diff --git a/CustomModel.py b/CustomModel.py index aa0742b..9f42ec5 100644 --- a/CustomModel.py +++ b/CustomModel.py @@ -28,7 +28,7 @@ def Model(inits, simstep, totpop, theta=0, npass=0, bi={}, bp={}, values=(), mod """ ##### Get state variables' current values - if simstep == 1: # get initial values + if simstep == 0: # get initial values E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: # get last value E, I, S = inits diff --git a/Epigrass/epimodels.c b/Epigrass/epimodels.c index 3e89eb2..f465ee7 100644 --- a/Epigrass/epimodels.c +++ b/Epigrass/epimodels.c @@ -1033,27 +1033,27 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); #define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) #endif -/* GetItemInt.proto */ -#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ - __Pyx_GetItemInt_Generic(o, to_py_func(i)))) -#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck); +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif PY_MAJOR_VERSION < 3 + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_str(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_str(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif /* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); @@ -1090,6 +1090,28 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { /* BytesEquals.proto */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); @@ -1103,17 +1125,6 @@ static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* s return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); } -/* DictGetItem.proto */ -#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY -static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); -#define __Pyx_PyObject_Dict_GetItem(obj, name)\ - (likely(PyDict_CheckExact(obj)) ?\ - __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) -#else -#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) -#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) -#endif - /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); @@ -1359,7 +1370,7 @@ static const char __pyx_k_S3[] = "S3"; static const char __pyx_k_S4[] = "S4"; static const char __pyx_k_bi[] = "bi"; static const char __pyx_k_bp[] = "bp"; -static const char __pyx_k_ts[] = "{}:ts"; +static const char __pyx_k_ts[] = ":ts"; static const char __pyx_k_Ic1[] = "Ic1"; static const char __pyx_k_Ic2[] = "Ic2"; static const char __pyx_k_Ic3[] = "Ic3"; @@ -1415,6 +1426,7 @@ static const char __pyx_k_self[] = "self"; static const char __pyx_k_step[] = "step"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_totc[] = "totc"; +static const char __pyx_k_ts_2[] = "{}:ts"; static const char __pyx_k_utf8[] = "utf8"; static const char __pyx_k_E1pos[] = "E1pos"; static const char __pyx_k_E2pos[] = "E2pos"; @@ -1434,7 +1446,7 @@ static const char __pyx_k_SIR_s[] = "SIR_s"; static const char __pyx_k_SIS_s[] = "SIS_s"; static const char __pyx_k_alpha[] = "alpha"; static const char __pyx_k_delta[] = "delta"; -static const char __pyx_k_inits[] = "{}:inits"; +static const char __pyx_k_inits[] = "inits"; static const char __pyx_k_items[] = "items"; static const char __pyx_k_model[] = "model"; static const char __pyx_k_npass[] = "npass"; @@ -1466,12 +1478,12 @@ static const char __pyx_k_author[] = "__author__"; static const char __pyx_k_format[] = "format"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_kwargs[] = "kwargs"; +static const char __pyx_k_lindex[] = "lindex"; static const char __pyx_k_lrange[] = "lrange"; static const char __pyx_k_migInf[] = "{}:migInf"; static const char __pyx_k_module[] = "__module__"; static const char __pyx_k_negbin[] = "negbin"; static const char __pyx_k_object[] = "object"; -static const char __pyx_k_sinits[] = "sinits"; static const char __pyx_k_totpop[] = "totpop"; static const char __pyx_k_vaccov[] = "vaccov"; 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PyObject *__pyx_n_s_inits; static PyObject *__pyx_n_s_items; static PyObject *__pyx_n_s_k; static PyObject *__pyx_n_s_kwargs; +static PyObject *__pyx_n_s_lindex; static PyObject *__pyx_n_s_lrange; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_metaclass; @@ -1792,6 +1804,7 @@ static PyObject *__pyx_kp_s_npass_2; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_n_s_numpy_random; static PyObject *__pyx_n_s_object; +static PyObject *__pyx_n_b_p; static PyObject *__pyx_n_s_p; static PyObject *__pyx_n_s_parallel; static PyObject *__pyx_n_b_pc1; @@ -1822,12 +1835,10 @@ static PyObject *__pyx_n_s_res; static PyObject *__pyx_n_s_results; static PyObject *__pyx_n_s_rpush; static PyObject *__pyx_n_b_s; -static PyObject *__pyx_n_s_s; static PyObject *__pyx_n_s_selectModel; static PyObject *__pyx_n_s_self; static PyObject *__pyx_n_s_set; static PyObject *__pyx_n_s_simstep; -static PyObject *__pyx_n_s_sinits; static PyObject *__pyx_n_s_state; static PyObject *__pyx_n_s_step; 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get initial values * S1, E1, Is1, Ic1, Ig1 = ( - * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) # <<<<<<<<<<<<<< + * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) # <<<<<<<<<<<<<< * S2, E2, Is2, Ic2, Ig2 = ( - * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) + * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) */ __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_v_bi, __pyx_n_b_susc_age1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 183, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); @@ -5376,11 +5274,11 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * __pyx_t_6 = 0; /* "Epigrass/epimodels.py":185 - * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) + * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) * S2, E2, Is2, Ic2, Ig2 = ( - * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) # <<<<<<<<<<<<<< + * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) # <<<<<<<<<<<<<< * S3, E3, Is3, Ic3, Ig3 = ( - * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) + * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) */ __pyx_t_6 = __Pyx_PyObject_GetItem(__pyx_v_bi, __pyx_n_b_susc_age2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 185, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_6); @@ -5404,11 +5302,11 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * __pyx_t_2 = 0; /* "Epigrass/epimodels.py":187 - * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) + * bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) * S3, E3, Is3, Ic3, Ig3 = ( - * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) # <<<<<<<<<<<<<< + * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) # <<<<<<<<<<<<<< * S4, E4, Is4, Ic4, Ig4 = ( - * bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) + * bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) */ __pyx_t_2 = __Pyx_PyObject_GetItem(__pyx_v_bi, __pyx_n_b_susc_age3); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 187, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); @@ -5432,9 +5330,9 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * __pyx_t_6 = 0; /* "Epigrass/epimodels.py":189 - * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) + * bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) * S4, E4, Is4, Ic4, Ig4 = ( - * bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) # <<<<<<<<<<<<<< + * bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) # <<<<<<<<<<<<<< * else: # get values from last time step * # print(len(inits)) */ @@ -5462,9 +5360,9 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * /* "Epigrass/epimodels.py":181 * 'Susc_age3', 'Incub_age3', 'Subc_age3', 'Sympt_age3', 'Comp_age3', * 'Susc_age4', 'Incub_age4', 'Subc_age4', 'Sympt_age4', 'Comp_age4',) - * if simstep == 1: # get initial values # <<<<<<<<<<<<<< + * if simstep == 0: # get initial values # <<<<<<<<<<<<<< * S1, E1, Is1, Ic1, Ig1 = ( - * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) + * bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) */ goto __pyx_L3; } @@ -5834,8 +5732,8 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * * # Vacination event * * if 'vaccineNow' in bp: # TODO: add to bp when creating model # <<<<<<<<<<<<<< - * vaccineNow = bp['vaccineNow'] - * vaccov = bp['vaccov'] + * vaccineNow = bp[b'vaccineNow'] + * vaccov = bp[b'vaccov'] */ __pyx_t_1 = (__Pyx_PySequence_ContainsTF(__pyx_n_s_vaccineNow, __pyx_v_bp, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(0, 216, __pyx_L1_error) __pyx_t_25 = (__pyx_t_1 != 0); @@ -5844,30 +5742,30 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * /* "Epigrass/epimodels.py":217 * * if 'vaccineNow' in bp: # TODO: add to bp when creating model - * vaccineNow = bp['vaccineNow'] # <<<<<<<<<<<<<< - * vaccov = bp['vaccov'] + * vaccineNow = bp[b'vaccineNow'] # <<<<<<<<<<<<<< + * vaccov = bp[b'vaccov'] * S1 -= vaccov * S1 */ - __pyx_t_21 = __Pyx_PyObject_Dict_GetItem(__pyx_v_bp, __pyx_n_s_vaccineNow); if (unlikely(!__pyx_t_21)) __PYX_ERR(0, 217, __pyx_L1_error) + __pyx_t_21 = __Pyx_PyObject_GetItem(__pyx_v_bp, 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S3 -= vaccov * S3 @@ -5881,7 +5779,7 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * __pyx_t_20 = 0; /* "Epigrass/epimodels.py":220 - * vaccov = bp['vaccov'] + * vaccov = bp[b'vaccov'] * S1 -= vaccov * S1 * S2 -= vaccov * S2 # <<<<<<<<<<<<<< * S3 -= vaccov * S3 @@ -5929,8 +5827,8 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_2stepFlu(CYTHON_UNUSED PyObject * * # Vacination event * * if 'vaccineNow' in bp: # TODO: add to bp when creating model # <<<<<<<<<<<<<< - * vaccineNow = bp['vaccineNow'] - * vaccov = bp['vaccov'] + * vaccineNow = bp[b'vaccineNow'] + * vaccov = bp[b'vaccov'] */ } @@ -6957,7 +6855,7 @@ static PyObject *__pyx_pw_8Epigrass_9epimodels_5stepSIS(PyObject *__pyx_self, Py __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("stepSIS (wrapper)", 0); { - static PyObject **__pyx_pyargnames[] = 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r = bp['r']; + * beta = bp[b'beta']; # <<<<<<<<<<<<<< + * alpha = bp[b'alpha']; + * r = bp[b'r']; */ - __pyx_t_7 = __Pyx_PyObject_Dict_GetItem(__pyx_v_bp, __pyx_n_s_beta); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 297, __pyx_L1_error) + __pyx_t_7 = __Pyx_PyObject_GetItem(__pyx_v_bp, __pyx_n_b_beta); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 297, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_7); __pyx_t_3 = __pyx_PyFloat_AsDouble(__pyx_t_7); if (unlikely((__pyx_t_3 == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 297, __pyx_L1_error) __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0; @@ -7266,45 +7164,45 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_4stepSIS(CYTHON_UNUSED PyObject * /* "Epigrass/epimodels.py":298 * - * beta = bp['beta']; - * alpha = bp['alpha']; # <<<<<<<<<<<<<< - * r = bp['r']; - * b = bp['b'] + * beta = bp[b'beta']; + * alpha = bp[b'alpha']; # <<<<<<<<<<<<<< + * r = bp[b'r']; + * b = bp[b'b'] */ - __pyx_t_7 = __Pyx_PyObject_Dict_GetItem(__pyx_v_bp, __pyx_n_s_alpha); if 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+ npass)) ** alpha # Number of new cases # <<<<<<<<<<<<<< * # Model @@ -7439,7 +7337,7 @@ static PyObject *__pyx_pw_8Epigrass_9epimodels_7stepSIS_s(PyObject *__pyx_self, __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("stepSIS_s (wrapper)", 0); { - static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_inits_2,&__pyx_n_s_simstep,&__pyx_n_s_totpop,&__pyx_n_s_theta,&__pyx_n_s_npass,&__pyx_n_s_bi,&__pyx_n_s_bp,&__pyx_n_s_values,&__pyx_n_s_model,&__pyx_n_s_dist,0}; + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_inits,&__pyx_n_s_simstep,&__pyx_n_s_totpop,&__pyx_n_s_theta,&__pyx_n_s_npass,&__pyx_n_s_bi,&__pyx_n_s_bp,&__pyx_n_s_values,&__pyx_n_s_model,&__pyx_n_s_dist,0}; PyObject* values[10] = {0,0,0,0,0,0,0,0,0,0}; values[5] = ((PyObject *)((PyObject *)Py_None)); values[6] = ((PyObject *)((PyObject *)Py_None)); @@ -7476,7 +7374,7 @@ static PyObject *__pyx_pw_8Epigrass_9epimodels_7stepSIS_s(PyObject *__pyx_self, kw_args = PyDict_Size(__pyx_kwds); switch (pos_args) { case 0: - if 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inits = (E,I,S)\n - theta = infectious individuals from neighbor sites\n "; +static char __pyx_doc_8Epigrass_9epimodels_8stepSIR[] = "\n calculates the model SIR, and return its values (no demographics)\n - inits = current state (E,I,S)\n - theta = infectious individuals from neighbor sites\n "; static PyMethodDef __pyx_mdef_8Epigrass_9epimodels_9stepSIR = {"stepSIR", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_8Epigrass_9epimodels_9stepSIR, METH_VARARGS|METH_KEYWORDS, __pyx_doc_8Epigrass_9epimodels_8stepSIR}; static PyObject *__pyx_pw_8Epigrass_9epimodels_9stepSIR(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_inits = 0; @@ -8090,7 +7988,7 @@ static PyObject *__pyx_pw_8Epigrass_9epimodels_9stepSIR(PyObject *__pyx_self, Py __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("stepSIR (wrapper)", 0); { - static PyObject **__pyx_pyargnames[] = 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*__pyx_pf_8Epigrass_9epimodels_8stepSIR(CYTHON_UNUSED PyObject * /* "Epigrass/epimodels.py":365 * """ * # print(inits) - * if simstep == 1: # get initial values # <<<<<<<<<<<<<< + * if simstep == 0: # get initial values # <<<<<<<<<<<<<< * E, I, S = (bi.get(b'e', 0), bi[b'i'], bi[b's']) * else: */ - __pyx_t_1 = ((__pyx_v_simstep == 1) != 0); + __pyx_t_1 = ((__pyx_v_simstep == 0) != 0); if (__pyx_t_1) { /* "Epigrass/epimodels.py":366 * # print(inits) - * if simstep == 1: # get initial values + * if simstep == 0: # get initial values * E, I, S = (bi.get(b'e', 0), bi[b'i'], bi[b's']) # <<<<<<<<<<<<<< * else: * E, I, S = inits @@ -8301,7 +8199,7 @@ static PyObject *__pyx_pf_8Epigrass_9epimodels_8stepSIR(CYTHON_UNUSED PyObject * /* "Epigrass/epimodels.py":365 * """ * # print(inits) - * if simstep == 1: # get initial values # <<<<<<<<<<<<<< + * if simstep == 0: # get initial values # <<<<<<<<<<<<<< * E, I, S = (bi.get(b'e', 0), bi[b'i'], bi[b's']) * else: */ @@ -8393,7 +8291,7 @@ static 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__pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_k, __pyx_n_s_v, __pyx_n_s_k, __pyx_n_s_v, __pyx_n_s_i); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(0, 67, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); - __pyx_codeobj__10 = (PyObject*)__Pyx_PyCode_New(1, 0, 15, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__9, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_get_args_from_redis, 67, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__10)) __PYX_ERR(0, 67, __pyx_L1_error) + __pyx_codeobj__10 = (PyObject*)__Pyx_PyCode_New(1, 0, 14, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__9, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_get_args_from_redis, 67, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__10)) __PYX_ERR(0, 67, __pyx_L1_error) /* "Epigrass/epimodels.py":85 * return inits, simstep, totpop, theta, npass, bi, bp, values @@ -18998,7 +18900,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Flu model with classes S,E,I subclinical, I mild, I medium, I serious, deaths */ - __pyx_tuple__15 = PyTuple_Pack(85, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_vnames, __pyx_n_s_S1, __pyx_n_s_E1, __pyx_n_s_Is1, __pyx_n_s_Ic1, __pyx_n_s_Ig1, __pyx_n_s_S2, __pyx_n_s_E2, __pyx_n_s_Is2, __pyx_n_s_Ic2, __pyx_n_s_Ig2, __pyx_n_s_S3, __pyx_n_s_E3, __pyx_n_s_Is3, __pyx_n_s_Ic3, __pyx_n_s_Ig3, __pyx_n_s_S4, __pyx_n_s_E4, __pyx_n_s_Is4, __pyx_n_s_Ic4, __pyx_n_s_Ig4, __pyx_n_s_e, __pyx_n_s_c, __pyx_n_s_g, __pyx_n_s_d, __pyx_n_s_pc1, __pyx_n_s_pc2, __pyx_n_s_pc3, __pyx_n_s_pc4, __pyx_n_s_pp1, __pyx_n_s_pp2, __pyx_n_s_pp3, __pyx_n_s_pp4, __pyx_n_s_vaccineNow, __pyx_n_s_vaccov, __pyx_n_s_Infectantes, __pyx_n_s_L1pos, __pyx_n_s_L2pos, __pyx_n_s_L3pos, __pyx_n_s_L4pos, __pyx_n_s_E1pos, __pyx_n_s_Is1pos, __pyx_n_s_Ic1pos, __pyx_n_s_Ig1pos, __pyx_n_s_S1pos, __pyx_n_s_E2pos, __pyx_n_s_Is2pos, __pyx_n_s_Ic2pos, __pyx_n_s_Ig2pos, __pyx_n_s_S2pos, __pyx_n_s_E3pos, __pyx_n_s_Is3pos, __pyx_n_s_Ic3pos, __pyx_n_s_Ig3pos, __pyx_n_s_S3pos, __pyx_n_s_E4pos, __pyx_n_s_Is4pos, __pyx_n_s_Ic4pos, __pyx_n_s_Ig4pos, __pyx_n_s_S4pos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(0, 172, __pyx_L1_error) + __pyx_tuple__15 = PyTuple_Pack(85, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_vnames, __pyx_n_s_S1, __pyx_n_s_E1, __pyx_n_s_Is1, __pyx_n_s_Ic1, __pyx_n_s_Ig1, __pyx_n_s_S2, __pyx_n_s_E2, __pyx_n_s_Is2, __pyx_n_s_Ic2, __pyx_n_s_Ig2, __pyx_n_s_S3, __pyx_n_s_E3, __pyx_n_s_Is3, __pyx_n_s_Ic3, __pyx_n_s_Ig3, __pyx_n_s_S4, __pyx_n_s_E4, __pyx_n_s_Is4, __pyx_n_s_Ic4, __pyx_n_s_Ig4, __pyx_n_s_e, __pyx_n_s_c, __pyx_n_s_g, __pyx_n_s_d, __pyx_n_s_pc1, __pyx_n_s_pc2, __pyx_n_s_pc3, __pyx_n_s_pc4, __pyx_n_s_pp1, __pyx_n_s_pp2, __pyx_n_s_pp3, __pyx_n_s_pp4, __pyx_n_s_vaccineNow, __pyx_n_s_vaccov, __pyx_n_s_Infectantes, __pyx_n_s_L1pos, __pyx_n_s_L2pos, __pyx_n_s_L3pos, __pyx_n_s_L4pos, __pyx_n_s_E1pos, __pyx_n_s_Is1pos, __pyx_n_s_Ic1pos, __pyx_n_s_Ig1pos, __pyx_n_s_S1pos, __pyx_n_s_E2pos, __pyx_n_s_Is2pos, __pyx_n_s_Ic2pos, __pyx_n_s_Ig2pos, __pyx_n_s_S2pos, __pyx_n_s_E3pos, __pyx_n_s_Is3pos, __pyx_n_s_Ic3pos, __pyx_n_s_Ig3pos, __pyx_n_s_S3pos, __pyx_n_s_E4pos, __pyx_n_s_Is4pos, __pyx_n_s_Ic4pos, __pyx_n_s_Ig4pos, __pyx_n_s_S4pos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__15)) __PYX_ERR(0, 172, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__15); __Pyx_GIVEREF(__pyx_tuple__15); __pyx_codeobj__16 = (PyObject*)__Pyx_PyCode_New(9, 0, 85, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__15, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepFlu, 172, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__16)) __PYX_ERR(0, 172, __pyx_L1_error) @@ -19010,7 +18912,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SIS, and return its values (no demographics) */ - __pyx_tuple__17 = PyTuple_Pack(25, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__17)) __PYX_ERR(0, 276, __pyx_L1_error) + __pyx_tuple__17 = PyTuple_Pack(25, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__17)) __PYX_ERR(0, 276, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__17); __Pyx_GIVEREF(__pyx_tuple__17); __pyx_codeobj__18 = (PyObject*)__Pyx_PyCode_New(9, 0, 25, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__17, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIS, 276, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__18)) __PYX_ERR(0, 276, __pyx_L1_error) @@ -19022,7 +18924,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SIS: */ - __pyx_tuple__19 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(0, 316, __pyx_L1_error) + __pyx_tuple__19 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(0, 316, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); __pyx_codeobj__20 = (PyObject*)__Pyx_PyCode_New(10, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__19, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIS_s, 316, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__20)) __PYX_ERR(0, 316, __pyx_L1_error) @@ -19034,7 +18936,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SIR, and return its values (no demographics) */ - __pyx_tuple__21 = PyTuple_Pack(25, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(0, 358, __pyx_L1_error) + __pyx_tuple__21 = PyTuple_Pack(25, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(0, 358, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); __pyx_codeobj__22 = (PyObject*)__Pyx_PyCode_New(9, 0, 25, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__21, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIR, 358, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__22)) __PYX_ERR(0, 358, __pyx_L1_error) @@ -19046,7 +18948,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SIR: */ - __pyx_tuple__23 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(0, 395, __pyx_L1_error) + __pyx_tuple__23 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(0, 395, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); __pyx_codeobj__24 = (PyObject*)__Pyx_PyCode_New(10, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__23, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIR_s, 395, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__24)) __PYX_ERR(0, 395, __pyx_L1_error) @@ -19058,7 +18960,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines the model SEIS: */ - __pyx_tuple__25 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(0, 437, __pyx_L1_error) + __pyx_tuple__25 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__25)) __PYX_ERR(0, 437, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__25); __Pyx_GIVEREF(__pyx_tuple__25); __pyx_codeobj__26 = (PyObject*)__Pyx_PyCode_New(9, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__25, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIS, 437, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__26)) __PYX_ERR(0, 437, __pyx_L1_error) @@ -19070,7 +18972,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SEIS: */ - __pyx_tuple__27 = PyTuple_Pack(29, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__27)) __PYX_ERR(0, 473, __pyx_L1_error) + __pyx_tuple__27 = PyTuple_Pack(29, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__27)) __PYX_ERR(0, 473, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__27); __Pyx_GIVEREF(__pyx_tuple__27); __pyx_codeobj__28 = (PyObject*)__Pyx_PyCode_New(10, 0, 29, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__27, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIS_s, 473, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__28)) __PYX_ERR(0, 473, __pyx_L1_error) @@ -19082,7 +18984,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines the model SEIR: */ - __pyx_tuple__29 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__29)) __PYX_ERR(0, 515, __pyx_L1_error) + __pyx_tuple__29 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__29)) __PYX_ERR(0, 515, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__29); __Pyx_GIVEREF(__pyx_tuple__29); __pyx_codeobj__30 = (PyObject*)__Pyx_PyCode_New(9, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__29, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIR, 515, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__30)) __PYX_ERR(0, 515, __pyx_L1_error) @@ -19094,7 +18996,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SEIR: */ - __pyx_tuple__31 = PyTuple_Pack(29, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__31)) __PYX_ERR(0, 553, __pyx_L1_error) + __pyx_tuple__31 = PyTuple_Pack(29, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__31)) __PYX_ERR(0, 553, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__31); __Pyx_GIVEREF(__pyx_tuple__31); __pyx_codeobj__32 = (PyObject*)__Pyx_PyCode_New(10, 0, 29, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__31, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIR_s, 553, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__32)) __PYX_ERR(0, 553, __pyx_L1_error) @@ -19106,7 +19008,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SIpRpS, and return its values (no demographics) */ - __pyx_tuple__33 = PyTuple_Pack(26, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_delta, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__33)) __PYX_ERR(0, 598, __pyx_L1_error) + __pyx_tuple__33 = PyTuple_Pack(26, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_delta, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__33)) __PYX_ERR(0, 598, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__33); __Pyx_GIVEREF(__pyx_tuple__33); __pyx_codeobj__34 = (PyObject*)__Pyx_PyCode_New(9, 0, 26, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__33, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIpRpS, 598, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__34)) __PYX_ERR(0, 598, __pyx_L1_error) @@ -19118,7 +19020,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SIpRpS: */ - __pyx_tuple__35 = PyTuple_Pack(28, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_delta, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__35)) __PYX_ERR(0, 634, __pyx_L1_error) + __pyx_tuple__35 = PyTuple_Pack(28, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_delta, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__35)) __PYX_ERR(0, 634, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__35); __Pyx_GIVEREF(__pyx_tuple__35); __pyx_codeobj__36 = (PyObject*)__Pyx_PyCode_New(10, 0, 28, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__35, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIpRpS_s, 634, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__36)) __PYX_ERR(0, 634, __pyx_L1_error) @@ -19130,7 +19032,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines the model SEIpRpS: */ - __pyx_tuple__37 = PyTuple_Pack(28, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_delta, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__37)) __PYX_ERR(0, 676, __pyx_L1_error) + __pyx_tuple__37 = PyTuple_Pack(28, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_delta, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__37)) __PYX_ERR(0, 676, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__37); __Pyx_GIVEREF(__pyx_tuple__37); __pyx_codeobj__38 = (PyObject*)__Pyx_PyCode_New(9, 0, 28, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__37, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIpRpS, 676, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__38)) __PYX_ERR(0, 676, __pyx_L1_error) @@ -19142,7 +19044,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SEIpRpS: */ - __pyx_tuple__39 = PyTuple_Pack(30, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_delta, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__39)) __PYX_ERR(0, 713, __pyx_L1_error) + __pyx_tuple__39 = PyTuple_Pack(30, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_delta, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__39)) __PYX_ERR(0, 713, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__39); __Pyx_GIVEREF(__pyx_tuple__39); __pyx_codeobj__40 = (PyObject*)__Pyx_PyCode_New(10, 0, 30, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__39, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIpRpS_s, 713, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__40)) __PYX_ERR(0, 713, __pyx_L1_error) @@ -19154,7 +19056,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SIpR, and return its values (no demographics) */ - __pyx_tuple__41 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_p, __pyx_n_s_Lpos2, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__41)) __PYX_ERR(0, 755, __pyx_L1_error) + __pyx_tuple__41 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_p, __pyx_n_s_Lpos2, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__41)) __PYX_ERR(0, 755, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__41); __Pyx_GIVEREF(__pyx_tuple__41); __pyx_codeobj__42 = (PyObject*)__Pyx_PyCode_New(9, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__41, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIpR, 755, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__42)) __PYX_ERR(0, 755, __pyx_L1_error) @@ -19166,7 +19068,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SIpRs: */ - __pyx_tuple__43 = PyTuple_Pack(30, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_p, __pyx_n_s_Lpos2_esp, __pyx_n_s_Lpos2, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__43)) __PYX_ERR(0, 793, __pyx_L1_error) + __pyx_tuple__43 = PyTuple_Pack(30, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_p, __pyx_n_s_Lpos2_esp, __pyx_n_s_Lpos2, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__43)) __PYX_ERR(0, 793, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__43); __Pyx_GIVEREF(__pyx_tuple__43); __pyx_codeobj__44 = (PyObject*)__Pyx_PyCode_New(10, 0, 30, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__43, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIpR_s, 793, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__44)) __PYX_ERR(0, 793, __pyx_L1_error) @@ -19178,7 +19080,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SEIpR, and return its values (no demographics) */ - __pyx_tuple__45 = PyTuple_Pack(29, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_p, __pyx_n_s_Lpos2, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__45)) __PYX_ERR(0, 840, __pyx_L1_error) + __pyx_tuple__45 = PyTuple_Pack(29, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_p, __pyx_n_s_Lpos2, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__45)) __PYX_ERR(0, 840, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__45); __Pyx_GIVEREF(__pyx_tuple__45); __pyx_codeobj__46 = (PyObject*)__Pyx_PyCode_New(9, 0, 29, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__45, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIpR, 840, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__46)) __PYX_ERR(0, 840, __pyx_L1_error) @@ -19190,7 +19092,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SEIpRs: */ - __pyx_tuple__47 = PyTuple_Pack(32, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_p, __pyx_n_s_Lpos2_esp, __pyx_n_s_Lpos2, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__47)) __PYX_ERR(0, 880, __pyx_L1_error) + __pyx_tuple__47 = PyTuple_Pack(32, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_e, __pyx_n_s_p, __pyx_n_s_Lpos2_esp, __pyx_n_s_Lpos2, __pyx_n_s_prob, __pyx_n_s_Epos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__47)) __PYX_ERR(0, 880, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__47); __Pyx_GIVEREF(__pyx_tuple__47); __pyx_codeobj__48 = (PyObject*)__Pyx_PyCode_New(10, 0, 32, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__47, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSEIpR_s, 880, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__48)) __PYX_ERR(0, 880, __pyx_L1_error) @@ -19202,7 +19104,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * calculates the model SIRS, and return its values (no demographics) */ - __pyx_tuple__49 = PyTuple_Pack(25, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__49)) __PYX_ERR(0, 929, __pyx_L1_error) + __pyx_tuple__49 = PyTuple_Pack(25, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__49)) __PYX_ERR(0, 929, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__49); __Pyx_GIVEREF(__pyx_tuple__49); __pyx_codeobj__50 = (PyObject*)__Pyx_PyCode_New(9, 0, 25, 0, CO_OPTIMIZED|CO_NEWLOCALS, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__49, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Epigrass_epimodels_py, __pyx_n_s_stepSIRS, 929, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__50)) __PYX_ERR(0, 929, __pyx_L1_error) @@ -19214,7 +19116,7 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { * """ * Defines an stochastic model SIR: */ - __pyx_tuple__51 = PyTuple_Pack(27, __pyx_n_s_inits_2, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__51)) __PYX_ERR(0, 966, __pyx_L1_error) + __pyx_tuple__51 = PyTuple_Pack(27, __pyx_n_s_inits, __pyx_n_s_simstep, __pyx_n_s_totpop, __pyx_n_s_theta, __pyx_n_s_npass, __pyx_n_s_bi, __pyx_n_s_bp, __pyx_n_s_values, __pyx_n_s_model, __pyx_n_s_dist, __pyx_n_s_beta, __pyx_n_s_alpha, __pyx_n_s_E, __pyx_n_s_I, __pyx_n_s_S, __pyx_n_s_N, __pyx_n_s_r, __pyx_n_s_b, __pyx_n_s_w, __pyx_n_s_Lpos_2, __pyx_n_s_Lpos_esp, __pyx_n_s_R, __pyx_n_s_Ipos, __pyx_n_s_Spos, __pyx_n_s_Rpos, __pyx_n_s_prob, __pyx_n_s_migInf_2); if (unlikely(!__pyx_tuple__51)) __PYX_ERR(0, 966, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__51); __Pyx_GIVEREF(__pyx_tuple__51); __pyx_codeobj__52 = (PyObject*)__Pyx_PyCode_New(10, 0, 27, 0, CO_OPTIMIZED|CO_NEWLOCALS, 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PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - /* RaiseTooManyValuesToUnpack */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, @@ -21666,6 +21481,93 @@ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int eq #endif } +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { @@ -21695,30 +21597,6 @@ static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { } #endif -/* DictGetItem */ -#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY -static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { - PyObject *value; - value = PyDict_GetItemWithError(d, key); - if (unlikely(!value)) { - if (!PyErr_Occurred()) { - if (unlikely(PyTuple_Check(key))) { - PyObject* args = PyTuple_Pack(1, key); - if (likely(args)) { - PyErr_SetObject(PyExc_KeyError, args); - Py_DECREF(args); - } - } else { - PyErr_SetObject(PyExc_KeyError, key); - } - } - return NULL; - } - Py_INCREF(value); - return value; -} -#endif - /* PyIntBinop */ #if !CYTHON_COMPILING_IN_PYPY static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) { diff --git a/Epigrass/epimodels.py b/Epigrass/epimodels.py index 9cd8fe8..1a3c907 100644 --- a/Epigrass/epimodels.py +++ b/Epigrass/epimodels.py @@ -68,9 +68,9 @@ def get_args_from_redis(self): """ Get updated parameters from the redis database. """ - sinits = redisclient.lrange("{}:inits".format(self.geocode), -1, -1) + # sinits = redisclient.lindex("{}:inits".format(self.geocode), -1) # print(sinits) - inits = eval(sinits[0]) + inits = eval(redisclient.lindex(f"{self.geocode}:ts", -1)) simstep = int(redisclient.get("simstep")) totpop = int(float(redisclient.get("{}:totpop".format(self.geocode)))) theta = int(nan_to_num(float(redisclient.get("{}:theta".format(self.geocode))))) @@ -90,7 +90,7 @@ def update_redis(self, results): # Site state state, Lpos, migInf = results # print("Updating redis: ", state, migInf) - redisclient.rpush("{}:inits".format(self.geocode), str(state)) # updating inits + # redisclient.rpush("{}:inits".format(self.geocode), str(state)) # updating inits redisclient.rpush('{}:ts'.format(self.geocode), str(state)) redisclient.set('{}:Lpos'.format(self.geocode), Lpos) totc = int(nan_to_num(float(redisclient.get('{}:totalcases'.format(self.geocode))))) @@ -157,7 +157,7 @@ def selectModel(modtype): # try: # # TODO: move this import to the graph level # import CustomModel - # vnames['Custom'] = CustomModel.vnames + # vnames[b'Custom'] = CustomModel.vnames # return CustomModel.Model # except ImportError: # print("You have to Create a CustomModel.py file before you can select\nthe Custom model type") @@ -178,15 +178,15 @@ def stepFlu(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values=N 'Susc_age2', 'Incub_age2', 'Subc_age2', 'Sympt_age2', 'Comp_age2', 'Susc_age3', 'Incub_age3', 'Subc_age3', 'Sympt_age3', 'Comp_age3', 'Susc_age4', 'Incub_age4', 'Subc_age4', 'Sympt_age4', 'Comp_age4',) - if simstep == 1: # get initial values + if simstep == 0: # get initial values S1, E1, Is1, Ic1, Ig1 = ( - bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) + bi[b'susc_age1'], bi[b'incub_age1'], bi[b'subc_age1'], bi[b'sympt_age1'], bi[b'comp_age1']) S2, E2, Is2, Ic2, Ig2 = ( - bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) + bi[b'susc_age2'], bi[b'incub_age2'], bi[b'subc_age2'], bi[b'sympt_age2'], bi[b'comp_age2']) S3, E3, Is3, Ic3, Ig3 = ( - bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) + bi[b'susc_age3'], bi[b'incub_age3'], bi[b'subc_age3'], bi[b'sympt_age3'], bi[b'comp_age3']) S4, E4, Is4, Ic4, Ig4 = ( - bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) + bi[b'susc_age4'], bi[b'incub_age4'], bi[b'subc_age4'], bi[b'sympt_age4'], bi[b'comp_age4']) else: # get values from last time step # print(len(inits)) S1, E1, Is1, Ic1, Ig1, S2, E2, Is2, Ic2, Ig2, S3, E3, Is3, Ic3, Ig3, S4, E4, Is4, Ic4, Ig4 = inits @@ -214,8 +214,8 @@ def stepFlu(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values=N # Vacination event if 'vaccineNow' in bp: # TODO: add to bp when creating model - vaccineNow = bp['vaccineNow'] - vaccov = bp['vaccov'] + vaccineNow = bp[b'vaccineNow'] + vaccov = bp[b'vaccov'] S1 -= vaccov * S1 S2 -= vaccov * S2 S3 -= vaccov * S3 @@ -288,16 +288,16 @@ def stepSIS(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values=N :param values: tuple of extra values :return: """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - r = bp['r']; - b = bp['b'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + r = bp[b'r']; + b = bp[b'b'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -319,20 +319,20 @@ def stepSIS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -358,23 +358,23 @@ def stepSIS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values def stepSIR(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values=None, model=None): """ calculates the model SIR, and return its values (no demographics) - - inits = (E,I,S) + - inits = current state (E,I,S) - theta = infectious individuals from neighbor sites """ # print(inits) - if simstep == 1: # get initial values + if simstep == 0: # get initial values E, I, S = (bi.get(b'e', 0), bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop beta = bp[b'beta']; alpha = bp[b'alpha']; - # e = bp['e']; + # e = bp[b'e']; r = bp[b'r']; - # delta = bp['delta']; + # delta = bp[b'delta']; b = bp[b'b']; - # w = bp['w']; - # p = bp['p'] + # w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -398,19 +398,19 @@ def stepSIR_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -440,19 +440,19 @@ def stepSEIS(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values= - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -477,19 +477,19 @@ def stepSEIS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, value - par = (Beta, alpha, E,r,delta,B,w,p) see docs. - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -519,19 +519,19 @@ def stepSEIR(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values= - par = (Beta, alpha, E,r,delta,B,w,p) see docs. - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -557,19 +557,19 @@ def stepSEIR_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, value - par = (Beta, alpha, E,r,delta,B,w,p) see docs. - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -601,19 +601,19 @@ def stepSIpRpS(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, value - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -637,19 +637,19 @@ def stepSIpRpS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, val - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -679,19 +679,19 @@ def stepSEIpRpS(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, valu - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases @@ -716,19 +716,19 @@ def stepSEIpRpS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, va - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': @@ -758,20 +758,20 @@ def stepSIpR(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values= - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop R = N - E - I - S - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases Lpos2 = p * float(beta) * R * ((I + theta) / (N + npass)) ** alpha # number of secondary Infections @@ -796,19 +796,19 @@ def stepSIpR_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, value - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + p = bp[b'p'] R = N - E - I - S Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases @@ -843,20 +843,20 @@ def stepSEIpR(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop R = N - E - I - S - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases Lpos2 = p * float(beta) * R * ((I + theta) / (N + npass)) ** alpha # secondary infections @@ -883,19 +883,19 @@ def stepSEIpR_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, valu - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop - beta = bp['beta']; - alpha = bp['alpha']; - e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - # w = bp['w']; - p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + # w = bp[b'w']; + p = bp[b'p'] R = N - E - I - S Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases @@ -932,20 +932,20 @@ def stepSIRS(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, values= - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop R = N - (E + I + S) - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + w = bp[b'w']; + # p = bp[b'p'] Lpos = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases # Model @@ -969,20 +969,20 @@ def stepSIRS_s(inits, simstep, totpop, theta=0, npass=0, bi=None, bp=None, value - inits = (E,I,S) - theta = infectious individuals from neighbor sites """ - if simstep == 1: # get initial values - E, I, S = (bi['e'], bi['i'], bi['s']) + if simstep == 0: # get initial values + E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: E, I, S = inits N = totpop R = N - (E + I + S) - beta = bp['beta']; - alpha = bp['alpha']; - # e = bp['e']; - r = bp['r']; - # delta = bp['delta']; - b = bp['b']; - w = bp['w']; - # p = bp['p'] + beta = bp[b'beta']; + alpha = bp[b'alpha']; + # e = bp[b'e']; + r = bp[b'r']; + # delta = bp[b'delta']; + b = bp[b'b']; + w = bp[b'w']; + # p = bp[b'p'] Lpos_esp = float(beta) * S * ((I + theta) / (N + npass)) ** alpha # Number of new cases if dist == 'poisson': diff --git a/Epigrass/epipanel.py b/Epigrass/epipanel.py index 7953211..c257491 100644 --- a/Epigrass/epipanel.py +++ b/Epigrass/epipanel.py @@ -1,16 +1,20 @@ import pandas as pd import altair as alt +import networkx as NX +import nx_altair as nxa import geopandas as gpd import os import panel as pn import panel.widgets as pnw import hvplot.pandas +import holoviews as hv +from holoviews.operation.datashader import datashade, bundle_graph +from holoviews import opts from bokeh.resources import INLINE import param import base64 from sqlalchemy import create_engine import glob -# import gpdvega from functools import lru_cache enc_icon = b'iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAA3ElEQVRYhe2WWQ7DIAxEH1XvVR+dmzkfVSJngWIW0VYZKV8EZgzDmABARFkhBDxomQs8z+tFvfoxBUG8nHkBgnircAlOwlt5LzxmkN4CbgFfJeB950vSrDHxUihOwtbEKxaQScKxQTUrCU87YJE5jnEeOJJfkdkxNUcTaDCnrbb0OCJRFbavmtySer3QVcAMI/5GEmaN1utNqAIhpjwghm8/Lsg2twbTd8CsU2/AlrnTTbhDbSX/swPgr2ZIeHl6QStX8tqsi79MBqxXMNcpu+LY7Ub0i48VdOv3CSxJ9X3LgJP02QAAAABJRU5ErkJggg==' @@ -41,6 +45,13 @@ def get_sims(fname): get_sims.cache_clear() return [] +def get_graph(pth): + full_path = os.path.join(os.path.abspath(pth), 'network.gml') + if os.path.exists(full_path): + G = NX.read_gml(full_path) + else: + G = NX.MultiDiGraph() + return G @lru_cache(maxsize=10) def get_meta_table(fname, simname): @@ -123,7 +134,7 @@ def update_sims(self): sims = get_sims(self.model_path) self.param['simulation_run'].objects = sims if sims: - self.simulation_run = sims[0] + self.simulation_run = sims[-1] self.df = read_simulation(self.model_path, self.simulation_run) @param.depends('simulation_run', watch=True) @@ -156,7 +167,7 @@ def view_meta(self): else: return pn.pane.Markdown("") - @param.depends('map_selector', 'simulation_run') + @param.depends('model_path', 'map_selector', 'simulation_run') def view_map(self): if self.map_selector and self.mapdf is not None: mapdf = self.mapdf @@ -201,6 +212,38 @@ def view_map(self): else: return pn.indicators.LoadingSpinner(value=True, width=100, height=100) + @param.depends('model_path') + def view_network(self): + G = get_graph(self.model_path) + if G.order == 0: + return pn.pane.Alert(f'## No network file found on {self.model_path}') + + # Draw the graph using Altair + pos = NX.get_node_attributes(G, 'pos') + # print(pos) + # viz = nxa.draw_networkx( + # G, pos=pos, + # node_color='weight', + # cmap='viridis', + # width='weight', + # edge_color='black', + # ) + + kwargs = dict(width=800, height=800, xaxis=None, yaxis=None) + hv.opts.defaults(opts.Nodes(**kwargs), opts.Graph(**kwargs)) + colors = ['#000000'] + hv.Cycle('Category20').values + epi_graph = hv.Graph.from_networkx(G, positions=pos) + + epi_graph.opts(cmap=colors, node_size=10, edge_line_width=1, + directed=True, + node_line_color='gray', + edge_color='gray', + node_color='circle' + ) + + f = epi_graph + return f + @param.depends('localities', 'time_slider', 'simulation_run') def view_series(self): if self.simulation_run is None: @@ -276,8 +319,9 @@ def altair_series(self): return chart -series_viewer = SeriesViewer() # model_path=sm.output()[0], sim_run=sm.output()[1], locality=sm.output()[2]) -button = pnw.Button(name='Refresh', button_type='primary') + + + def refresh(e): # Clear caches get_sims.cache_clear() @@ -289,46 +333,53 @@ def refresh(e): series_viewer.update_sims() series_viewer.update_localities() -button.on_click(refresh) - -# def save(fname='dashboard.html'): -# material.save(fname, resources=INLINE) -# save_button = pnw.Button(name="Save as HTML", button_type='warning') -# save_fname = pnw.TextInput(name="File name", value='Dashboard.html') -# save_button.on_click(save(save_fname.value)) - -# Assembling the panel -material.sidebar.append(pn.Param(series_viewer, name='Control Panel')) -material.sidebar.append(button) -material.sidebar.append(pn.layout.Divider()) -material.sidebar.append(series_viewer.view_meta) -# material.sidebar.append(pn.Row(save_fname, save_button)) - -# material.sidebar.append(pn.widgets.StaticText(sm.simulation_run.path)) -material.main.append( - pn.Column( - pn.Row( - pn.Card(series_viewer.view_map, title='Final State') - ), - pn.Row( - pn.Card(series_viewer.view_series, title='Time Map') - ), - pn.Row( - pn.Card(series_viewer.altair_series, title='Time Series') - ), +def main(): + global series_viewer + series_viewer = SeriesViewer() # model_path=sm.output()[0], sim_run=sm.output()[1], locality=sm.output()[2]) + button = pnw.Button(name='Refresh', button_type='primary') + button.on_click(refresh) + # Assembling the panel + material.sidebar.append(pn.Param(series_viewer, name='Control Panel')) + material.sidebar.append(button) + material.sidebar.append(pn.layout.Divider()) + material.sidebar.append(series_viewer.view_meta) + # material.sidebar.append(pn.Row(save_fname, save_button)) + # material.sidebar.append(pn.widgets.StaticText(sm.simulation_run.path)) + material.main.append( + pn.Column( + pn.Row( + pn.Card(series_viewer.view_map, title='Final State') + ), + # pn.Row( + # pn.Card(series_viewer.view_network, title='Network') + # ), + pn.Row( + pn.Card(series_viewer.view_series, title='Time Map') + ), + pn.Row( + pn.Card(series_viewer.altair_series, title='Time Series') + ), + + ) ) -) + return material, series_viewer + + + + + # material.servable(); def show(pth): + material, series_viewer = main() series_viewer.model_path = pth pn.serve(material, port=5006) if __name__ == "__main__": + material, series_viewer = main() series_viewer.model_path = '../demos/outdata-rio' refresh(None) pn.serve(material, port=5006, threaded=False) - diff --git a/Epigrass/manager.py b/Epigrass/manager.py index ac5cb58..b2282b9 100755 --- a/Epigrass/manager.py +++ b/Epigrass/manager.py @@ -862,7 +862,7 @@ def runGraph(self, graphobj, iterations=1, transp=1): g.maxstep = iterations sites = list(graphobj.site_dict.values()) edges = list(graphobj.edge_dict.values()) - + # redisclient.flushall() if transp: for n in tqdm(range(iterations), desc='Simulation steps'): # print() diff --git a/Epigrass/simobj.py b/Epigrass/simobj.py index 0e96918..2f012e7 100644 --- a/Epigrass/simobj.py +++ b/Epigrass/simobj.py @@ -8,6 +8,7 @@ import time import json import os +import numpy as np from numpy.random import binomial import networkx as NX from networkx.exception import NetworkXNoPath, NetworkXError @@ -171,7 +172,8 @@ def runModel(self, parallel=True): pipe = redisclient.pipeline() pipe.set("simstep", simstep) pipe.set("{}:totpop".format(self.geocode), totpop) - pipe.rpush("{}:inits".format(self.geocode), str(inits)) + # pipe.rpush("{}:inits".format(self.geocode), str(inits)) + pipe.rpush("{}:ts".format(self.geocode), str(inits)) pipe.set("{}:npass".format(self.geocode), float(npass)) pipe.set("{}:theta".format(self.geocode), int(nan_to_num(theta))) pipe.hmset("{}:bi".format(self.geocode), self.bi) @@ -197,10 +199,10 @@ def handle(self, res): Processes the output of a step updating simulation statistics :param res: Tuple with the output of the simulation model """ - pipe = redisclient.pipeline() - last_state, Lpos, migInf = pipe.lindex("{}:ts".format(self.geocode), -1) \ - .get("{}:Lpos".format(self.geocode)) \ - .get("{}:migInf".format(self.geocode)).execute() + # pipe = redisclient.pipeline() + last_state = redisclient.lindex("{}:ts".format(self.geocode), -1) + Lpos = redisclient.lindex(f"{self.geocode}:incidence", -1) + migInf = redisclient.get("{}:migInf".format(self.geocode)) self.ts.append(eval(last_state)) Lpos = float(Lpos) migInf = float(migInf) @@ -510,7 +512,7 @@ def __init__(self, graph_name, digraph=0): self.site_dict = {} # geocode as keys self.edge_dict = {} # geocode tuple as key self.speed = 0 # speed of the transportation system - self.simstep = 1 # current step in the simulation + self.simstep = 0 # current step in the simulation self.maxstep = 100 # maximum number of steps in the simulation self.epipath = [] self.graphdict = {} @@ -1023,12 +1025,11 @@ def save_topology(self, pa): g = NX.MultiDiGraph() for gc, n in self.site_dict.items(): - g.add_node(gc, name=n.sitename) + g.add_node(gc, name=str(n.sitename), pos=n.pos) for ed, e in self.edge_dict.items(): g.add_edge(ed[0], ed[1], weight=e.fmig + e.bmig) # print(g.nodes) # NX.write_graphml_lxml(g, pa) - NX.write_gexf(g, pa) NX.write_gml(g, pa.replace('gexf', 'gml')) nl = json_graph.node_link_data(g) jsonpath = pa.replace('gexf', 'json') diff --git a/demos/CustomModel.py b/demos/CustomModel.py index aa0742b..9f42ec5 100644 --- a/demos/CustomModel.py +++ b/demos/CustomModel.py @@ -28,7 +28,7 @@ def Model(inits, simstep, totpop, theta=0, npass=0, bi={}, bp={}, values=(), mod """ ##### Get state variables' current values - if simstep == 1: # get initial values + if simstep == 0: # get initial values E, I, S = (bi[b'e'], bi[b'i'], bi[b's']) else: # get last value E, I, S = inits diff --git a/demos/CustomModel_example.py b/demos/CustomModel_example.py index 37f9e0d..d78d853 100644 --- a/demos/CustomModel_example.py +++ b/demos/CustomModel_example.py @@ -17,7 +17,7 @@ def Model(inits,simstep, totpop,theta=0, npass=0,bi={},bp={},values=()): """ ##### Get state variables' current values - if simstep == 1: #get initial values + if simstep == 0: #get initial values E,I,S = (bi['e'], bi['i'], bi['s']) else: # get last value E,I,S = inits diff --git a/demos/rio.epg b/demos/rio.epg index 8e1dc40..82a1d0d 100644 --- a/demos/rio.epg +++ b/demos/rio.epg @@ -35,7 +35,7 @@ encoding = #==============================================================# #model types available: SIS, SIS_s ,SIR, SIR_s, SEIS, SEIS_s, SEIR, SEIR_s, # SIpRpS, SIpRpS_s,SIpR,SIpR_s,Influenza or Custom (see documentation for description). -modtype = SIR +modtype = SEIpRpS #==============================================================# [MODEL PARAMETERS] diff --git a/demos/spread.gml b/demos/spread.gml index 384daf2..1156c63 100644 --- a/demos/spread.gml +++ b/demos/spread.gml @@ -9,200 +9,200 @@ graph [ ] node [ id 1 - label "4550602" - name "Santo Cristo 2" - time 2 + label "4550701" + name "Av.Passos" + time 1 ] node [ id 2 - label "4550604" - name "Santo Cristo" - time 2 + label "4550702" + name "Mal.Floriano" + time 1 ] node [ id 3 - label "4550605" - name "Praca Maua" - time 2 + label "4550703" + name "Candelaria" + time 1 ] node [ id 4 - label "4550701" - name "Av.Passos" - time 2 + label "4550704" + name "1o. de marco" + time 1 ] node [ id 5 - label "4550702" - name "Mal.Floriano" - time 2 + label "4550705" + name "Aerop.S.Dumont" + time 1 ] node [ id 6 - label "4550703" - name "Candelaria" - time 2 + label "4550706" + name "Alm. Barroso" + time 1 ] node [ id 7 - label "4550704" - name "1o. de marco" - time 2 + label "4550709" + name "Cruz Vermelha" + time 1 ] node [ id 8 - label "4550705" - name "Aerop.S.Dumont" - time 2 + label "4550804" + name "Rio Comprido 1" + time 1 ] node [ id 9 - label "4550706" - name "Alm. Barroso" - time 2 + label "4550904" + name "Sao Salvador" + time 1 ] node [ id 10 - label "4550707" - name "Passeio Publico" - time 2 + label "4550906" + name "Morro da Viuva" + time 1 ] node [ id 11 - label "4550708" - name "Av. Chile" - time 2 + label "4550908" + name "General Glicerio" + time 1 ] node [ id 12 - label "4550709" - name "Cruz Vermelha" - time 2 + label "4550909" + name "Cosme Velho" + time 1 ] node [ id 13 - label "4550710" - name "Praca da Republica" - time 2 + label "4551001" + name "Leme" + time 1 ] node [ id 14 - label "4550711" - name "Central do Brasil" - time 2 + label "4551105" + name "Morro da Catacumba" + time 1 ] node [ id 15 - label "4550801" - name "Sambodromo" - time 2 + label "4551106" + name "Fonte da Saudade" + time 1 ] node [ id 16 - label "4550802" - name "Piranhao" - time 2 + label "4551107" + name "Parque Lage" + time 1 ] node [ id 17 - label "4550803" - name "Estacio" - time 2 + label "4551108" + name "Hipica" + time 1 ] node [ id 18 - label "4550804" - name "Rio Comprido 1" - time 2 + label "4551114" + name "Vidigal" + time 1 ] node [ id 19 - label "4550805" - name "Rio Comprido 2" - time 2 + label "4551310" + name "Vista Chinesa" + time 1 ] node [ id 20 - label "4550806" - name "Campos da Paz" - time 2 + label "4553301" + name "Rocinha" + time 1 ] node [ id 21 - label "4550904" - name "Sao Salvador" + label "4550604" + name "Santo Cristo" time 2 ] node [ id 22 - label "4550906" - name "Morro da Viuva" + label "4550605" + name "Praca Maua" time 2 ] node [ id 23 - label "4550908" - name "General Glicerio" + label "4550707" + name "Passeio Publico" time 2 ] node [ id 24 - label "4550909" - name "Cosme Velho" + label "4550708" + name "Av. Chile" time 2 ] node [ id 25 - label "4551001" - name "Leme" + label "4550710" + name "Praca da Republica" time 2 ] node [ id 26 - label "4551105" - name "Morro da Catacumba" + label "4550711" + name "Central do Brasil" time 2 ] node [ id 27 - label "4551106" - name "Fonte da Saudade" + label "4550801" + name "Sambodromo" time 2 ] node [ id 28 - label "4551107" - name "Parque Lage" + label "4550802" + name "Piranhao" time 2 ] node [ id 29 - label "4551108" - name "Hipica" + label "4550803" + name "Estacio" time 2 ] node [ id 30 - label "4551114" - name "Vidigal" + label "4550805" + name "Rio Comprido 2" time 2 ] node [ id 31 - label "4551308" - name "Alto da Boa Vista" + label "4550806" + name "Campos da Paz" time 2 ] node [ id 32 - label "4551309" - name "Sumare" + label "4551308" + name "Alto da Boa Vista" time 2 ] node [ id 33 - label "4551310" - name "Vista Chinesa" + label "4551309" + name "Sumare" time 2 ] node [ @@ -225,416 +225,512 @@ graph [ ] node [ id 37 - label "4553301" - 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The Simulation

Output¶

-

The output of a simulation in Epigrass is three-fold: A graphical display which the animated outcome of the simulation, +

The output of a simulation in Epigrass is three-fold: A web-based graphical dashboard to interact with outcome of the simulation, a written report, and a database table with numeric results.

Graphical display¶

-

During a simulation, selected epidemiological variables are animated in a 3-dimensional rendering over the map of the -region containing the network.

+

Epigrass’s Dashboard can be set to open immediately after the simulation has been completed:

+
$ epirunner -D mymodel.epg
+
+
+

or it can be invoked to explore simulations of a model already stored in the database, withou simulating it first:

+
$ epirunner -V mymodel
+
+

Report Generation¶

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 751df39..22d2e47 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ 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to Epigrass\u2019s documentation!","Building and Installing","Epidemic Models","Overview of Epigrass","Writing Custom Models","Using Epigrass"],titleterms:{AND:6,THE:6,The:[4,5,6],Using:6,analysi:0,api:5,approach:4,attribut:5,background:4,build:2,cartograph:4,configur:2,correspond:3,custom:5,data:6,databas:[2,4],defin:4,develop:5,diseas:3,dispers:3,displai:4,document:1,edg:[4,6],environ:5,epidem:[3,6],epidemiolog:6,epigrass:[1,2,4,6],event:6,file:6,gener:4,geograph:4,get:5,graph:5,graphic:[4,6],gui:6,individu:3,infect:3,infecti:3,instal:2,interfac:6,introduct:3,like:3,method:5,model:[3,4,5,6],mysql:2,network:[3,4],node:4,oper:6,option:6,other:2,output:[4,6],overview:4,packag:2,paramet:6,part:6,post:2,python:2,redi:2,report:4,requir:2,run:6,script:6,set:6,simul:[4,6],sipr:3,sir:3,site:[5,6],sninrn:3,specifi:6,start:5,stochast:3,transport:[3,6],typolog:3,user:6,util:6,visual:6,welcom:1,world:6,write:5}}) \ No newline at end of file diff --git a/docs/source/analysis.md b/docs/source/analysis.md deleted file mode 100644 index 88282d2..0000000 --- a/docs/source/analysis.md +++ /dev/null @@ -1,2 +0,0 @@ -Analysis -======== diff --git a/docs/source/index.md b/docs/source/index.md deleted file mode 100644 index 36b6e2d..0000000 --- a/docs/source/index.md +++ /dev/null @@ -1,23 +0,0 @@ -# Welcome to Epigrass\'s documentation! - - -Epigrass is a framework for the construction and simulation of complex -network epidemiology models. At least this is its main vocation. -However, Epigrass can be used to simulate any dynamical system on a set -of nodes (connected into a network or not!). Epigrass is written in pure -Python and is scriptable in Python, for your enjoyment!. - -Contents: - -.. toctree:: - :maxdepth: 2 - - overview - install - intromodels - using - analysis - scripting - - - diff --git a/docs/source/install.md b/docs/source/install.md deleted file mode 100644 index 71ff41e..0000000 --- a/docs/source/install.md +++ /dev/null @@ -1,207 +0,0 @@ -Building and Installing {#install} -======================= - -This chapter will walk through all aspects of Epigrass installation. -From obtaining, building and installing the prerequisites to the -installation of Epigrass itself. - -Most of the steps will be quite simple and similar since they will make -use of standard tools for package installation on Debian GNU/Linux and -derivatives. If you use a different distribution, you should check its -documentation for package installation instructions. - -If, on your distribution, a package is not available for the required -version, you can try to obtain an updated version of the package at the -web-sites provided. On the rare cases where pre-built packages are not -available, instructions on how to build the software from source should -also be available from its web-site. - -Required Packages ------------------ - -### Python - -*Web-site:* - -: - -*Version required:* - -: \>= 2.5 \< 3 - -Python is a simple yet powerful object-oriented language. Its simplicity -makes it easy to learn, but its power means that large and complex -applications can also be created easily. Its interpreted nature means -that Python programmers are very productive because there is no -edit/compile/link/run development cycle. - -Python is probably installed automatically by your GNU/Linux -distribution (it is on Debian). If not, it is best to use your -distribution\'s standard tools for package installation. On Debian for -example: - -```bash -$ sudo apt-get install python python-dev build-essentials python-setuptools -``` - -### Numeric Python - -*Web-site:* - -: - -*Version required:* - -: \>= 1.0 - -Numeric Python is a module for fast numeric computations in Python. - -Example installation: - -```bash -$ sudo apt-get install python-numpy -``` - -### Matplotlib - -*Web-site:* - -: - -*Version required:* - -: \>0.9 - -Matplotlib is a Module that provides plotting capabilities to Python. - -```bash -$ sudo apt-get install python-matplotlib python-matplotlib-data python-matplotlib-doc -``` - -### PyQt - -*Web-site:* - -: - -*Version required:* - -: \>4.0 - -PyQt is a set of Python bindings for the Qt toolkit. PyQt combines all -the advantages of Qt and Python. A programmer has all the power of Qt, -but is able to exploit it with the simplicity of Python. - -PyQt depends on the Qt libraries to run. This dependency will be taken -care by the package installation tools of most distributions, which will -automatically install the required version of Qt. - -Example installation: - -```bash -$ sudo apt-get install python-qt4 -``` - -### PyQwt - -*Web-site:* - -: - -*Version required:* - -: \>5.0 - -PyQwt is a Python binding for the Qwt5 technical widget library. It is -necessary for some os Epigrass\' visualizations capabilities. - -Example installation: - -```bash -$ sudo apt-get install python-qwt5-qt4 -``` - -### MySQL - -*Web-site:* - -: - -*Version required:* - -: \>5.0 - -MySQL is a fast, multi-threaded, multi-user SQL database server. If you -have a MySQL server available in your LAN, you may skip this step after -making sure you have permission to access and use it to store your data. - -Example installation: - -```bash -$ sudo apt-get install mysql-server mysql-client python-mysqldb -``` - -### Redis - -*web-site:* - -: - -*Version required:* - -: \>=2.8 - -Redis is a persistent Key-value database. - -Example installation: - -```bash -$ sudo apt-get install redis-server -``` - -### Other Requirements - -Some other requirements can be installed directly from the Python -Package index using the *pip* tool: - -```bash -$ sudo pip install networkx redis sqlsoup requests sphinx -``` - -#### Post-install configuration - -After installing MySQL, you will need to create a new database in the -server, called *epigrass* and a user with all priviledges to access and -modify it. This is the user Epigrass will use to interact with MySQL. - -section{Installing Epigrass} If you got through all the steps above, it -will be an easy task to install Epigrass. There is a *.deb* package for -installing Epirass on Debian and Ubuntu. However, since it is not -maitained by the developers of Epigrass, It may very well be outdated. -So please download Epigrass\'s source tarball (something named -`Epigrass_someversion.tar.gz`{.interpreted-text role="file"}) from -Sourceforge and, after unpacking it to some temporary directory, install -it by typing: - -```bash -$ sudo python setup.py install -``` - -If the installation proceeds without errors, you will have three new -executables available on your system: - -*epigrass* - -: This starts Epigrass graphical user interface (GUI). - -*epirunner* - -: This is a command line version of Epigrass. With it you can run - models without invoking the GUI. It\'s great for batch simulations - and for remote use. for a quick help, try \"epirunner -h\". - -*epgeditor* - -: A graphical editor of .epg models. A easy way to edit already - existing models. Contains detailed explanations of every section of - the EPG file. $x_3+y$ diff --git a/docs/source/intromodels.md b/docs/source/intromodels.md deleted file mode 100644 index 3f88fb2..0000000 --- a/docs/source/intromodels.md +++ /dev/null @@ -1,467 +0,0 @@ -Epidemic Models -=============== - -::: {.index} -Model -::: - -Introduction ------------- - -::: {.index} -Epidemiological models -::: - -Epidemiological models are implemented in the EpiGrass environment as a -tool to predict, understand and develop strategies to control the spread -of infectious diseases at different time/spatial scales. In this -context, there are two direct potential applications. One is to model -the spread of new diseases through an entirely susceptible population -(ecological invasion). The velocity of spread of new diseases in a -network of susceptible populations depends on their spatial -distribution, size, susceptibility and patterns of contact. In a spatial -scale, climate and environment may also impact the dynamics of -geographical spread as it introduces temporal and spatial heterogeneity. -Understanding and predicting the direction and velocity of an invasion -wave is key for emergency preparedness. - -Besides invasion, network epidemiological models can also be used to -understand patterns of geographical spread of endemic diseases. Many -infectious diseases can only be maintained in a endemic state in cities -with population size above a threshold, or under appropriate -environmental conditions (climate, availability of a reservoir, vectors, -etc). The variables and the magnitudes associated with endemicity -threshold depends on the natural history of the disease -cite{MJKeelingandBTGrenfell1997}. Theses magnitudes may vary from place -to place as it depends on the contact structure of the individuals. -Predicting which cities are sources for the endemicity and understanding -the path of recurrent traveling waves may help us to design optimal -surveillance and control strategies. For measles, for example, models -suggest that geographical spread tend to follow a hierarchical pattern, -starting in big cities (core) and then spreading to smaller neighborhood -cities (satellites) cite{BTGrenfellandONBjoernstadandJKappey2001}. - -EpiGrass implements a series of epidemiological models which, integrated -with a set of analytical and visualization tools, may give us clues -about the overall pattern of diseases spread in the geographical space. -Besides, it can be used for scenario analysis to compare alternative -intervention scenarios. - -Disease models --------------- - -Models of disease dynamics are quite diverse, ranging from caricatures -to very detailed simulations. Traditional models of spread of diseases -are based on the mean field assumption, i.e., that individuals interact -randomly at a certain rate. Important references to the subject are -cite{DiekmannOandHeesterbeekJAP2000,DaleyDJandGaniJandCanningsC2001,IshamVandMedleyG1996,AndersonRMandMayRMandAndersonB1992}. -These models are expressed mathematically as difference equations -(discrete time) or differential equations (continuous time). In the -simplest form, these models do not take into consideration either -individual heterogeneity or the local nature of transmission events. -Increased realism is achieved by structuring the population according to -age, risk behavior, sex, susceptibility, or other category associated -with different risk of getting or transmitting the disease. Within each -sub-population, however, the assumption of well mixing must hold. When -other species are involved in the transmission process (non-human hosts -and vectors), these are also considered as compartments that may be -sub-divided as well according to covariates associated with the risk of -acquiring or transmitting the disease. - -In this context, epidemiological models take the form of -multi-compartmental models where each compartment is a well-mixed -homogeneous population. The model describes the transition of the -individuals in this population through a sequence of disease-related -stages. These stages could be *Susceptible*, *Infected*, *Recovered*, -for example. And the transitions could be .. math:: Susceptible -longrightarrow Infected .. math:: Infected longrightarrow Recovered - -If only these transitions are allowed, then individuals in the recovered -class never become susceptible again (lifelong immunity). If, on the -other hand, immunity is only temporary (as in pertussis), then another -transition should be included: .. math:: Susceptible longrightarrow -Infected .. math:: Infected longrightarrow Recovered .. math:: Recovered -longrightarrow Susceptible - -One way to visualize these models is using state-flow diagrams, where -boxes represent states (compartments) and arrows indicate the -transitions. It is the state identity together with the transitions -allowed that define the type of model in use. - -### Dispersal of infected individuals - -A key feature of EpiGrass is to allow the simulation of geographical -spread of infection from one locality to others. Dispersal of infection -is modeled as in a \'forest fire\' model -cite{BTGrenfellandONBjoernstadandJKappey2001}. An infected individual, -traveling to another city, acts as a spark that may trigger an epidemic -in the new locality. This approach is based on the assumption that -individuals commute between localities and contribute temporarily to the -number of infected in the new locality, but not to its demography. -Further details about this approach can be found in Grenfell et al -(2001). - -In all models presented below, new infections in locality $i$ arise from -the contact between Susceptibles in $i$ and Infectious individuals. -Infectious individuals are of two types: those residents in $i$ $(I_t)$ -and those visiting $i$ $(\theta)$. Mathematically, this is written as: - -$$L_{t+1} = \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}$$ - -where $L_{t+1}$ is the number of new cases, $\beta$ is the contact rate -between Susceptibles and Infectious individuals, $S_t$ is the number of -susceptibles, $I_t$ is the number of infectious individuals resident in -the locality, $\theta$ is the number of infectious individuals visiting -the locality, $N_t$ is the population residing in the locality and $n_t$ -is the total number of individuals visiting the locality. $\alpha$ is a -mixing parameter. $\alpha=1$ corresponds to homogeneous mixing -(Finkenstadt and Grenfell, 2000). - -### Typology of infectious diseases and corresponding models - -::: {.index} -Models;typology -::: - -Here we present a brief description of the typology of infectious -diseases models based on the main route of transmission, and type of -immunity resulting from infection. These models correspond to the types -of models built into EpiGrass. - -#### SIR-like models - -The natural history of many directly transmitted infectious diseases can -be appropriately described by a SIR-like model. *SIR* stands for -Susceptible $(S)$, Infected $(I)$ and Recovered $(R)$. Archetypal *SIRs* -are measles or chickenpox, i.e., diseases that confer lifelong immunity -(but see cite{KGlassandBTGrenfell2004}). An individual starts his life -in the $S$ state and may progress to the $I$ state. The rate of -progression of individuals from $S$ to $I$ is called the incidence rate -or force of infection $(\lambda)$ which is a function of contact rate, -probability of transmission per contact and density of infectious -individuals. Individuals stay in the infectious period for a certain -time and then move to the recovered state where they become immune to -new infections. Generally, the removal rate from the infectious class is -the inverse of the infectious period (i.e., it is assumed that the -duration of infection is exponentially distributed). - -> Figure: SIR-like models - -Variations of this model allow cases where infected individuals do not -acquire immunity after infection, thus returning to the susceptible pool -(*SIS* model). Another variation is the inclusion of a latent stage to -hold individuals that are infected but not infectious to others yet -(incubation period). These are the *SEIR* (with immunity) and *SEIS* (no -immunity) models. - -Next, we describe in more detail each one of these models in their -deterministic and stochastic versions, as used by EpiGrass. - -::: {.tabularcolumns} -l\| -::: - - -------------------------------------------------------------------- - **Symbol** **Meaning** - ---------------- --------------------------------------------------- - $L_t$ number of newly infected individuals at time - - $E$ number of exposed but not infectious individuals at - time t - - $I$ number of infectious individuals at time t - - $R$ number of recovered individuals at time t - - $\beta$ contact rate ($t^{-1}$) - - $\theta$ number of infectious visitors - - $\alpha$ mixing parameter ($\alpha = 1$ means homogeneous - mixing) - - $n$ number of visitors - - $N$ population $(S+E+I+R)$ - - $B$ susceptible pool replenishment - - $r$ fraction of $I$ recovering from infection per unit - of time $([0,1])$ - - $e$ fraction of $E$ becoming infectious per unit of - time $([0,1])$ - - $\delta$ probability of acquiring immunity $([0,1])$ - - $w$ probability of losing immunity $([0,1])$ - - $p$ probability of recovered individual acquiring - infection, given exposure $([0,1])$ - -------------------------------------------------------------------- - -::: {.index} -Models;SIR -::: - -**SIR models** - -: Examples of diseases represented by SIR models are measles, - chickenpox. Some diseases that do not confer lifelong immunity may - be represented by this model if only short term dynamics is of - interest. In the scale of a year, influenza and pertussis, for - example, could be described using SIR. The SIR model is implemented - in EpiGrass as a system of four difference equations. Besides the - three equations describing the dynamics of $S$, $I$ and $R$, a - fourth equation explicitly defines the number of new cases per time - step, $L(t)$ (i.e., the incidence). In general, this quantity is - embedded in the $I$ equation (prevalence), but it is important to - keep track of the incidence if one wishes to compare prediction with - notification data. - -$$\begin{aligned} -\begin{align} - L_{t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - I_{t+1} &= L_{t+1} + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{t+1}\nonumber\\ - R_{t+1} &= N_t-(S_{t+1}+I_{t+1})\nonumber -\end{align} -\end{aligned}$$ - -This model can be easily extended to include diseases without recovery, -for example AIDS, the so called SI models. Basically, the recovery rate -is set to 0. - -::: {.index} -Models;SIS -::: - -**SIS models** - -: In the SIS model, individuals do not acquire immunity after the - infection. They return directly to the susceptible class. - - The only difference between \$SIS\$ and \$SIR\$ models is the - absence of \$R\$ and the flow of recovered individuals to the - susceptible stage: - -$$\begin{aligned} -\begin{align} - L_{t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t} \nonumber\\ - I_{t+1} &= L_{t+1} + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{t+1} + r I_{t+1}\nonumber -\end{align} -\end{aligned}$$ - -::: {.index} -Models;SEIR -::: - -**SEIR models** - -: These models have an extra compartment for those individuals who - have acquired the infection but are still not infectious to others. - This is the latent period and it is often parameterized as the - inverse of the incubation period. Note, however, that for many - diseases, initiation of infectiousness does not necessarily - coincides with symptoms. In principle, any disease described by the - SIR model can also be described by the SEIR model. The decision - regarding the use of one or another depends on the magnitude of the - latent period in relation to the time frame of other events in the - simulation. The model has the form: - -$$\begin{aligned} -\begin{align} - L_{t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - E_{t+1} &= (1-e) E_t + L_{t+1}\nonumber\\ - I_{t+1} &= e E_t + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{t+1}\nonumber\\ - R_{t+1} &= N_t-(S_{t+1}+I_{t+1}+E_{t+1})\nonumber -\end{align} -\end{aligned}$$ - -::: {.index} -Models;SEIS -::: - -**SEIS models** - -: These are SIS models with the inclusion of the latent stage. - -$$\begin{aligned} -\begin{align} - L_{t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - E_{t+1} &= (1-e) E_t + L_{t+1}\nonumber\\ - I_{t+1} &= e E_t + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{t+1} + r I_t\nonumber -\end{align} -\end{aligned}$$ - -#### SIpR-like models - -These are *SIR* models with immunity intermediary between full (*SIR*) -and null (*SIS*). Some possibilities arise here: 1) Infection confers -full immunity to a fraction of the individuals and the remaining ones -return to the susceptible class again, after infection. (*SIpRpS*); 2) -Infection provides only partial immunity and recovered individuals are -partially susceptible to new infection (*SIpR*); 3) Immunity is full -right after infection but wanes with time (*SIRS*). Each model is -presented below. Figure `fig:sipr`{.interpreted-text role="ref"} -illustrates them diagrammatically. - -![SIpR-like models.](SIpRdiagram.png) - -Related models, that included the latent state \$E\$ are: -textit{SEIpRpS}, *SEIpR*, *SEIRS*. - -::: {.index} -Models;SIpRpS -::: - -**SIpRpS model** - -: This model assumes that a fraction \$delta\$ of infectious - individuals acquire full immunity while the remaining \$(1-delta)\$ - returns to the susceptible stage. The model is: - -$$\begin{aligned} -\begin{align} - L_{t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - I_{t+1} &= L_{t+1} + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{t+1} + (1-\delta) r I_t\nonumber\\ - R_{t+1} &= N_t-(S_{t+1}+I_{t+1})\nonumber -\end{align} -\end{aligned}$$ - -**SIpR model** - -: This model assumes that immunity is only partial and recovered - individuals may acquire infection again (at a lower rate - $p \lambda$, where $0\leq p \leq 1$). Two equations calculate the - number of new infections. $L_S$ calculates the number of - susceptibles that become infected at $t+1$. $L_R$ calculates the - number of recovered that become infected at $t+1$. The latter are - less susceptible to the disease when compared to susceptibles. The - model is: - -$$\begin{aligned} -\begin{align} - L_{S,t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - L_{R,t+1} &= p \beta R_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber\\ - I_{t+1} &= L_{S,t+1} + L_{R,t+1} + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{S,t+1} \nonumber\\ - R_{t+1} &= N_t-(S_{t+1}+I_{t+1}) \nonumber - \end{align} -\end{aligned}$$ - -::: {.index} -Models;SIRS -::: - -**SIRS model** - -: Here, the immunity acquired by infection wanes with time. Pertussis - is an example of this dynamic. - -$$\begin{aligned} -\begin{align} \label{} - L_{S,t+1} &= \beta S_t \frac{(I_t+\theta)^\alpha} {N_t+n_t}\nonumber \\ - I_{t+1} &= L_{S,t+1} + L_{R,t+1} + (1-r)I_t\nonumber\\ - S_{t+1} &= S_t + B - L_{S,t+1} + w R_t\nonumber\\ - R_{t+1} &= N_t-(S_{t+1}+I_{t+1}) \nonumber -\end{align} -\end{aligned}$$ - -#### SnInRn-like Models - -These are models with more than one compartment for susceptibles, -infected and recovered stages. They are used when infection involves -more than one distinct populations. Vector borne diseases are classical -examples where a SIR model is used to describe infection in humans and -another SIR-like model is used to describe infection in the vector -(and/or the reservoir(s)). Dengue fever and yellow fever are examples. -Sexually transmitted diseases may also be modeled with SnInRn models if -male and female populations are distinguished. These models can be -define by the user as a custom model. - -### Stochastic models - -::: {.index} -Models;Stochastic -::: - -All models described so far are deterministic. EpiGrass allows -simulation of stochastic processes. This is done by assuming that -$L_{t+1}$ is a random variable with expected value given by the -expressions found in the deterministic models. The user may choose the -probability distribution for $L_{t+1}$ between Poisson or Negative -Binomial to draw realizations of $L_{t+1}$: - -$$l_{t+1} \sim Poisson (L_{t+1})$$ - -or - -$$l_{t+1} \sim NegBin (I_t, \frac{I_t}{I_t+L_{t+1}})$$ - -The Poisson distribution assumes independent events while the negative -Binomial assume clustering of transmission events. - -Network transportation models ------------------------------ - -::: {.index} -Models;transportation -::: - -The transmission models describe the dynamics of infection in a -well-mixed population. EpiGrass allows the user to model the movement of -infectious individuals between well-mixed populations, thus simulating -the spread of disease through space. EpiGrass represents geographical -space as a network where cities or localities are nodes and -transportation routes are edges. The term network refers to the -framework of routes within a system of locations, identified as nodes or -sites. An edge is a single link between two sites (a road, a railroad, -an air route or a river/sea corridor). - -Transportation networks, like many networks, are generally embodied as -set of locations and a set of links representing connections between -those locations. The arrangement and connectivity of a network is known -as its *topology*. Major types of topology are illustrated in figure -ref{fig:artgraphs}. Velocity and direction of disease spread depend on -the topology and weight of the edges of the transport network and there -are many properties of networks that may useful when analyzing the -spread of diseases. EpiGrass calculates a set of these properties as -described in chapter `analysis`{.interpreted-text role="ref"}. - -In a transportation network, each edge (or link) is characterized by a -variable *flow* which states the number of passengers that travel -through that link per unit time. EpiGrass uses this information to -calculate the number of passengers arriving at each city, per time step. -For example, consider node $N1$ in figure `simpnet`{.interpreted-text -role="ref"}. At each time step, it receives 10 passengers from $N2$, 5 -from $N5$, 1 from $N4$. Now suppose that, at this time step, 10% of the -population within each site is infectious ($I$ state), according to the -epidemic model. Thus, a total of -$10\% \times 10 + 10\% \times 5 + 10\% \times 1 = 1.6$ infectious -individuals are visiting site $N1$. In the epidemic model embedded in -$N1$, EpiGrass sets $n = 16$ and $\theta = 1.6$. This calculation of -$\theta$ is based on a deterministic argument. The other possibility, -allowed by Epigrass, is to define $\theta$ as a random variable, that -follows a binomial distribution with parameters $n$,$p$, where $n$ is as -given in the deterministic version and $p$ is the proportion of -infectious individuals in the source population. From version 1.4 on, -EpiGrass can also take into account the specific delays of each -connection. When the average speed of the transportation system is set -to a value greater than zero, epigrass calculates the time required to -complete each trip and attributes this delay (in units of time) to the -parameter $\delta$. So, the number of infectious passengers arriving at -any given city, at time $t$ corresponds to the number of infectious -passengers that left the city of origin at time $t-\delta$. - -Deterministic: - -$$\Theta_t=n\times\frac{I_{t-\delta}}{N}$$ - -Stochastic: - -$$\Theta_t \sim Binomial(N,\frac{I_{t-\delta}}{N})$$$$A simple transportation network$$ diff --git a/docs/source/overview.md b/docs/source/overview.md deleted file mode 100644 index b25ffab..0000000 --- a/docs/source/overview.md +++ /dev/null @@ -1,265 +0,0 @@ -Overview of Epigrass -==================== - -Epigrass is a platform for network epidemiological simulation and -analysis. It enables researchers to perform comprehensive -spatio-temporal simulations incorporating epidemiological data and -models for disease transmission and control in order to create -sophisticated scenario analyses. - -Modeling Approach ------------------ - -The geographical networks over which epidemiological processes take -place can be very straightforwardly represented in a object-oriented -framework. In such a framework, the nodes and edges of the geographical -networks are objects with their attributes and methods. - -Once the archetypal node and edge objects are defined with appropriate -attributes and methods, then a code representation of the real system -can be constructed, where cities (or other geographical localities) and -transportation routes are instances of the node and edge objects, -respectively. The whole network is also an object with a whole -collection of attributes and methods. - -This framework leads to a compact and hierarchical computational model -consisting of a network object containing a variable number of node and -edge objects. This framework also do not pose limitations to -encapsulation, potentially allowing for networks within networks if -desirable. - -For the end user perspective, this framework is transparent since it -mimics the natural structure of the real system. Even after the model is -converted into a code object all of its component objects remain -accessible to the user\'s models. - -Geographical Network Models ---------------------------- - -::: {.index} -shapefile -::: - -Epigrass\'s geo-referenced models are built from two basic sources of -data: a map (in shapefile format) which provide the cartographical base -over which the models are represented and specific data about nodes and -edges that are provided by the user for the network of interest. - -### Defining the Cartographic Background - -If the user has a map for the georeferred data, this can be passed to -Epigrass. In this case, the cartographic background is defined by -defining the name of the shapefile file (with path relative to the -working directory) in the model\'.epg file. Along with the path to the -shapefile, the variable in the shapefile, which contains the geocode of -localities and their name must also be specified: - - shapefile = ['riozonas_LatLong.shp','nome_zonas','zona_trafe'] - -If the user does not have a map in shapefile format, he can still use -Epigrass. In this case, the georeferred data is read only from two .csv -files (more on that ahead). - -::: {.index} -node, site -::: - -### Defining Nodes - -A graph has nodes and edges. Nodes can be cities or other localities -depending on the model being constructed. The definition of nodes -require the setting of many attributes listed below. The nodes will have -many more attributes defined at run-time which will depend on other -aspects of the model, these will be discussed later. - -The data necessary at build time to create nodes come from a CSV -(comma-separated-values) ASCII-text file, with the following attributes, -(in this order): - -*Latitude, Longitude* - -: This attribute will be used to geo reference the node. The - coordinate system must match those used in the cartographic base - imported from GRASS. Coordinates can be coded in either decimal or - sexagesimal format. - -*Name* - -: Used for identification purposes only. It can be a city name, for - instance. - -*Population* - -: Since the simulation models will all be of a populational nature. - Population size must be specified at build time. - -*Geocode* - -: A Unique Geocode (a number) is required. It will be used as a - label/index to facilitate reference to specific nodes. - -::: {.index} -edge -::: - -### Defining Edges - -In Epigrass\' graphs, edges represent transportation routes. Similarly -to nodes, edges are defined at build-time with a reduced set of -attributes which will be extended at run-time. Epigrass also expects to -get these attributes from a CSV file: - -*Source* - -: The name of the source node. The edges are bi-directional, but the - nodes are labeled source and destination for reference purposes. - -*Destination* - -: The name of the destination node. - -*Forward migration* - -: Migration rate from source to destination, in number of persons per - unit of time. - -*Backward migration* - -: Migration rate from destination to source, in number of persons per - unit of time. - -*Length* - -: Distance in kilometers (or another unit) from source to destination - via the particular route (not straight line distance). - -*Source\'s geocode* - -: This is the unique numerical identifier used in the sites file. - -*Destination\'s geocode* - -: This is the unique numerical identifier used in the sites file. - -::: {.index} -models -::: - -### Defining models - -The word model in Epigrass can mean two distinct objects: The network -model and the node\'s epidemic model. - -Node objects, in an Epigrass model, contain well-mixed population -dynamic models within them. These models determine the dynamics of -epidemics within the particular environments of each node. Epigrass -comes with a few standard epidemiological -modelsindex{Models!epidemiological models} to choose from when setting -up your network. Currently, The same model type is applied to every node -although their parameterization is node-specific. Besides the built-in -model types, users can define their own, as shown in the chapter *Using -Epigrass*. - -::: {.index} -single: models;epidemiological models single: models;network models -::: - -Network models are specified in a ASCII-text script file (Called a -`.epg`{.interpreted-text role="file"} file). Epigrass comes with a few -demo Network models for the user to play with until he/she is confident -enough to build their own. Even then, it is advisable to use the demo -scripts provided as templates to minimize syntax errors. - -The script on the appendix specifies a network model with an stochastic -SEIR (see chapter on epidemiological modeling) epidemic model in its -nodes. The user should study this model and play with its parameters to -understand the features of Epigrass. A step-by-step tutorial on how to -edit the model script can be found in the chapter *Using Epigrass*. - -The Simulation --------------- - -A simulation run in Epigrass consists of a series of tasks performed at -each time step[^1] . - -*Calculate migration* - -: For all edges in the network, the number of persons traveling each - way is determined for the current time-step. - -*Run epidemic models* - -: For each node in the network the epidemic demographics are updated - based on the local number of infected and susceptible individuals - which have been updated by the transportation system. - -All aspects of the simulation such as number of passengers traveling on -each edge, number of infected/susceptible on each node and etc., are -recorded in a step-by-step basis. This complete record allows for the -model to be analyzed after the simulation has been completed without -having to recalculate it. - -### Output - -The output of a simulation in Epigrass is three-fold: A graphical -display which the animated outcome of the simulation, a written report, -and a database table with numeric results. - -#### Graphical display - -During a simulation, selected epidemiological variables are animated in -a 3-dimensional rendering over the map of the region containing the -network. - -#### Report Generation - -The report contains a detailed analysis of the network model and the -simulations ran with it. The report generates a LaTeX source file and -compiles it to a PDF document for visualization. - -Three types of report are currently available: - -**Report = 1** - -: Returns a set of descriptors of the network, described in chapter - -**Report = 2** - -: Returns a set of basic epidemiological measures and plots of the - time series. - -**Report = 3** - -: Report 1 + Report 2 - -Report Generation is an optional, though recommended, step done at the -end of the simulation. For the report, descriptive statistics are -generated for the network. These have to do with network topology and -properties. Additional sections can be added to the report with basic -statistical analyses of the output of pre-selected nodes[^2] . - -#### Database output - -::: {.index} -single: Database;results table single: Database;epigrass database -::: - -Time series of **L**, **S**, **E**, and **I**, from simulations, are -stored in a MySQL database named *epigrass* . The results of each -individual simulation is stored in a different table named after the -model\'s script name, the date and time the simulation has been run. For -instance, suppose you run a simulation of a model stored in a file named -`script.epg`{.interpreted-text role="file"}, then at the end of the -simulation, a new table in the epigrass database will be created with -the following name: *script\_Wed\_Jan\_26\_154411\_2005*. Thus, the -results of multiple runs from the same model get stored independently. - -Epigrass also supports the SQLite database and CSV files as output for -the time-series. The naming convention also applies to these other -formats. - -**Footnotes** - -[^1]: The number of time steps is defined in the model script - -[^2]: Listed in the siteRep variable at the script diff --git a/docs/source/overview.rst b/docs/source/overview.rst index 062007e..281383a 100644 --- a/docs/source/overview.rst +++ b/docs/source/overview.rst @@ -137,14 +137,23 @@ All aspects of the simulation such as number of passengers traveling on each edg Output ------ -The output of a simulation in Epigrass is three-fold: A graphical display which the animated outcome of the simulation, +The output of a simulation in Epigrass is three-fold: A web-based graphical dashboard to interact with outcome of the simulation, a written report, and a database table with numeric results. Graphical display ^^^^^^^^^^^^^^^^^ -During a simulation, selected epidemiological variables are animated in a 3-dimensional rendering over the map of the -region containing the network. +Epigrass's Dashboard can be set to open immediately after the simulation has been completed: + +.. code-block:: bash + + $ epirunner -D mymodel.epg + +or it can be invoked to explore simulations of a model already stored in the database, withou simulating it first: + +.. code-block:: bash + + $ epirunner -V mymodel Report Generation ^^^^^^^^^^^^^^^^^ diff --git a/docs/source/scripting.md b/docs/source/scripting.md deleted file mode 100644 index d3e6664..0000000 --- a/docs/source/scripting.md +++ /dev/null @@ -1,420 +0,0 @@ -Writing Custom Models {#custom} -===================== - -::: {.index} -custom models -::: - -The most powerful feature of Epigrass is the ability to use custom -models. It allows the user to specify intra-node dynamics and, in doing -so, break away from the limitations imposed by the built-in models. - -By learning to write his/her own models, the user begins to realize the -full potential of Epigrass, which goes beyond being a platform to -simulate networked epidemics. In reality Epigrass can be used to model -any distributed dynamical system taking place on a set of nodes -(connected or not). - -Getting Started ---------------- - -The best way to get started in writing custom models is to look at the -example distributed with Epigrass. It can be found in -`demos/CustomModel_example.py`{.interpreted-text role="file"}: - - # This is a custom model to used in place of Epigrass' built-in models. Custom - # models must always be on a file named CustomModel.py and contain at least - # a function named Model. Both the File name and the function Names are case-sensitive, - # so be careful. Please refer to the manual for intructions on how to write your - # own custom models. - - def Model(self,vars,par,theta=0, npass=0): - """ - Calculates the model SIR, and return its values. - * vars The state variables of the models - * par The parameters (Beta, alpha, E,r,delta,B, w, p) see docs. - * theta = infectious individuals from neighbor sites - * npass = Total number of people arriving at this node - """ - # Get state variables' current values - - if self.parentSite.parentGraph.simstep == 1: # if first step - # Define variable names to appear in the output - self.parentSite.vnames = ('Exposed','Infectious','Susceptible') - # And get state variables's initial values (stored in dict self.bi) - - E,I,S = (self.bi['e'],self.bi['i'],self.bi['s']) - else: # if nor first step - E,I,S = vars - - # Get parameter values - N = self.parentSite.totpop - beta,alpha,e,r,delta,B,w,p = (self.bp['beta'],self.bp['alpha'], - self.bp['e'],self.bp['r'],self.bp['delta'],self.bp['b'], - self.bp['w'],self.bp['p']) - - #Vacination event (optional) - if self.parentSite.vaccineNow: - S -= self.parentSite.vaccov*S - - # Model - Lpos = beta*S*((I+theta)/(N+npass))**alpha #Number of new cases - Ipos = (1-r)*I + Lpos - Spos = S + B - Lpos - Rpos = N-(Spos+Ipos) - - # Update stats - self.parentSite.totalcases += Lpos #update number of cases - self.parentSite.incidence.append(Lpos) - - # Raise site infected flag and add parent site to the epidemic history list. - if not self.parentSite.infected: - if Lpos > 0: - self.parentSite.infected = self.parentSite.parentGraph.simstep - self.parentSite.parentGraph.epipath.append( - (self.parentSite.parentGraph.simstep, - self.parentSite,self.parentSite.infector)) - - self.parentSite.migInf.append(Ipos) - - return [0,Ipos,Spos] - -Let\'s analyze the above code. The first thing to notice is that an -Epigrass custom model is a Python program. So anything you can do with -Python in your system, you can do in your custom model. Naturally, your -knowledge of the Python programming language will define how far you can -go in this customization. There are a few requirements on this Python -program in order to make it a valid custom model from Epigrass\'s -perspective. - -1. - - It must define a global function named **Model**. This function will be inserted as a method on every node object, at run time. - - : 1. - - This function must declare the following arguments: - - : - *self*: reference to the model object. - - *vars*: A list with the values of the model\'s state - variables in time t-1 in the same order as returned - by this function. - - *par*: The parameters of the model. Listed in the - same order as defined in the - `.epg`{.interpreted-text role="file"} file. - - *theta*: Number of infectious individuals arriving - from neighboring sites. For disconnected models, it - is 0. - - *npass*: The total number of passengers arriving - from neighboring sites. For disconnected models, it - is 0. - - 2. In the beginning of the function you define a list of - strings (self.parentSite.vnames) which will be the names - used when storing the resulting time-series in the database. - Choose strings that are not very long and are meaningful. - You only need to do this once, ate the beginning of the - simulation so put it inside an *if* statement, which will be - executed only at time-step 1 (see code above). - 3. After defining variable names, set their initial values in - the same *if* clause. An *else* clause linked to this one - will set variables values for the rest of the simulation. - 4. Define local names for the total population *N* and fixed - parameters. - 5. Proceed to implement your model anyway you see fit. - 6. - - Feed some site level variables (*incidence*,) with the result of the simulation. - - : - *incidence*: list of new cases per time step. - - *infected*: Boolean stating if the site has been - infected, i.e., it has had an autoctonous case. - - *epipath*: This variable is at the graph level and - contains the path of spread of the simulation. - - *migInf*: Number of infectious individuals in this - site per time-step. - - 7. Finally, this function must return a list/tuple with the - values of the state variables in the same order as received - in vars. - -> ::: {.warning} -> ::: {.admonition-title} -> Warning -> ::: -> -> The strings in self.parentSite.vnames must be valid *SQL* variable -> names, or else you will have a insert error at the end of the -> simulation. -> ::: - -After defining this function with all its required features, you can -continue to develop you custom model, writing other functions classes, -etc. Note however, that only the *Model* function will be called by -Epigrass, so any other code you add to your program must be called from -within that function. - -::: {.note} -::: {.admonition-title} -Note -::: - -Since `CustomModel`{.interpreted-text role="file"} is imported from -within Epigrass, any global code (unindented) in it is also executed. So -you may add imports and other initialization code. -::: - -::: {.warning} -::: {.admonition-title} -Warning -::: - -The name CustomModel.py is case-sensitive and cannot be changed. The -same is true for the *Model* function. -::: - -The Environment ---------------- - - - -From quickly going through the example Custom model above it probably -became clear, to the Python-initiated, that Yous can access variables at -the node and graph levels. This is possible because *Model* becomes a -method in a node object which in is turn is contained into a graph -object (see figure). - -Besides being nested within the *graph* object, *node* and *edge* -contain references to their containers. This means that using the -introspective abilities of Python the user can access any information at -any level of the full *graph* model and use it in the custom model. In -order to help you do this, Let\'s establish an API for developing custom -models. - -### Model Development API - -All attributes and methods (functions) from all around the simulation -must be references from the model\'s perspective, denoted by *self*. The -parent objects can be accessed through the following notation: - -- - - *self.parentSite* - - : Is the Site (node) containing the model. - -- - - *self.parentSite.parentGraph* - - : Is the Graph containing the parent site of the model. - -The following attributes and methods can be accessed by appending them -to one one the objects above. For example: - - self.parentSite.parentGraph.simstep - -#### Site Attributes and Methods - -Not all attributes and methods are listed, only the most useful. For a -complete reference, look at the source code documentation. - -::: {.Site} -self.parentSite. Actually named *siteobj* in the source code. - -::: {.attribute} -bi - -Dictionary with initial values for all of the model\'s state variables. -Keys are the variable names. -::: - -::: {.attribute} -bp - -Dictionary with initial values for all of the model\'s parameters. Keys -are the parameter names. -::: - -::: {.attribute} -totpop - -Initial total population -::: - -::: {.attribute} -ts - -List containing the model output time series (variables in the same -order of the model) -::: - -::: {.attribute} -incidence - -Incidence time series -::: - -::: {.attribute} -infected - -Has the site been already infected? (logical variable) -::: - -::: {.attribute} -sitename - -Site\'s name (provided in the .csv) -::: - -::: {.attribute} -values - -Tuple containing extra-variables provided by .csv file -::: - -::: {.attribute} -parentGraph - -Graph to which Site belongs (see class Graph) -::: - -::: {.attribute} -edges - -List containing all edge objects connected to Site -::: - -::: {.attribute} -inedges - -List containing all inbound edges -::: - -::: {.attribute} -outedges - -List containing all outbound edges -::: - -::: {.attribute} -geocode - -Site\'s geocode -::: - -::: {.attribute} -modtype - -Type of dynamic model running in Site -::: - -::: {.attribute} -vaccination - -Time and coverage of vaccination event. Format as in .epg -::: - -::: {.attribute} -vaccineNow - -Flag indicating that it is vaccine day (0 or 1) -::: - -::: {.attribute} -vaccov - -Current vaccination coverage -::: - -::: {.method} -vaccinate(cov) - -At time t, the population is vaccinated with coverage cov -::: - -::: {.method} -getOutEdges() - -Returns list of outbound edges -::: - -::: {.method} -getInEdges() - -Returns list of inbound edges -::: - -::: {.method} -getNeighbors() - -Returns a dictionary of neighbor sites as keys and distances as values -::: - -::: {.method} -getDistanceFromNeighbor(site) - -Returns the distance in km from a given neighbor -::: - -::: {.method} -getDegree(site) - -Returns degree of this site, that is, the number of sites connected to -it -::: -::: - -#### Graph Attributes and Methods - -Not all attributes and methods are listed, only the most useful. For a -complete reference, look at the source code documentation. - -::: {.Graph} -self.parentSite.parentGraph - -::: {.attribute} -simstep - -Time-step of the simulation. Use it to keep track of the simulation -progress. -::: - -::: {.attribute} -speed - -The speed of the transportation system -::: - -::: {.attribute} -maxstep - -Final time-step of the simulation -::: - -::: {.attribute} -episize - -Current size of the epidemic, graph-wise. -::: - -::: {.attribute} -site\_list - -Full list of nodes in the graph. Each element in this list is a real -node object. -::: - -::: {.attribute} -edge\_list - -Full list of nodes in the graph. Each element in this list is a real -node object. -::: - -::: {.method} -getSite(name) - -Returns an site object named *name* -::: -::: diff --git a/requirements.txt b/requirements.txt index e49bcc6..5a74a7c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,4 +15,5 @@ dbfread pweave pymysql fiona -shapely \ No newline at end of file +shapely +altair \ No newline at end of file