for model in features_dic.keys():
-            logtime0 = datetime.datetime.now()
 
             vweights = 10**(config[model]['VERT_AGG']['BETA'] * (radartab['HEIGHT']/1000.)) # vert. weights
 
             filterconf = config[model]['FILTERING'].copy()
-            logger.info('Computing data filter')
-            logger.info('List of stations to ignore {:s}'.format(','.join(filterconf['STA_TO_REMOVE'])))
-            logger.info('Start time {:s}'.format(str(tstart)))
-            logger.info('End time {:s}'.format(str(tend)))           
-            logger.info('ZH must be > {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MIN_ZH'][1],
+            logging.info('Computing data filter')
+            logging.info('List of stations to ignore {:s}'.format(','.join(filterconf['STA_TO_REMOVE'])))
+            logging.info('Start time {:s}'.format(str(tstart)))
+            logging.info('End time {:s}'.format(str(tend)))           
+            logging.info('ZH must be > {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MIN_ZH'][1],
                                                 filterconf['CONSTRAINT_MIN_ZH'][0]))   
-            logger.info('ZH must be < {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MAX_ZH'][1],
+            logging.info('ZH must be < {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MAX_ZH'][1],
                                                 filterconf['CONSTRAINT_MAX_ZH'][0]))    
 
             ZH_agg = vert_aggregation(pd.DataFrame(radartab['ZH_mean']),
@@ -595,6 +371,12 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None,
             invalid = np.logical_or(invalid,cond3)
             invalid = np.array(invalid)
 
+            if tend != None:
+                tend_unix = (tend - datetime.datetime(1970,1,1) ).total_seconds()
+                invalid[gaugetab['TIMESTAMP'] > tend_unix] = 1
+            if tstart != None:
+                tstart_unix = (tstart - datetime.datetime(1970,1,1) ).total_seconds()
+                invalid[gaugetab['TIMESTAMP'] < tstart_unix] = 1
             invalid[np.isnan(gaugetab['RRE150Z0'])] = 1
 
             ###############################################################################
@@ -603,7 +385,7 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None,
         
             gaugetab_train = gaugetab[~invalid].copy()
         
-            logger.info('Performing vertical aggregation of input features for model {:s}'.format(model))                
+            logging.info('Performing vertical aggregation of input features for model {:s}'.format(model))                
             features_VERT_AGG = vert_aggregation(radartab[features_dic[model]], 
                                  vweights, grp_vertical,
                                  config[model]['VERT_AGG']['VISIB_WEIGHTING'],
@@ -639,287 +421,26 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None,
             config[model]['FILTERING']['STA_INCLUDED'] = gaugetab['STATION'][~invalid].unique()
             config[model]['FILTERING']['CREATED'] = datetime.datetime.utcnow().strftime('%d %b %Y %H:%M UTC')
 
-            logger.info('')
-            logger.info('Training model on gauge data')
+            logging.info('')
+            logging.info('Training model on gauge data')
 
-            logger.info('Initializing random forest model {:s}'.format(model))                
-            if len(config[model]['QUANTILES']) == 0:
-                reg = RandomForestRegressorBC(degree = 1, 
+            reg = RandomForestRegressorBC(degree = 1, 
                           bctype = config[model]['BIAS_CORR'],
+                          visib_weighting = config[model]['VERT_AGG']['VISIB_WEIGHTING'],
                           variables = features,
                           beta = config[model]['VERT_AGG']['BETA'],
-                          visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'],
+                          metadata = config[model]['FILTERING'],
                           **config[model]['RANDOMFOREST_REGRESSOR'])
-            else:
-                reg = QuantileRandomForestRegressorBC(degree = 1, 
-                          bctype = config[model]['BIAS_CORR'],
-                          variables = features,
-                          beta = config[model]['VERT_AGG']['BETA'],
-                          visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'],
-                          **config[model]['RANDOMFOREST_REGRESSOR'])
-                # reg = QuantileRegressionForest(**config[model]['RANDOMFOREST_REGRESSOR'])
-                # reg.bctype = config[model]['BIAS_CORR']
-                # reg.variables = features
-                # reg.beta = config[model]['VERT_AGG']['BETA']
-                # reg.visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING']
-
-            logger.info('Fitting random forest model {:s}'.format(model))
-
-            reg.fit(features_VERT_AGG[valid].to_numpy(), Y[valid])
-
-            logger.info('Model {} took {} minutes to be trained'.format(model, datetime.datetime.now()-logtime0))
 
+            reg.fit(features_VERT_AGG[valid], Y[valid])
+            
             out_name = str(Path(output_folder, '{:s}_BETA_{:2.1f}_BC_{:s}.p'.format(model, 
                                                   config[model]['VERT_AGG']['BETA'],
                                                   config[model]['BIAS_CORR'])))
-            logger.info('Saving model to {:s}'.format(out_name))
+            logging.info('Saving model to {:s}'.format(out_name))
             
             pickle.dump(reg, open(out_name, 'wb'))
 
-            del reg
-
-
-class RFModelEval(object):
-    '''
-    This is the main class that allows to preparate data for random forest
-    training, train random forests and perform cross-validation of trained models
-    '''
-    def __init__(self, db_location, input_location=None):
-        """
-        Initializes the class to analyse the model and perform standard ML 
-        evaluation on it
-        
-        Note that when calling this constructor the input data is only 
-        generated for the central pixel (NX = NY = 0 = loc of gauge), if you
-        want to regenerate the inputs for all neighbour pixels, please 
-        call the function self.prepare_input(only_center_pixel = False)
-        
-        Parameters
-        ----------
-        db_location : str
-            Location of the main directory of the database (with subfolders
-            'reference', 'gauge' and 'radar' on the filesystem)
-        input_location : str
-            Location of the prepared input data, if this data cannot be found
-            in this folder, it will be computed here, default is a subfolder
-            called rf_input_data within db_location
-        force_regenerate_input : bool
-            if True the input parquet files will always be regenerated from
-            the database even if already present in the input_location folder
-        """
-        
-        if input_location == None:
-            input_location = str(Path(db_location, 'rf_input_data'))
-            
-        # Check if at least gauge.parquet, refer_x0y0.parquet and radar_x0y0.parquet
-        # are present
-        valid = True
-        if not os.path.exists(input_location):
-            valid = False
-            os.makedirs(input_location)
-        files = glob.glob(str(Path(input_location, '*')))
-        files = [os.path.basename(f) for f in files]
-        if ('gauge.parquet' not in files or 'reference_x0y0.parquet' not in files
-            or 'radar_x0y0.parquet' not in files):
-            valid = False
-        
-        self.input_location = input_location
-        self.db_location = db_location
-        
-        if not valid :
-            logger.info('Could not find valid input data from the folder {:s}'.format(input_location))
-
-    def calc_model_predictions(self, models_dic, output_folder, model_folder=None,
-                    tstart=None, tend=None, reference=['CPCH', 'RZC'],
-                    station_obs = ['TRE200S0', 'DKL010Z0', 'FKL010Z0'],
-                    ensemble = [] , quantile_dic = {}):
-        """_summary_
-
-        Parameters
-        -----------
-        models_dic : dic
-            dic with modelname, filename of model
-            e.g.,model_dic = {'RFO': 'RFO_BETA_-0.5_BC_spline.p'}
-        output_folder : str
-            Path where the dataframe is stored
-        model_folder : str, optional
-            Path where model is stored. Defaults to None.
-        output_folder : str
-            Path to where to store the scores
-        tstart: str (YYYYMMDDHHMM)
-            A date to define a starting time for the input data
-        tend: str (YYYYMMDDHHMM)
-            A date to define the end of the input data
-        reference : list, optional 
-            _description_. Defaults to ['CPCH', 'RZC'].
-        station_obs : list, optional
-            _description_. Defaults to ['TRE200S0', 'DKL1010Z0', 'FKL010Z0'].
-        ensemble : list
-            List with all models that a ensemble output is wished for
-        quantile_dic : dic
-            Dictionary with modelname and quantiles to extract,
-            e.g., quantile_dic = {'QuRFO': [0.05,0.10,0.5,0.9,0.95]}
-        """
-
-        # Get models and model path
-        modelnames = list(models_dic.keys())
-        if model_folder == None:
-            MODEL_FOLDER = self.model_paths
-        else:
-            MODEL_FOLDER = model_folder
-
-        # Get data from database, and filter it according to the time limitations
-        if len(reference) == 0 :
-            gaugetab, radartab, grp_hourly, grp_vertical = \
-                readInputData(self.input_location, tstart, tend, datalist=['gauge', 'radar'])
-        else:
-            gaugetab, radartab, refertab, grp_hourly, grp_vertical =\
-                readInputData(self.input_location, tstart, tend, datalist=['gauge', 'radar', 'refer'])
-
-        #################################################################################
-        # Read models and create features dictionary
-        #################################################################################
-        regressors = {}
-        features_dic = {}
-
-        for model in modelnames:
-            logger.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
-        
-            # regressors[model] = pickle.load(open(Path(MODEL_FOLDER,features_dic[model]),'rb'))
-            regressors[model] = read_rf(models_dic[model], MODEL_FOLDER)
-            features = regressors[model].variables.copy()
-            features_dic[model] = features
-            
-            # As RADAR_prop will be calculated below, adding variable here:
-            # Compute additional data if needed
-            features_to_be_removed = []
-            for f in features:
-                # Radar_prop is calculated with vert_aggregation
-                if f.startswith('RADAR_prop'):
-                    features_to_be_removed.append(f)
-
-            if len(features_to_be_removed) > 0:
-                for ftbr in features_to_be_removed:
-                    features.remove(ftbr)
-                features.append('RADAR')
-
-            regressors[model].features = features.copy()
-
-        ###############################################################################
-        # Get linear units of reflectivity
-        ###############################################################################
-        radartab, features = processFeatures(features_dic, radartab)
-
-        for colname in radartab.columns:
-            list_feat = np.unique([item for sub in list(features_dic.values())
-                        for item in sub])
-            if colname.replace('_mean', '') in list_feat:
-                radartab.rename(columns = {colname:colname.replace('_mean','')}, inplace=True)
-
-        ###############################################################################
-        # Compute vertical aggregation
-        ###############################################################################
-        features_VERT_AGG = {}
-        for im, model in enumerate(modelnames):
-            logger.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
-
-            beta = regressors[model].beta
-            visib_weighting = regressors[model].visib_weighting
-
-            if (im > 0) and (beta == regressors[modelnames[im-1]].beta) \
-                    and (visib_weighting == regressors[modelnames[im-1]].visib_weighting) :
-                logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1]))
-                features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy()
-            else:
-                vweights = 10**(beta*(radartab['HEIGHT']/1000.)) # vert. weights
-                try:
-                    features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]],
-                                        vweights, grp_vertical,
-                                        visib_weighting,
-                                        radartab['VISIB_mean'])
-                except:
-                    features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]],
-                                        vweights, grp_vertical,
-                                        visib_weighting,
-                                        radartab['VISIB'])                    
-
-        ###############################################################################
-        # Clean and prepare data
-        ###############################################################################
-        test_not_ok = False
-        for iv, val in enumerate(radartab['s-tstamp'].groupby(grp_vertical).first()):
-            if gaugetab['s-tstamp'].iloc[iv] != val:
-                test_not_ok = True
-                print(gaugetab['s-tstamp'][iv])
-        if test_not_ok:
-            logger.error('Time cut went wrong!!')
-        
-        valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]),
-                    axis = 1)
-
-        for model in modelnames:
-            features_VERT_AGG[model] = features_VERT_AGG[model][valid]
-
-        gaugetab = gaugetab[valid]
-        refertab = refertab[valid]
-        grp_hourly = grp_hourly[valid]
-
-        ###############################################################################
-        # Assemble dataframe with data from database
-        ###############################################################################
-        logger.info('Assembling dataframe')
-        data_pred = pd.DataFrame({'TIMESTAMP': gaugetab['TIMESTAMP'],
-                            'STATION': gaugetab['STATION'],
-                            'RRE150Z0': gaugetab['RRE150Z0']*6,
-                            'IDX_HOURLY': grp_hourly})
-
-        for var in station_obs:
-            try:
-                data_pred[var] = gaugetab[var]
-            except:
-                logger.error('Could not add {}'.format(var))                
-
-        for ref in reference:
-            try:
-                data_pred[ref] = refertab[ref]
-            except:
-                logger.error('Could not add {}'.format(ref))
-
-        ###############################################################################
-        # Calculate model values
-        ###############################################################################
-        for model in modelnames:
-            logger.info('Calculate estimates of {}'.format(model))
-
-            if model in quantile_dic.keys():
-                R_pred_10 = regressors[model].predict(features_VERT_AGG[model][regressors[model].variables],
-                            quantiles=quantile_dic[model])
-                data_pred[model] = R_pred_10[0]
-                data_quant = pd.DataFrame(R_pred_10[1], 
-                                        columns = [model+'_Q'+str(qu) for qu in quantile_dic[model]],
-                                        index = data_pred.index)
-                data_pred = pd.concat([data_pred, data_quant], axis=1)
-            else:
-                data_pred[model] = regressors[model].predict(\
-                    features_VERT_AGG[model][regressors[model].variables])
-
-            if (model in ensemble) & ('Qu' not in model) :
-                pred_ens = regressors[model].predict_ens(\
-                        features_VERT_AGG[model][regressors[model].variables])
-                for tree in range(regressors[model].n_estimators):
-                    data_pred['{}_E{}'.format(model, tree)] =  pred_ens[:,tree]
-                    # data_pred['{}_E{}'.format(model, tree)] = \
-                    #     regressors[model].estimators_[tree].predict(\
-                    #     features_VERT_AGG[model][regressors[model].variables])
-
-        ###############################################################################
-        # Save output
-        ###############################################################################
-        name_file = str(Path(output_folder, 'rfmodels_x0_y0.parquet'))
-        data_pred.to_parquet(name_file, compression='gzip', index=False)
-        logger.info('Saved file: {}'.format(name_file))
-
-
     def feature_selection(self, features_dic, featuresel_configfile, 
                         output_folder, K=5, tstart=None, tend=None):
         """
@@ -947,14 +468,71 @@ def feature_selection(self, features_dic, featuresel_configfile,
         config = envyaml(featuresel_configfile)
         modelnames = list(features_dic.keys())
 
+        #######################################################################
+        # Read data
+        #######################################################################
+        logging.info('Reading input data from {}'.format(self.input_location))
+        radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet')))
+        gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet')))
+        grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb'))
+        grp_vertical = grp['grp_vertical']
+        grp_hourly = grp['grp_hourly']
+
+        #######################################################################
+        # Filter time
+        #######################################################################
+        if tstart != None:
+            try:
+                tstart = datetime.datetime.strptime(tstart,
+                        '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
+            except:
+                tstart = gaugetab['TIMESTAMP'].min()
+                logging.info('The format of tstart was wrong, taking the earliest date')
+        if tend != None:
+            try:
+                tend = datetime.datetime.strptime(tend,
+                        '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
+            except:
+                tend = gaugetab['TIMESTAMP'].max()
+                logging.info('The format of tend was wrong, taking the earliest date')
+
+        timevalid = gaugetab['TIMESTAMP'].copy().astype(bool)
+        vertvalid = radartab['TIMESTAMP'].copy().astype(bool)
+
+        if (tstart != None):
+            timevalid[(gaugetab['TIMESTAMP'] < tstart)] = False
+            vertvalid[(radartab['TIMESTAMP'] < tstart)] = False
+        if (tend != None):
+            timevalid[(gaugetab['TIMESTAMP'] > tend)] = False
+            vertvalid[(radartab['TIMESTAMP'] > tend)] = False
+
+        gaugetab = gaugetab[timevalid]
+        grp_hourly = grp_hourly[timevalid]
+        radartab = radartab[vertvalid]
+        grp_vertical = grp_vertical[vertvalid]
 
         ###############################################################################
-        # Read data, filter time and get linear reflectivity units
+        # Compute additional data if needed
         ###############################################################################
-        gaugetab, radartab, _, grp_hourly, grp_vertical = \
-            readInputData(self.input_location, tstart, tend)
-
-        radartab, features = processFeatures(features_dic, radartab)
+        # currently the only supported additional features is zh (refl in linear units)
+        # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars)
+        # Get list of unique features names
+        features = np.unique([item for sub in list(features_dic.values())
+                            for item in sub])
+        for f in features:
+            if 'zh' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')])
+            elif 'zv' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')])        
+            if 'DIST_TO_RAD' in f:
+                info_radar = constants.RADARS
+                vals = np.unique(radartab['RADAR'])
+                for val in vals:
+                    dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+
+                           (radartab['Y'] - info_radar['Y'][val])**2) / 1000.
+                    radartab['DIST_TO_RAD' + str(val)] = dist
 
         ###############################################################################
         # Compute vertical aggregation
@@ -962,11 +540,11 @@ def feature_selection(self, features_dic, featuresel_configfile,
         features_VERT_AGG = {}
         regressors = {}
         for im, model in enumerate(modelnames):
-            logger.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
+            logging.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
           
             if (im > 0) and (config[model]['VERT_AGG']['BETA'] == config[modelnames[im-1]]['VERT_AGG']['BETA']) \
                     and (config[model]['VERT_AGG']['VISIB_WEIGHTING'] == config[modelnames[im-1]]['VERT_AGG']['VISIB_WEIGHTING']):
-                logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1]))
+                logging.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1]))
                 features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy()
             else:
                 vweights = 10**(config[model]['VERT_AGG']['BETA'] *
@@ -987,13 +565,18 @@ def feature_selection(self, features_dic, featuresel_configfile,
         valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]),
                        axis = 1)
 
+        # if (tstart != None):
+        #     valid[(gaugetab['TIMESTAMP'] < tstart)] = False
+        # if (tend != None):
+        #     valid[(gaugetab['TIMESTAMP'] > tend)] = False
+
         test_not_ok = False
         for iv, val in enumerate(radartab['s-tstamp'].groupby(grp_vertical).first()):
             if gaugetab['s-tstamp'][iv] != val:
                 test_not_ok = True
                 print(gaugetab['s-tstamp'][iv])
         if test_not_ok:
-            logger.error('Time cut went wrong!!')
+            logging.error('Time cut went wrong!!')
 
         for model in modelnames:
             features_VERT_AGG[model] = features_VERT_AGG[model][valid]
@@ -1011,7 +594,7 @@ def feature_selection(self, features_dic, featuresel_configfile,
         if (K != None):
             K = list(range(K))
         elif (K == None):
-            logger.info('Cross validation with random events defined but not specified, applying 5-fold CV')
+            logging.info('Cross validation with random events defined but not specified, applying 5-fold CV')
             K = list(range(5))
 
         idx_testtrain = split_event(gaugetab['TIMESTAMP'].values, len(K))
@@ -1028,7 +611,7 @@ def feature_selection(self, features_dic, featuresel_configfile,
                     scores[tagg][model][feat] = []
 
         for k in K:
-            logger.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K)))
+            logging.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K)))
 
             test = idx_testtrain == k
             train = idx_testtrain != k
@@ -1049,7 +632,7 @@ def feature_selection(self, features_dic, featuresel_configfile,
                 rmse_ref_60 = perfscores(R_pred_60, R_test_60, bounds=None)['all']['RMSE']
 
                 for feat in features_VERT_AGG[model].keys():
-                    logger.info('Shuffling feature: {}'.format(feat))
+                    logging.info('Shuffling feature: {}'.format(feat))
                     # Shuffle input feature on test fraction, keep others untouched
                     x_test = features_VERT_AGG[model][test].copy()
                     x_test[feat] = np.random.permutation(x_test[feat].values)
@@ -1076,7 +659,7 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
                               bounds10 = [0,2,10,100], bounds60 = [0,2,10,100],
                               cross_val_type='years', K=5, years=None,
                               tstart=None, tend=None, station_scores=False,
-                              save_model=False, save_output=False):
+                              save_model=False):
         """
         Does an intercomparison (cross-validation) of different RF models and
         reference products (RZC, CPC, ...) and plots the performance plots
@@ -1110,7 +693,7 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
             at hourly time resolution
             [0,1,10,100] will give scores in range [0-1], [1-10] and [10-100]
         cross_val_type: str
-            Define how the split of events is done. Options are "random", 
+            Define how the split of events is done. Options are "random events", 
             "years" and "seasons" (TODO)
         K : int or None
             Number of splits in iterations do perform in the K fold cross-val
@@ -1141,194 +724,267 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
             raise ValueError('Keys in features_dic are not all present in intercomparison config file!')
   
         if (cross_val_type == 'years') and (years == None):
-            logger.info('Cross validation years defined, but not specified, years from 2016-2021 used')
-            K = list(range(2016,2023,1))
+            logging.info('Cross validation years defined, but not specified, years from 2016-2021 used')
+            K = list(range(2016,2022,1))
         elif (cross_val_type == 'years') and (years != None):
             K = years
 
-        if (cross_val_type == 'random') and (K != None):
+        if (cross_val_type == 'random events') and (K != None):
             K = list(range(K))
-        elif (cross_val_type == 'random') and (K == None):
-            logger.info('Cross validation with random events defined but not specified, applying 5-fold CV')
+        elif (cross_val_type == 'random events') and (K == None):
+            logging.info('Cross validation with random events defined but not specified, applying 5-fold CV')
             K = list(range(5))
 
+        #######################################################################
+        # Read data
+        #######################################################################
+        logging.info('Reading input data from {}'.format(self.input_location))
+        radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet')))
+        refertab = pd.read_parquet(str(Path(self.input_location, 'reference_x0y0.parquet')))
+        gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet')))
+        grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb'))
+        grp_vertical = grp['grp_vertical']
+        grp_hourly = grp['grp_hourly']
+
+        #######################################################################
+        # Filter time
+        #######################################################################
+        if tstart != None:
+            try:
+                tstart = datetime.datetime.strptime(tstart,
+                        '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
+            except:
+                tstart = gaugetab['TIMESTAMP'].min()
+                logging.info('The format of tstart was wrong, taking the earliest date')
+        if tend != None:
+            try:
+                tend = datetime.datetime.strptime(tend,
+                        '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp()
+            except:
+                tend = gaugetab['TIMESTAMP'].max()
+                logging.info('The format of tend was wrong, taking the earliest date')
 
         ###############################################################################
-        # Read and filter data with time constraints
+        # Compute additional data if needed
         ###############################################################################
-        gaugetab, radartab, refertab, grp_hourly, grp_vertical = \
-            readInputData(self.input_location, tstart, tend, 
-                        datalist=['gauge', 'radar', 'refer'])
-
-        radartab, _ = processFeatures(features_dic, radartab)
+        # currently the only supported additional features is zh (refl in linear units)
+        # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars)
+        # Get list of unique features names
+        features = np.unique([item for sub in list(features_dic.values())
+                            for item in sub])
+        for f in features:
+            if 'zh' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')])
+            elif 'zv' in f:
+                logging.info('Computing derived variable {:s}'.format(f))
+                radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')])        
+            if 'DIST_TO_RAD' in f:
+                info_radar = constants.RADARS
+                vals = np.unique(radartab['RADAR'])
+                for val in vals:
+                    dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+
+                           (radartab['Y'] - info_radar['Y'][val])**2) / 1000.
+                    radartab['DIST_TO_RAD' + str(val)] = dist
+                    
         ###############################################################################
-        # Compute vertical aggregation and initialize model
+        # Compute vertical aggregation
         ###############################################################################
-
         features_VERT_AGG = {}
         regressors = {}
-        for im, model in enumerate(modelnames):
-            logger.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
+        for model in modelnames:
+            logging.info('Performing vertical aggregation of input features for model {:s}'.format(model))            
           
-            # Save computational time if the input data is the same within the model
-            if (im > 0) and (config[model]['VERT_AGG']['BETA'] == config[modelnames[im-1]]['VERT_AGG']['BETA']) \
-                    and (config[model]['VERT_AGG']['VISIB_WEIGHTING'] == config[modelnames[im-1]]['VERT_AGG']['VISIB_WEIGHTING']):
-                logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1]))
-                features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy()
-            else:
-                vweights = 10**(config[model]['VERT_AGG']['BETA'] *
-                                    (radartab['HEIGHT']/1000.)) # vert. weights
-                features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], 
-                                    vweights, grp_vertical,
-                                    config[model]['VERT_AGG']['VISIB_WEIGHTING'],
-                                    radartab['VISIB_mean'])
-            
-            # Use either the classical RainForest model or the new one
-            if len(config[model]['QUANTILES']) == 0:
-                regressors[model] = RandomForestRegressorBC(degree = 1, 
+            vweights = 10**(config[model]['VERT_AGG']['BETA'] *
+                                (radartab['HEIGHT']/1000.)) # vert. weights
+            features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], 
+                                 vweights, grp_vertical,
+                                 config[model]['VERT_AGG']['VISIB_WEIGHTING'],
+                                 radartab['VISIB_mean'])
+                  
+            regressors[model] = RandomForestRegressorBC(degree = 1, 
                           bctype = config[model]['BIAS_CORR'],
                           variables = features_dic[model],
                           beta = config[model]['VERT_AGG']['BETA'],
                           visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'],
                           **config[model]['RANDOMFOREST_REGRESSOR'])
-            else:
-                regressors[model] = QuantileRegressionForest(**config[model]['RANDOMFOREST_REGRESSOR'])
-
-        ###############################################################################
-        # Remove nans within dataset
-        ###############################################################################
+        
+        # remove nans
         valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]),
                        axis = 1)
+        # if (tstart != None) and (tend == None):
+        #     (gaugetab['TIMESTAMP'] >= tstart)
+        # elif (tstart == None) and (tend != None):
+        #     timeperiod = (gaugetab['TIMESTAMP'] <= tend)
+        # elif (tstart != None) and (tend != None):
+        #     timeperiod = (gaugetab['TIMESTAMP'] >= tstart) & (gaugetab['TIMESTAMP'] <= tend)
+        # else:
+        #     timeperiod = valid
+        if (tstart != None):
+            valid[(gaugetab['TIMESTAMP'] < tstart)] = False
+        if (tend != None):
+            valid[(gaugetab['TIMESTAMP'] > tend)] = False
+
         for model in modelnames:
             features_VERT_AGG[model] = features_VERT_AGG[model][valid]
-
+        
         gaugetab = gaugetab[valid]
         refertab = refertab[valid]
         grp_hourly = grp_hourly[valid]
         
-        ###############################################################################
         # Get R, T and idx test/train
-        ###############################################################################
         R = np.array(gaugetab['RRE150Z0'] * 6) # Reference precip in mm/h
         R[np.isnan(R)] = 0
+        
         T = np.array(gaugetab['TRE200S0'])  # Reference temp in degrees
+        # features must have the same size as gauge
 
-        if cross_val_type == 'random':
+        if cross_val_type == 'random_events':
             idx_testtrain = split_event(gaugetab['TIMESTAMP'].values, len(K))
         elif cross_val_type == 'years':
             idx_testtrain = split_years(gaugetab['TIMESTAMP'].values, years=K)
         else:
-            logger.error('Please define your cross validation separation')
+            logging.error('Please define your cross validation separation')
+
 
-        ###############################################################################
-        # Initialize outputs
-        ###############################################################################
         modelnames.extend(reference_products)
 
-        if station_scores == False:
-            all_scores, all_stats = prepareScoreDic(modelnames, station_scores)
-        else:
-            all_scores, all_stats, all_station_scores, all_station_stats = \
-                    prepareScoreDic(modelnames, station_scores)
-            
+        all_scores = {'10min':{},'60min':{}}
+        all_stats = {'10min':{},'60min':{}}
+        
+        if station_scores == True:
+            all_station_scores = {'10min': {}, '60min': {}}
+            all_station_stats = {'10min': {}, '60min': {}}
+
         ###############################################################################
-        # MAIN LOOP: CROSS VALIDATION
+        # Initialize outputs
         ###############################################################################
-        for ik, k in enumerate(K):
-            logger.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K)))
+        for model in modelnames:
+            all_scores['10min'][model] = {'train': {'solid':[],'liquid':[],'all':[]},
+                         'test':  {'solid':[],'liquid':[],'all':[]}}
+            all_scores['60min'][model] = {'train': {'solid':[],'liquid':[],'all':[]},
+                         'test':  {'solid':[],'liquid':[],'all':[]}}
+            
+            all_stats['10min'][model] = {'train': {'solid':{},'liquid':{},'all':{}},
+                         'test':  {'solid':{},'liquid':{},'all':{}}}
+            
+            all_stats['60min'][model] = {'train': {'solid':{},'liquid':{},'all':{}},
+                         'test':  {'solid':{},'liquid':{},'all':{}}}
+            
+            if station_scores == True:
+                # for station scores we will limit the output to test data only
+                for timeagg in all_station_scores.keys():
+                    all_station_scores[timeagg][model] = {'solid':{},'liquid':{},'all':{}}
+                    all_station_stats[timeagg][model] = {'solid':{},'liquid':{},'all':{}}
+            
+
+        for k in K:
+            logging.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K)))
 
             test = idx_testtrain == k
             train = idx_testtrain != k
             
             if cross_val_type == 'years':
-                logger.info('Time range for testing set: {} - {} with {:3.2f}% of datapoints'.format(
+                logging.info('Time range for testing set: {} - {} with {:3.2f}% of datapoints'.format(
                                 datetime.datetime.utcfromtimestamp(gaugetab['TIMESTAMP'][test].min()),
                                 datetime.datetime.utcfromtimestamp(gaugetab['TIMESTAMP'][test].max()),
                                 gaugetab['TIMESTAMP'][test].count()/ gaugetab['TIMESTAMP'].count()*100))
-            
-            idxTemp = getTempIDX(T, grp_hourly, test, train)
 
-            R_obs_test_60 = np.squeeze(np.array(pd.DataFrame(R[test])
+            # Get reference values
+            R_test_60 = np.squeeze(np.array(pd.DataFrame(R[test])
                             .groupby(grp_hourly[test]).mean()))
     
-            R_obs_train_60 = np.squeeze(np.array(pd.DataFrame(R[train])
+            R_train_60 = np.squeeze(np.array(pd.DataFrame(R[train])
                             .groupby(grp_hourly[train]).mean()))
-
-            idxTemp['10min']['test']['all'] = R[test].astype(bool)
-            idxTemp['10min']['train']['all'] = R[train].astype(bool)
-            idxTemp['60min']['train']['all'] = R_obs_train_60.astype(bool)
-            idxTemp['60min']['test']['all'] =  R_obs_test_60.astype(bool)
-
+            
+            T_test_60 = np.squeeze(np.array(pd.DataFrame(T[test])
+                            .groupby(grp_hourly[test]).mean()))
+            
+            T_train_60 = np.squeeze(np.array(pd.DataFrame(T[train])
+                            .groupby(grp_hourly[train]).mean()))
+            
+            
+            liq_10_train = T[train] >= constants.THRESHOLD_SOLID
+            sol_10_train = T[train] < constants.THRESHOLD_SOLID
+            liq_60_train = T_train_60 >= constants.THRESHOLD_SOLID
+            sol_60_train = T_train_60 < constants.THRESHOLD_SOLID
+    
+            liq_10_test = T[test] >= constants.THRESHOLD_SOLID
+            sol_10_test = T[test] < constants.THRESHOLD_SOLID
+            liq_60_test = T_test_60 >= constants.THRESHOLD_SOLID
+            sol_60_test = T_test_60 < constants.THRESHOLD_SOLID
+            
             # Fit every regression model
             for model in modelnames:
-                logger.info('Checking model {:s}'.format(model))
+                logging.info('Checking model {:s}'.format(model))
+                logging.info('Evaluating test error')
+                # 10 min
+                logging.info('at 10 min')
 
                 # Performing fit
                 if model not in reference_products:
-                    logger.info('Training model on gauge data')
-
-                    regressors[model].fit(features_VERT_AGG[model][train].to_numpy(),R[train])
-
-                    if len(config[model]['QUANTILES']) == 0 :
-                        R_pred_10 = regressors[model].predict(features_VERT_AGG[model][test])
-                    else:
-                        # In this case, R_pred_10 becomes a tuple
-                        R_pred_10_all = regressors[model].predict(features_VERT_AGG[model][test].to_numpy(),
-                                                quantiles=config[model]['QUANTILES'])
-
-                        # Save output for analysis
-                        R_pred_10 = R_pred_10_all[0]
-
-                        RFquantiles = np.column_stack([R_pred_10_all[0], R_pred_10_all[1]])
-                        header = ['mean'] + [str(qe) for qe in config[model]['QUANTILES']]
-                        RFQ = pd.DataFrame(RFquantiles, columns=header)
-                        RFQ['gauge'] = R[test]
-                        filename = str(Path(output_folder, 'RFQuantiles_{}_K_fold_{}.parquet'.format(model, k)))
-                        logger.info('Saving data to {:s}'.format(filename))
-                        RFQ.to_parquet(filename)
+                    logging.info('Training model on gauge data')
 
+                    regressors[model].fit(features_VERT_AGG[model][train],R[train])
+                    R_pred_10 = regressors[model].predict(features_VERT_AGG[model][test])
+    
                     if (save_model == True):
                         regressors[model].variables = features_VERT_AGG[model].columns
                         out_name = str(Path(output_folder, '{:s}_BETA_{:2.1f}_BC_{:s}_excl_{}.p'.format(model, 
                                                             config[model]['VERT_AGG']['BETA'],
                                                             config[model]['BIAS_CORR'],k)))
-                        logger.info('Saving model to {:s}'.format(out_name))
+                        logging.info('Saving model to {:s}'.format(out_name))
                         pickle.dump(regressors[model], open(out_name, 'wb'))
 
                 else:
                     R_pred_10 = refertab[model].values[test]
                 
-                logger.info('Evaluating test error')
-                logger.info('at 10 min')
 
-                all_scores = calcScore(all_scores, obs=R[test], pred=R_pred_10,
-                                idxTemp = idxTemp, bounds= bounds10, 
-                                tagg = '10min', model=model, data_type='test')
+                scores_solid = perfscores(R_pred_10[sol_10_test],
+                                          R[test][sol_10_test],
+                                          bounds = bounds10)
+               
+                all_scores['10min'][model]['test']['solid'].append(scores_solid)
+                
+                scores_liquid = perfscores(R_pred_10[liq_10_test],
+                                                 R[test][liq_10_test],
+                                                 bounds = bounds10)
+                all_scores['10min'][model]['test']['liquid'].append(scores_liquid)
+                
+                scores_all = perfscores(R_pred_10,
+                                                 R[test],
+                                                 bounds = bounds10)
+                all_scores['10min'][model]['test']['all'].append(scores_all)
     
                 # 60 min
-                logger.info('at 60 min')
+                logging.info('at 60 min')
                 R_pred_60 = np.squeeze(np.array(pd.DataFrame(R_pred_10)
                                     .groupby(grp_hourly[test]).mean()))
-
-                all_scores = calcScore(all_scores, obs=R_obs_test_60, pred=R_pred_60,
-                                idxTemp = idxTemp, bounds= bounds60, 
-                                tagg = '60min', model=model, data_type='test')
+    
+                scores_solid = perfscores(R_pred_60[sol_60_test],
+                                                 R_test_60[sol_60_test],
+                                                 bounds = bounds60)
+                all_scores['60min'][model]['test']['solid'].append(scores_solid)
+                            
+                
+                scores_liquid = perfscores(R_pred_60[liq_60_test],
+                                                 R_test_60[liq_60_test],
+                                                 bounds = bounds60)
+                all_scores['60min'][model]['test']['liquid'].append(scores_liquid)
+                
+                scores_all = perfscores(R_pred_60,
+                                                 R_test_60,
+                                                 bounds = bounds60)
+                all_scores['60min'][model]['test']['all'].append(scores_all)
                 
                 if station_scores == True:
-                    logger.info('Calculating station performances for model {}'.format(model)) 
+                    logging.info('Calculating station performances for model {}'.format(model)) 
                     
                     stations_60 = np.array(gaugetab['STATION'][test]
                                     .groupby(grp_hourly[test]).first())
 
                     df = pd.DataFrame(columns=gaugetab['STATION'].unique(),      
-                                        index = all_scores['60min'][model]['test']['all'][ik]['all'].keys())
-
-                    # Fast fix
-                    liq_10_test = idxTemp['10min']['test']['liquid']
-                    sol_10_test = idxTemp['10min']['test']['solid']
-                    liq_60_test = idxTemp['60min']['test']['liquid'] 
-                    sol_60_test = idxTemp['60min']['test']['solid']
-
+                    index = scores_all['all'].keys())
 
                     for timeagg in all_station_scores.keys():
                         all_station_scores[timeagg][model]['all'][k] = df.copy()
@@ -1344,83 +1000,80 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
                                                     R[test][sta_idx])['all']
                             all_station_scores['10min'][model]['all'][k][sta] = scores_all_10
                             
-                            scores_all_60 = perfscores(R_pred_60[sta_idx_60],R_obs_test_60[sta_idx_60])['all']
+                            scores_all_60 = perfscores(R_pred_60[sta_idx_60],R_test_60[sta_idx_60])['all']
                             all_station_scores['60min'][model]['all'][k][sta] = scores_all_60
                             
                             del scores_all_10, scores_all_60
                         except:
-                            logger.info('No performance score for {}'.format(sta))
+                            logging.info('No performance score for {}'.format(sta))
                         try:                            
                             scores_liquid_10 = perfscores(R_pred_10[liq_10_test & sta_idx],
                                                     R[test][liq_10_test & sta_idx])['all']
                             all_station_scores['10min'][model]['liquid'][k][sta] = scores_liquid_10
                             
                             scores_liquid_60 = perfscores(R_pred_60[liq_60_test & sta_idx_60],
-                                                    R_obs_test_60[liq_60_test & sta_idx_60])['all']
+                                                    R_test_60[liq_60_test & sta_idx_60])['all']
                             all_station_scores['60min'][model]['liquid'][k][sta] = scores_liquid_60  
                         except:
-                            logger.info('No performance score for liquid precip for {}'.format(sta))
+                            logging.info('No performance score for liquid precip for {}'.format(sta))
                         try:                            
                             scores_solid_10 = perfscores(R_pred_10[sol_10_test & sta_idx],
                                                     R[test][sol_10_test & sta_idx])['all']  
                             all_station_scores['10min'][model]['solid'][k][sta] = scores_solid_10 
                             
                             scores_solid_60 = perfscores(R_pred_60[sol_60_test & sta_idx_60],
-                                                        R_obs_test_60[sol_60_test & sta_idx_60])['all']
+                                                        R_test_60[sol_60_test & sta_idx_60])['all']
                             all_station_scores['60min'][model]['solid'][k][sta] = scores_solid_60
                         except:
-                            logger.info('No performance score for solid precip for {}'.format(sta))
-
-                # Save output of training and testing data
-                if save_output:
-                    if model not in reference_products:
-                        data = features_VERT_AGG[model][test].copy()
-                        data[model] = R_pred_10.copy()
-                    else: 
-                        data = pd.DataFrame(pd.Series(R_pred_10, name=model))
-                    
-                    data['gauge'] = R[test].copy()
-                    data['TIMESTAMP'] = gaugetab['TIMESTAMP'][test]
-                    data['STATION'] = gaugetab['STATION'][test]
-
-                    out_name = str(Path(output_folder, 'data_{}_test_10min_CVfold_{}.csv'.format(model,k)))
-                    data.to_csv(out_name, index=False)
-                    logger.info('Saving data to {:s}'.format(out_name))
-
-                    data_60 = pd.DataFrame({'gauge': R_obs_test_60, model: R_pred_60})
-                    data_60['STATION'] = np.array(gaugetab['STATION'][test]
-                                                .groupby(grp_hourly[test]).first())
-                    data_60['TIMESTAMP'] = np.array(gaugetab['TIMESTAMP'][test]
-                                                .groupby(grp_hourly[test]).max())
-                    data_60['n_counts'] = np.squeeze(np.array(pd.DataFrame(R[test])
-                                                .groupby(grp_hourly[test]).count()))
-                    out_name = str(Path(output_folder, 'data_{}_test_60min_CVfold_{}.csv'.format(model,k)))
-                    data_60.to_csv(out_name, index=False)
-                    logger.info('Saving data to {:s}'.format(out_name))
+                            logging.info('No performance score for solid precip for {}'.format(sta))
 
                 # train
-                logger.info('Evaluating train error')
+                logging.info('Evaluating train error')
                 # 10 min
-                logger.info('at 10 min')
+                logging.info('at 10 min')
                 
                 if model not in reference_products:
                     R_pred_10 = regressors[model].predict(features_VERT_AGG[model][train])
                 else:
                     R_pred_10 = refertab[model].values[train]
                     
-
-                all_scores = calcScore(all_scores, obs=R[train], pred=R_pred_10,
-                                idxTemp = idxTemp, bounds= bounds60, 
-                                tagg = '10min', model=model, data_type='train')   
-
-                # 60 min
-                logger.info('at 60 min')
+                scores_solid = perfscores(R_pred_10[sol_10_train],
+                                                 R[train][sol_10_train],
+                                                 bounds = bounds10)
+                all_scores['10min'][model]['train']['solid'].append(scores_solid)
+                
+                scores_liquid = perfscores(R_pred_10[liq_10_train],
+                                                 R[train][liq_10_train],
+                                                 bounds = bounds10)
+                
+                all_scores['10min'][model]['train']['liquid'].append(scores_liquid)
+                
+                scores_all = perfscores(R_pred_10,
+                                                 R[train],
+                                                 bounds = bounds10)
+                all_scores['10min'][model]['train']['all'].append(scores_all)
+                
+                
                 R_pred_60 = np.squeeze(np.array(pd.DataFrame(R_pred_10)
                                     .groupby(grp_hourly[train]).mean()))
-
-                all_scores = calcScore(all_scores, obs=R_obs_train_60, pred=R_pred_60,
-                                idxTemp = idxTemp, bounds= bounds60, 
-                                tagg = '60min', model=model, data_type='train')
+                
+                # 60 min
+                logging.info('at 60 min')
+                # Evaluate model 10 min
+    
+                scores_solid = perfscores(R_pred_60[sol_60_train],
+                                        R_train_60[sol_60_train],
+                                        bounds = bounds60)
+                all_scores['60min'][model]['train']['solid'].append(scores_solid)
+                
+                scores_liquid = perfscores(R_pred_60[liq_60_train],
+                                        R_train_60[liq_60_train],
+                                         bounds = bounds60)
+                all_scores['60min'][model]['train']['liquid'].append(scores_liquid)
+                
+                scores_all = perfscores(R_pred_60,R_train_60,
+                                        bounds = bounds60)
+                all_scores['60min'][model]['train']['all'].append(scores_all)
 
 
         # Compute statistics after the 5-fold cross validation
@@ -1468,9 +1121,11 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
                                                                                           perfs[score].std().rename('std')],
                                                                                          axis=1)
 
+
         if not os.path.exists(output_folder):
             os.makedirs(output_folder)
 
+        plot_crossval_stats(all_stats, output_folder)
         name_file = str(Path(output_folder, 'all_scores.p'))
         pickle.dump(all_scores, open(name_file, 'wb'))
         name_file = str(Path(output_folder, 'all_scores_stats.p'))
@@ -1481,9 +1136,8 @@ def model_intercomparison(self, features_dic, intercomparison_configfile,
             pickle.dump(all_station_scores, open(name_file, 'wb'))
             name_file = str(Path(output_folder, 'all_station_stats.p'))
             pickle.dump(all_station_stats, open(name_file, 'wb'))
-
-        plot_crossval_stats(all_stats, output_folder)
         
-        logger.info('Finished script and saved all scores to {}'.format(output_folder))
+        
+        logging.info('Finished script and saved all scores to {}'.format(output_folder))
         return all_scores, all_stats
             
diff --git a/rainforest/ml/rfdefinitions.py b/rainforest/ml/rfdefinitions.py
index 9856b12..ae52f88 100644
--- a/rainforest/ml/rfdefinitions.py
+++ b/rainforest/ml/rfdefinitions.py
@@ -14,7 +14,6 @@
 import pickle
 import numpy as np
 from sklearn.ensemble import RandomForestRegressor
-from doubt import QuantileRegressionForest
 import os
 from scipy.interpolate import UnivariateSpline
 from pathlib import Path
@@ -84,8 +83,7 @@ def __init__(self,
                  n_jobs=None,
                  random_state=None,
                  verbose=0,
-                 warm_start=False,
-                 max_samples=None):
+                 warm_start=False):
         super().__init__(n_estimators = n_estimators,
                          criterion = criterion,
                          max_depth = max_depth,
@@ -100,8 +98,7 @@ def __init__(self,
                          n_jobs = n_jobs,
                          random_state = random_state,
                          verbose = verbose,
-                         warm_start = warm_start, 
-                         max_samples=None)
+                         warm_start = warm_start)
         
         self.degree = degree
         self.bctype = bctype
@@ -187,198 +184,9 @@ def predict(self, X, round_func = None, bc = True):
         out = func(pred)
         out[out < 0] = 0
         return round_func(out)
-
-    def predict_ens(self, X, round_func = None, bc = True):
-        """
-        Predict regression target for X.
-        The predicted regression target of an input sample is computed as the
-        mean predicted regression targets of the trees in the forest.
-        Parameters
-        ----------
-        X : array-like or sparse matrix of shape (n_samples, n_features)
-            The input samples. Internally, its dtype will be converted to
-            ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csr_matrix``.
-        round_func : lambda function
-            Optional function to apply to outputs (for example to discretize them
-            using MCH lookup tables). If not provided f(x) = x will be applied
-            (i.e. no function)
-        bc : bool
-            if True the bias correction function will be applied
-            
-        Returns
-        -------
-        pred : array (n_samples,n_trees)
-            The predicted values.
-        """
-
-        if round_func == None:
-            round_func = lambda x: x
-            
-        func = lambda x: x
-        if bc:
-            if self.bctype in ['cdf','raw']:
-                func = lambda x : np.polyval(self.p,x)
-            elif self.bctype == 'spline':
-                func = lambda x : self.p(x)
-        
-        pred = np.empty(shape=[X.shape[0], self.n_estimators]) * np.nan
-
-        for tree in range(self.n_estimators):
-            pred_tree = self.estimators_[tree].predict(X)  
-        
-            out = func(pred_tree)
-            out[out < 0] = 0
-
-            pred[:,tree] = round_func(out)
-
-        return pred
-
-
-##################
-
-class QuantileRandomForestRegressorBC(QuantileRegressionForest):
-    '''
-    This is an extension of the RandomForestRegressor regressor class of
-    sklearn that does additional bias correction, is able
-    to apply a rounding function to the outputs on the fly and adds a 
-    bit of metadata:
-    
-        *bctype* : type of bias correction method
-        *variables* : name of input features
-        *beta* : weighting factor in vertical aggregation
-        *degree* : order of the polyfit used in some bias-correction methods
-        *metadata* : configuration setup used to train this model
-    
-    For *bc_type* tHe available methods are currently "raw":
-    simple linear fit between prediction and observation, "cdf": linear fit
-    between sorted predictions and sorted observations and "spline" :
-    spline fit between sorted predictions and sorted observations. Any
-    new method should be added in this class in order to be used.
-    
-    For any information regarding the parent class from the package doubt see
-    https://github.com/saattrupdan/doubt (used version: 4.3.1)
-    
-    '''
-    def __init__(self, 
-                 variables, 
-                 beta,
-                 visib_weighting, 
-                 degree = 1, 
-                 bctype = 'cdf',
-                 metadata = {},
-                 n_estimators=100,
-                 criterion="mse",
-                 max_depth=None,
-                 min_samples_split=2,
-                 min_samples_leaf=1,
-                 min_weight_fraction_leaf=0.,
-                 max_features="auto",
-                 max_leaf_nodes=None,
-                 min_impurity_decrease=0.,
-                 bootstrap=True,
-                 oob_score=False,
-                 n_jobs=None,
-                 random_state=None,
-                 verbose=0,
-                 warm_start=False):
-        super().__init__(n_estimators = n_estimators,
-                         criterion = criterion,
-                         max_depth = max_depth,
-                         min_samples_split = min_samples_split,
-                         min_samples_leaf = min_samples_leaf,
-                         min_weight_fraction_leaf = min_weight_fraction_leaf,
-                         max_features = max_features,
-                         max_leaf_nodes = max_leaf_nodes,
-                         n_jobs = n_jobs,
-                         random_state = random_state,
-                         verbose = verbose)
-        
-        self.degree = degree
-        self.bctype = bctype
-        self.variables = variables
-        self.beta = beta
-        self.visib_weighting = visib_weighting
-        self.metadata = metadata
-
-    def fit(self, X, y):
-        """
-        Fit both estimator and a-posteriori bias correction
-        Parameters
-        ----------
-        X (array-like or sparse matrix):
-            The input samples, of shape [n_samples, n_features]. Internally, it
-            will be converted to `dtype=np.float32` and if a sparse matrix is
-            provided to a sparse `csr_matrix`.
-        y (array-like):
-            The target values (class labels) as integers or strings, of shape
-            [n_samples] or [n_samples, n_outputs].
-
-        Returns
-        -------
-        self : object
-        """
-        
-        super().fit(X,y)
-        y_pred = super().predict(X)
-        if self.bctype in ['cdf','raw']:
-            if self.bctype == 'cdf':
-                x_ = np.sort(y_pred)
-                y_ = np.sort(y)
-            elif self.bctype == 'raw':
-                x_ = y_pred
-                y_ = y
-            self.p = _polyfit_no_inter(x_,y_,self.degree)
-        elif self.bctype == 'spline':
-            x_ = np.sort(y_pred)
-            y_ = np.sort(y)
-            _,idx = np.unique(x_, return_index = True)
-            self.p = UnivariateSpline(x_[idx], y_[idx])
-        else:
-            self.p = 1
-            
-        return 
-    
-    def predict(self, X, round_func = None, bc = True):
-        """
-        Predict regression target for X.
-        The predicted regression target of an input sample is computed as the
-        mean predicted regression targets of the trees in the forest.
-        Parameters
-        ----------
-        X : array-like or sparse matrix of shape (n_samples, n_features)
-            The input samples. Internally, its dtype will be converted to
-            ``dtype=np.float32``. If a sparse matrix is provided, it will be
-            converted into a sparse ``csr_matrix``.
-        round_func : lambda function
-            Optional function to apply to outputs (for example to discretize them
-            using MCH lookup tables). If not provided f(x) = x will be applied
-            (i.e. no function)
-        bc : bool
-            if True the bias correction function will be applied
-            
-        Returns
-        -------
-        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-            The predicted values.
-        """
-        pred = super().predict(X)
-        
-        if round_func == None:
-            round_func = lambda x: x
-        
-        func = lambda x: x
-        if bc:
-            if self.bctype in ['cdf','raw']:
-                func = lambda x : np.polyval(self.p,x)
-            elif self.bctype == 'spline':
-                func = lambda x : self.p(x)
-        out = func(pred)
-        out[out < 0] = 0
-        return round_func(out)
     
 ##################
-
+        
 class MyCustomUnpickler(pickle.Unpickler):
     """
     This is an extension of the pickle Unpickler that handles the 
diff --git a/rainforest/performance/eval_get_estimates.py b/rainforest/performance/eval_get_estimates.py
index 5376dba..6c5c0ec 100644
--- a/rainforest/performance/eval_get_estimates.py
+++ b/rainforest/performance/eval_get_estimates.py
@@ -10,6 +10,8 @@
 import numpy as np
 import pandas as pd
 from pathlib import Path
+import dask
+dask.config.set({'dataframe.query-planning': False})
 import dask.dataframe as dd
 import copy
 import pickle
@@ -435,4 +437,4 @@ def extractEstimatesFromMaps(self, slf_stations=False, tagg_hourly=True, save_ou
                 with open(self.configfile,'a') as yamlfile:
                     yaml.safe_dump({'FILE_60MIN' : save_file_60}, yamlfile)
 
-        return
\ No newline at end of file
+        return
diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py
index 8dcdca2..f4026b2 100644
--- a/rainforest/qpe/qpe.py
+++ b/rainforest/qpe/qpe.py
@@ -3,10 +3,13 @@
 """
 Main function to compute the QPE estimations on the Swiss grid
 
-Daniel Wolfensberger, MeteoSwiss/EPFL, daniel.wolfensberger@meteoswiss.ch
-Rebecca Gugerli, EPFL/ MeteoSwiss
-March 2023
+Daniel Wolfensberger
+MeteoSwiss/EPFL
+daniel.wolfensberger@epfl.ch
+December 2019
 
+Modified by D. Wolfensberger and R. Gugerli
+December 2022
 """
 
 
@@ -20,8 +23,6 @@
 from pathlib import Path
 import os
 
-import h5py
-
 from pathlib import Path
 from scipy.ndimage import gaussian_filter
 from scipy.signal import convolve2d
@@ -31,7 +32,7 @@
 from pyart.aux_io.odim_h5_writer import write_odim_grid_h5
 from pyart.aux_io.odim_h5 import proj4_to_dict
 
-
+from ..common.utils import get_version
 from ..common.logger import logger
 from ..common import constants
 from ..common.retrieve_data import retrieve_prod, get_COSMO_T, retrieve_hzt_prod, retrieve_prod_RT, retrieve_hzt_RT
@@ -135,7 +136,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files):
     grid.time['units'] = 'seconds since {:s}'.format(
                     datetime.datetime.strftime(time_start,
                                                '%Y-%m-%dT%H:%M:%SZ'))
-    grid.time['data'] = np.arange(0, 5 *60)
+    grid.time['data'] = np.arange(0, 5 *60 + 1)
     grid.origin_latitude['data'] = 46.9524
     grid.origin_longitude['data'] = 7.43958333
     grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\
@@ -158,6 +159,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files):
     
     grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)'
     grid.metadata['version'] = b'H5rad 2.3'
+    grid.metadata['sw_version'] = get_version()
     # Add missing radar information
     quality = 'ADLPW'
     if len(missing_files) != 0:
@@ -205,7 +207,7 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2):
     grid.time['units'] = 'seconds since {:s}'.format(
                     datetime.datetime.strftime(time_start,
                                                '%Y-%m-%dT%H:%M:%SZ'))
-    grid.time['data'] = np.arange(0, 5 *60)
+    grid.time['data'] = np.arange(0, 5 *60 + 1)
     grid.origin_latitude['data'] = 46.9524
     grid.origin_longitude['data'] = 7.43958333
     grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\
@@ -224,6 +226,7 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2):
     grid.fields['radar_estimated_rain_rate']['prodname'] = 'CHRFO'
     grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)'
     grid.metadata['version'] = b'H5rad 2.3'
+    grid.metadata['sw_version'] = get_version()
     # Add missing radar information
     quality = 'ADLPW'
     if len(missing_files) != 0:
@@ -233,7 +236,17 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2):
             qual_new = qual_new.replace(rad, '-')
         quality = qual_new
     grid.metadata['radar'] = quality.encode()
-    grid.metadata['nodes'] = 'WMO:06661,WMO:06699,WMO:06768,WMO:06726,WMO:06776'
+    
+    if '-' not in quality:
+        grid.metadata['nodes'] = 'WMO:06661,WMO:06699,WMO:06768,WMO:06726,WMO:06776'
+    else:
+        # 06661: Albis; 06699: Dôle; 06768: Lema; 06726: Plaine Morte; 06776: Weissfluh
+        all_wmo = ['WMO:06661','WMO:06699','WMO:06768','WMO:06726','WMO:06776']
+        rad_wmo = []
+        for ir, rad in enumerate(['A', 'D', 'L', 'P', 'W']):
+            if rad in quality:
+                rad_wmo.append(all_wmo[ir])
+        grid.metadata['nodes'] = ','.join(rad_wmo)
     
     return grid
 
@@ -420,8 +433,9 @@ def fetch_data(self, t0, t1 = None):
 
                 self.radar_files[rad] = split_by_time(radfiles)
                 self.status_files[rad] = split_by_time(statfiles)
-            except:
-                logger.error('Failed to retrieve data for radar {:s}'.format(rad))
+            except Exception as error:
+                error_msg = 'Failed to retrieve data for radar {:s}.\n'.format(rad)+ str(error)
+                logger.error(error_msg)
                 
         # Retrieve iso0 height files
         if 'ISO0_HEIGHT' in self.cosmo_var:
@@ -532,7 +546,7 @@ def save_features(self, features, features_labels, t, filepath):
 
         
     def compute(self, output_folder, t0, t1, timestep = 5,
-                basename = 'RFO%y%j%H%MVH', test_mode = False, ensembles = False):
+                basename = 'RFO%y%j%H%MVH', test_mode = False):
         """
         Computes QPE values for a given time range and stores them in a
         folder, in a binary format
@@ -646,8 +660,8 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                             logger.info('HZT fields for timestep {} missing, creating empty one'.format(t.strftime('%Y%m%d%H%M')))
 
             """Part one - compute radar variables and mask"""
-            logger.info('Computing radar variables and masks')
             # Begin compilation of radarobject
+            logger.info('Preparing all polar radar data')
             radobjects = {}
             for rad in self.config['RADARS']:
                 # if radar does not exist, add to missing file list
@@ -662,12 +676,28 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                 
                 # Read raw radar file and create a RADAR object
                 radobjects[rad] = Radar(rad, self.radar_files[rad][t],
-                                        self.status_files[rad][t])
+                                        self.status_files[rad][t],
+                                        metranet_reader='C')
+                # try:
+                #     radobjects[rad] = Radar(rad, self.radar_files[rad][t],
+                #                             self.status_files[rad][t],
+                #                             metranet_reader='C')
+                # except:
+                #     try:
+                #         wrnmsg = 'Could not read polar radar data for radar {:s}'.format(rad)+\
+                #                 ' with c-reader (default), trying with python-reader'
+                #         logger.warning(wrnmsg)
+                #         radobjects[rad] = Radar(rad, self.radar_files[rad][t],
+                #             self.status_files[rad][t],
+                #             metranet_reader='python')
+                #     except:
+                #         logger.error('Could not read polar radar data of {:s}'.format(rad))
+                        
                 
                 # if problem with radar file, exclude it and add to missing files list
                 if len(radobjects[rad].radarfields) == 0:
                     self.missing_files[rad] = t
-                    logger.info('Removing timestep {:s} of radar {:s}'.format(str(t), rad))
+                    logger.warning('Removing timestep {:s} of radar {:s}'.format(str(t), rad))
                     continue
                 
                 # Process the radar data
@@ -678,8 +708,8 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                     radobjects[rad].snr_mask(self.config['SNR_THRESHOLD'])
                 except Exception as e:
                     self.missing_files[rad] = t
-                    logger.info(e)
-                    logger.info('Removing timestep {:s} of radar {:s}'.format(str(t), rad))
+                    logger.warning(e)
+                    logger.warning('Removing timestep {:s} of radar {:s}'.format(str(t), rad))
                     continue
                 radobjects[rad].compute_kdp(self.config['KDP_PARAMETERS'])
 
@@ -720,9 +750,11 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                 except:
                     logger.error('No cleanup was defined, unzipped files remain in temp-folder')
             
+            logger.info('Processing all sweeps of all radars')
             for sweep in self.config['SWEEPS']: # Loop on sweeps
-                logger.info('---')
-                logger.info('Processing sweep ' + str(sweep))
+                if not self.rt:
+                    logger.info('---')
+                    logger.info('Processing sweep ' + str(sweep))
 
                 for rad in self.config['RADARS']: # Loop on radars, A,D,L,P,W                    
                     # If there is no radar file for the specific radar, continue to next radar
@@ -733,7 +765,8 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                         logger.info('Processing sweep {} of {} - no data for this timestep!'.format(sweep, rad))
                         continue
                     else:
-                        logger.info('Processing radar ' + str(rad))
+                        if not self.rt:
+                            logger.info('Processing radar ' + str(rad) + '; sweep '+str(sweep))
                         
                     try:
                         """Part two - retrieve radar data at every sweep"""
@@ -814,14 +847,16 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                                 W * isvalidzh_radsweep)),2)
 
                     except:
-                        logger.error('Could not compute sweep {:d}'.format(sweep))
                         raise
+                        logger.error('Could not compute sweep {:d}'.format(sweep))
+                        pass
+
 
             """Part four - RF prediction"""
+            logger.info('Applying RF model to retrieve predictions')
             # Get QPE estimate
             # X: current time step; X_prev: previous timestep (t-5min)
             for k in self.models.keys():
-                logger.info('Calculating prediction for model {}'.format(k))
                 model = self.models[k]
                 X = []
                 for v in model.variables:
@@ -857,64 +892,18 @@ def compute(self, output_folder, t0, t1, timestep = 5,
 
                 # Convert radar data to precipitation estimates
                 qpe = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel()
-
-                if len(self.config['QUANTILES'][k]) == 0: 
-                    try:
-                        qpe[validrows] = self.models[k].predict(Xcomb[validrows,:])
-                        ensemble_qpe = np.zeros((NBINS_X, NBINS_Y, self.models[k].n_estimators))
-                        for tree in range(self.models[k].n_estimators):
-                            qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel()
-                            qpe_temp[validrows] = self.models[k].estimators_[tree].predict(Xcomb[validrows,:])
-                            ensemble_qpe[:,:,tree] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y))
-                    except:
-                        logger.error('Model failed!')
-                        raise
-                else:
-                    try:
-                        # This will render a tuple with the first element as a traditional
-                        # Random Forest estimation, and second element a 2D array with all
-                        # the quantile estimates
-                        timelog0 = datetime.datetime.now()
-                        qpe_all = self.models[k].predict(Xcomb[validrows,:],
-                                                quantiles = self.config['QUANTILES'][k])
-                        logger.info('Quantile prediction took {} '.format(datetime.datetime.now()-timelog0))
-                        qpe[validrows] = qpe_all[0]
-
-                        quantile_qpe = np.zeros((NBINS_X, NBINS_Y, len(self.config['QUANTILES'][k])))
-                        for iq, q in enumerate(self.config['QUANTILES'][k]):
-                            qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel()
-                            qpe_temp[validrows] = qpe_all[1][:,iq]
-                            quantile_qpe[:,:,iq] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y))
-                        
-                        if ensembles:
-                            ensemble_qpe = np.zeros((NBINS_X, NBINS_Y, self.models[k].n_estimators))
-                            for tree in range(self.models[k].n_estimators):
-                                qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel()
-                                qpe_temp[validrows] = self.models[k].estimators_[tree].predict(Xcomb[validrows,:])
-                                ensemble_qpe[:,:,tree] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y))    
-                    except:
-                        logger.error('Model failed!')
-                        raise
+                try:
+                    qpe[validrows] = self.models[k].predict(Xcomb[validrows,:])
+                except:
+                    raise
+                    logger.error('Model failed!')
+                    pass
                 
-                if ensembles:
-                    filepath = output_folder + '/' + k+'_ENS'
-                    if not os.path.exists(filepath):
-                        os.mkdir(filepath)                
-                    filepath += '/' + tstr
-                    f = h5py.File(filepath, 'w')
-                    f.create_dataset('RFO_ENS', data=ensemble_qpe)
-                    f.close()
-                    logger.info('Continuing with next timestep as we dont apply a temporal integration')
-                    continue
-
-                # Traditional QPE output
                 qpe = np.reshape(qpe, (NBINS_X, NBINS_Y))
-
                 if self.config['SAVE_NON_POSTPROCESSED_DATA']:
                     qpe_no_temp = qpe.copy()
                     
                 """Temporal disaggregation; Rescale qpe through rproxy"""
-                logger.info('Temporal disaggregation from 10-min to 5min')
                 idx_zh = np.where(np.array(model.variables)
                                 == 'zh_VISIB')[0][0]
 
@@ -960,6 +949,7 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                         logger.error("Pysteps is not available, no qpe disaggregation will be performed!")
                     qpe_ac = _disaggregate(comp)
                     
+                    
                     filepath = output_folder + '/' + k +'_AC/'
                     if not os.path.exists(filepath):
                         os.mkdir(filepath)
@@ -971,24 +961,7 @@ def compute(self, output_folder, t0, t1, timestep = 5,
                     os.mkdir(filepath)                
                 filepath += '/' + tstr
                 self.save_output(qpe, t, filepath)
-
-                if ensembles:
-                    filepath = output_folder + '/' + k+'_ENS'
-                    if not os.path.exists(filepath):
-                        os.mkdir(filepath)                
-                    filepath += '/' + tstr
-                    f = h5py.File(filepath, 'w')
-                    f.create_dataset('RFO_ENS', data=ensemble_qpe)
-                    f.close()
-
-                if len(self.config['QUANTILES'][k]) != 0 :
-                    for iq, qu in enumerate(self.config['QUANTILES'][k]):
-                        filepath = output_folder + '/' + k +'_Q{}/'.format(str(qu).replace('.','_'))
-                        if not os.path.exists(filepath):
-                            os.mkdir(filepath)
-                        self.save_output(quantile_qpe[:,:,iq], t, filepath+tstr)  
-
-
+                
                 if self.config['SAVE_NON_POSTPROCESSED_DATA']:
                     # Coding of folders (0 not applied, 1 applied)
                     # T: temporal disaggregation
diff --git a/requirements.txt b/requirements.txt
index 123b557..40861fb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,4 +17,3 @@ h5py
 pysteps
 xlrd
 pyproj
-doubt
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 8b9d14d..3b176be 100644
--- a/setup.py
+++ b/setup.py
@@ -32,7 +32,7 @@
 s = setup(name        = "rainforest_mch",
     description = "RandomForest QPE python library",
     long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields",
-    version     = "1.4.0",
+    version     = "1.4.4",
     author = 'Rebecca Gugerli, Daniel Wolfensberger',
     author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch',
     license = 'GPL-3.0',
diff --git a/tests_ci/qpe/test_qpe.py b/tests_ci/qpe/test_qpe.py
index 73a158b..d3442f8 100644
--- a/tests_ci/qpe/test_qpe.py
+++ b/tests_ci/qpe/test_qpe.py
@@ -4,6 +4,7 @@
 import datetime
 import numpy as np
 import shutil
+import pyart
 
 from rainforest.common.object_storage import ObjectStorage
 from rainforest.qpe import QPEProcessor
@@ -32,11 +33,17 @@ def test_qpe():
         print(str(Path(cwd, 'test_config.yml')))        
         qpeproc = QPEProcessor(str(Path(cwd, 'test_config.yml')), models)
         qpeproc.compute(cwd, t0,t1, basename = '{}{}'.format(names[model], tstr), test_mode = True)
-        qpe = read_cart(str(Path(cwd, model, datetime.datetime.strftime(t1, '{}{}.h5'.format(names[model], tstr)))))
-        qpe_field = qpe.data
+        qpe = pyart.aux_io.read_odim_grid_h5(str(Path(cwd, model, datetime.datetime.strftime(t1,
+            '{}{}.h5'.format(names[model], tstr)))))
 
         # Assertions
-        assert qpe_field.shape == (640, 710) or qpe_field.shape == (1,640, 710)
-        assert len(np.unique(qpe_field)) > 2
+        # Data
+        assert 'radar_estimated_rain_rate' in qpe.fields
+        assert qpe.fields['radar_estimated_rain_rate']['data'].shape == (1,640, 710)
+        assert len(np.unique(qpe.fields['radar_estimated_rain_rate']['data'])) > 2
+
+        # Time
+        assert qpe.time['units'] == 'seconds since 2022-09-28T05:05:00Z'
+        assert qpe.time['data'] == [0, 300]
 
         shutil.rmtree(str(Path(cwd, model)))
diff --git a/tests_cscs/summary.txt b/tests_cscs/summary.txt
index 8b0e609..b9894c1 100644
--- a/tests_cscs/summary.txt
+++ b/tests_cscs/summary.txt
@@ -6,3 +6,4 @@
 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt
 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt
 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt
+20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt