#58 _PyObject_MakeTpCall (callable=0x5555560c76d0, args=<optimized out>, nargs=<optimized out>, keywords=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:159
#59 0x00005555557356f4 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdbc40378, callable=0x5555560c76d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:125
#60 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#61 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#62 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdbc401f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#63 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#64 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#65 0x00005555556898bd in PyVectorcall_Call (kwargs=0x7fffdbbdbac0, tuple=0x7fffdbc30130, callable=0x7fffdc215d30) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:199
#66 PyObject_Call (callable=0x7fffdc215d30, args=0x7fffdbc30130, kwargs=0x7fffdbbdbac0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:227
#67 0x000055555573231a in do_call_core (kwdict=0x7fffdbbdbac0, callargs=0x7fffdbc30130, func=0x7fffdc215d30, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5010
#68 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#69 0x000055555571c6b9 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2d79f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#70 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fffdbbde6a8, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x7fffdc218300, name=0x7ffff75e1930, qualname=0x7fffdc50e270) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#71 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffdbbde6a0, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#72 0x000055555573083d in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdbbde6a0, callable=0x7fffdc215dc0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#73 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#74 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3486
#75 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdbbde520) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#76 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#77 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#78 0x00005555557064e0 in _PyObject_FastCallDict (kwargs=0x0, nargsf=2, args=0x7fffffff8180, callable=0x7fffdc6801f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:96
#79 _PyObject_Call_Prepend (kwargs=0x0, args=<optimized out>, obj=<optimized out>, callable=0x7fffdc6801f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:887
#80 slot_tp_init (self=<optimized out>, args=<optimized out>, kwds=0x0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/typeobject.c:6787
#81 0x0000555555683818 in type_call (kwds=0x0, args=0x7fffdbc30040, type=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/typeobject.c:994
#82 _PyObject_MakeTpCall (callable=0x5555560e5de0, args=<optimized out>, nargs=<optimized out>, keywords=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:159
#83 0x00005555557356f4 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdbbde500, callable=0x5555560e5de0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:125
#84 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#85 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#86 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdbbde380) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#87 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#88 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#89 0x0000555555735223 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd098fd8, callable=0x7fffdc1c00d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#90 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#91 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#92 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd098e40) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#93 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fffffff8698, kwcount=0, kwstep=1, defs=0x7ffff38b2928, defcount=1, kwdefs=0x0, closure=0x0, name=0x7ffff7f8c470, qualname=0x7fffdd3a21c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#94 0x000055555571d514 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffffff8680, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#95 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=3, args=0x7fffffff8680, callable=0x7fffdd3aba60) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#96 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#97 object_vacall (base=<optimized out>, callable=0x7fffdd3aba60, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#98 0x000055555569549e in PyObject_CallFunctionObjArgs (callable=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1259
#99 0x000055555568c5d4 in _PyObject_GenericGetAttrWithDict (suppress=0, dict=<optimized out>, name=0x7ffff7187ee0, obj=0x7ffff7189970) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:1307
#100 PyObject_GenericGetAttr (name=0x7ffff7187ee0, obj=0x7ffff7189970) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:1335
#101 PyObject_GetAttr (v=0x7ffff7189970, name=0x7ffff7187ee0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:941
#102 0x0000555555730ca9 in _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:2966
#103 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdbbde1e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#104 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#105 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#106 0x00005555556937a8 in _PyObject_Vectorcall (kwnames=0x0, nargsf=1, args=0x7fffffff89b0, callable=0x7fffdc1c01f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#107 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#108 property_descr_get (self=<optimized out>, obj=<optimized out>, type=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/descrobject.c:1496
#109 0x000055555568c4d4 in _PyObject_GenericGetAttrWithDict (suppress=0, dict=0x0, name=0x7fffdd3268b0, obj=0x7ffff7189970) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:1254
#110 PyObject_GenericGetAttr (name=0x7fffdd3268b0, obj=0x7ffff7189970) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:1335
#111 PyObject_GetAttr (v=0x7ffff7189970, name=0x7fffdd3268b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/object.c:941
#112 0x0000555555730ca9 in _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:2966
#113 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x555555bf6ac0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#114 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x7fffdbc2df58, kwargs=0x7fffdbc21478, kwcount=3, kwstep=1, defs=0x7fffdc1aa918, defcount=4, kwdefs=0x0, closure=0x0, name=0x7ffff7f8c530, qualname=0x7fffdc9abf30) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#115 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffdbc21470, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#116 0x0000555555706629 in _PyObject_FastCallDict (kwargs=0x7fffdbc2f080, nargsf=1, args=0x7fffffff8cf0, callable=0x7fffdc1bdee0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:104
#117 _PyObject_Call_Prepend (kwargs=0x7fffdbc2f080, args=<optimized out>, obj=<optimized out>, callable=0x7fffdc1bdee0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:887
#118 slot_tp_init (self=<optimized out>, args=<optimized out>, kwds=0x7fffdbc2f080) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/typeobject.c:6787
#119 0x000055555568ebc7 in type_call (type=<optimized out>, args=0x7ffff7f8b040, kwds=0x7fffdbc2f080) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/typeobject.c:994
#120 0x0000555555689c9d in PyObject_Call (callable=0x555556117490, args=0x7ffff7f8b040, kwargs=0x7fffdbc2f080) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:245
#121 0x000055555573231a in do_call_core (kwdict=0x7fffdbc2f080, callargs=0x7ffff7f8b040, func=0x555556117490, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5010
#122 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#123 0x000055555571c6b9 in PyEval_EvalFrameEx (throwflag=0, f=0x5555561bbb80) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#124 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x555555ff4f40, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x7fffdbceaa00, name=0x7ffff77d9db0, qualname=0x7fffdd2d27b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#125 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x555555ff4f38, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#126 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x555555ff4f38, callable=0x7fffdbbcfd30) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#127 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#128 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#129 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x555555ff4d70) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#130 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x7fffdbc29ef8, kwargs=0x7fffdc78f958, kwcount=1, kwstep=1, defs=0x7fffdd0a6fb8, defcount=6, kwdefs=0x0, closure=0x0, name=0x7ffff779f7b0, qualname=0x7ffff779f7b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#131 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffdc78f950, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#132 0x0000555555689b59 in PyVectorcall_Call (kwargs=<optimized out>, tuple=<optimized out>, callable=0x7fffdcffbaf0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:199
#133 PyObject_Call (callable=0x7fffdcffbaf0, args=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:227
#134 0x000055555573231a in do_call_core (kwdict=0x7fffdbc2b680, callargs=0x7fffdbc29e80, func=0x7fffdcffbaf0, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5010
#135 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#136 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x555555f8f1e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#137 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fffdc63fd18, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff779f7f0, qualname=0x7ffff779f7f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#138 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffdc63fd10, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#139 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdc63fd10, callable=0x7fffdc1c0e50) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#140 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#141 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#142 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdc63fba0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#143 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=0, kwstep=2, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#144 0x000055555574a8ca in PyEval_EvalCodeEx (closure=0x0, kwdefs=0x0, defcount=0, defs=0x0, kwcount=0, kws=0x0, argcount=0, args=0x0, locals=0x7fffdbc2b500, globals=0x7fffdbc2b500, _co=0x7fffdbc2e7c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4327
#145 PyEval_EvalCode (locals=0x7fffdbc2b500, globals=0x7fffdbc2b500, co=0x7fffdbc2e7c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:718
#146 builtin_exec_impl.isra.14 (locals=0x7fffdbc2b500, globals=0x7fffdbc2b500, source=0x7fffdbc2e7c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/bltinmodule.c:1033
#147 builtin_exec (module=<optimized out>, args=<optimized out>, nargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/clinic/bltinmodule.c.h:396
#148 0x000055555568dffd in cfunction_vectorcall_FASTCALL (func=0x7ffff78f1590, args=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/methodobject.c:422
#149 0x00005555556b1dee in PyVectorcall_Call (callable=0x7ffff78f1590, tuple=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:199
#150 0x000055555573647d in PyCFunction_Call (kwargs=0x7fffdbc2b640, args=0x7fffdbc2df00, func=0x7ffff78f1590) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:774
#151 do_call_core (kwdict=0x7fffdbc2b640, callargs=0x7fffdbc2df00, func=0x7ffff78f1590, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4983
#152 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#153 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffde8ac040) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#154 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7ffff719dd88, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78fa440, qualname=0x7ffff78fa440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#155 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7ffff719dd70, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#156 0x0000555555735223 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff719dd70, callable=0x7ffff790b4c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#157 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#158 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#159 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff719dbe0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#160 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#161 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#162 0x000055555573083d in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x555555fed230, callable=0x7ffff78b15e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#163 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#164 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3486
#165 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x555555fed0b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#166 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#167 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#168 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd2ef5f0, callable=0x7ffff790d0d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#169 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#170 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#171 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2ef440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#172 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#173 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#174 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd2ef7d8, callable=0x7ffff7910310) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#175 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#176 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#177 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2ef640) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#178 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#179 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#180 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=2, args=0x7fffffffa270, callable=0x7ffff79103a0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#181 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#182 object_vacall (base=<optimized out>, callable=0x7ffff79103a0, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#183 0x00005555556dfc35 in _PyObject_CallMethodIdObjArgs (obj=0x0, name=0x5555558b6e60 <PyId__find_and_load.15117>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1244
#184 0x0000555555679dd6 in import_find_and_load (abs_name=0x7fffdbc2a260) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1698
#185 PyImport_ImportModuleLevelObject (name=0x7fffdbc2b2f0, globals=<optimized out>, locals=<optimized out>, fromlist=0x7fffdcffc400, level=1) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1798
#186 0x0000555555733be6 in import_name (level=0x5555558e8d00 <small_ints+192>, fromlist=0x7fffdcffc400, name=0x7fffdbc2b2f0, f=0x7fffdc199d40, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5139
#187 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:2993
#188 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdc199d40) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#189 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=0, kwstep=2, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#190 0x000055555574a8ca in PyEval_EvalCodeEx (closure=0x0, kwdefs=0x0, defcount=0, defs=0x0, kwcount=0, kws=0x0, argcount=0, args=0x0, locals=0x7fffdbc28d00, globals=0x7fffdbc28d00, _co=0x7fffdbc279d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4327
#191 PyEval_EvalCode (locals=0x7fffdbc28d00, globals=0x7fffdbc28d00, co=0x7fffdbc279d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:718
#192 builtin_exec_impl.isra.14 (locals=0x7fffdbc28d00, globals=0x7fffdbc28d00, source=0x7fffdbc279d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/bltinmodule.c:1033
#193 builtin_exec (module=<optimized out>, args=<optimized out>, nargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/clinic/bltinmodule.c.h:396
#194 0x000055555568dffd in cfunction_vectorcall_FASTCALL (func=0x7ffff78f1590, args=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/methodobject.c:422
#195 0x00005555556b1dee in PyVectorcall_Call (callable=0x7ffff78f1590, tuple=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:199
#196 0x000055555573647d in PyCFunction_Call (kwargs=0x7fffdbc28e00, args=0x7fffdbc221c0, func=0x7ffff78f1590) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:774
#197 do_call_core (kwdict=0x7fffdbc28e00, callargs=0x7fffdbc221c0, func=0x7ffff78f1590, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4983
#198 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#199 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2d38b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#200 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7fffdc4e6aa8, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78fa440, qualname=0x7ffff78fa440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#201 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7fffdc4e6a90, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#202 0x0000555555735223 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdc4e6a90, callable=0x7ffff790b4c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#203 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#204 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#205 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdc4e6900) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#206 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#207 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#208 0x000055555573083d in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd2d7790, callable=0x7ffff78b15e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#209 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#210 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3486
#211 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2d7610) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#212 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#213 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#214 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd2efbf0, callable=0x7ffff790d0d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#215 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#216 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#217 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2efa40) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#218 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#219 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#220 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffdd2ecf68, callable=0x7ffff7910310) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#221 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#222 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#223 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7fffdd2ecdd0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#224 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#225 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#226 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=2, args=0x7fffffffb060, callable=0x7ffff79103a0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#227 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#228 object_vacall (base=<optimized out>, callable=0x7ffff79103a0, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#229 0x00005555556dfc35 in _PyObject_CallMethodIdObjArgs (obj=0x0, name=0x5555558b6e60 <PyId__find_and_load.15117>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1244
#230 0x0000555555679dd6 in import_find_and_load (abs_name=0x7ffff77a23b0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1698
#231 PyImport_ImportModuleLevelObject (name=0x7ffff77a23b0, globals=<optimized out>, locals=<optimized out>, fromlist=0x5555558bed60 <_Py_NoneStruct>, level=0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1798
#232 0x0000555555733be6 in import_name (level=0x5555558e8ce0 <small_ints+160>, fromlist=0x5555558bed60 <_Py_NoneStruct>, name=0x7ffff77a23b0, f=0x7ffff77801d0, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5139
#233 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:2993
#234 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77801d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#235 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=0, kwstep=2, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#236 0x000055555574a8ca in PyEval_EvalCodeEx (closure=0x0, kwdefs=0x0, defcount=0, defs=0x0, kwcount=0, kws=0x0, argcount=0, args=0x0, locals=0x7ffff779f340, globals=0x7ffff779f340, _co=0x7ffff77aa0e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4327
#237 PyEval_EvalCode (locals=0x7ffff779f340, globals=0x7ffff779f340, co=0x7ffff77aa0e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:718
#238 builtin_exec_impl.isra.14 (locals=0x7ffff779f340, globals=0x7ffff779f340, source=0x7ffff77aa0e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/bltinmodule.c:1033
#239 builtin_exec (module=<optimized out>, args=<optimized out>, nargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/clinic/bltinmodule.c.h:396
#240 0x000055555568dffd in cfunction_vectorcall_FASTCALL (func=0x7ffff78f1590, args=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/methodobject.c:422
#241 0x00005555556b1dee in PyVectorcall_Call (callable=0x7ffff78f1590, tuple=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:199
#242 0x000055555573647d in PyCFunction_Call (kwargs=0x7ffff779f440, args=0x7ffff7783440, func=0x7ffff78f1590) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:774
#243 do_call_core (kwdict=0x7ffff779f440, callargs=0x7ffff7783440, func=0x7ffff78f1590, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4983
#244 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#245 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77a01f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#246 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7ffff784bc68, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78fa440, qualname=0x7ffff78fa440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#247 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7ffff784bc50, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#248 0x0000555555735223 in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff784bc50, callable=0x7ffff790b4c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#249 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#250 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3469
#251 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff784bac0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#252 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#253 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#254 0x000055555573083d in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff77fa1c0, callable=0x7ffff78b15e0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#255 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#256 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3486
#257 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77fa040) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#258 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#259 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#260 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff77fc7f0, callable=0x7ffff790d0d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#261 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#262 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#263 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77fc640) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#264 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#265 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#266 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff7866998, callable=0x7ffff7910310) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#267 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#268 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#269 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff7866800) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#270 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#271 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#272 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=2, args=0x7fffffffbe50, callable=0x7ffff79103a0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#273 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#274 object_vacall (base=<optimized out>, callable=0x7ffff79103a0, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#275 0x00005555556dfc35 in _PyObject_CallMethodIdObjArgs (obj=0x0, name=0x5555558b6e60 <PyId__find_and_load.15117>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1244
#276 0x0000555555679dd6 in import_find_and_load (abs_name=0x7ffff779f230) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1698
#277 PyImport_ImportModuleLevelObject (name=0x7ffff779f230, globals=<optimized out>, locals=<optimized out>, fromlist=0x0, level=0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1798
#278 0x0000555555726088 in builtin___import__ (self=<optimized out>, args=<optimized out>, kwds=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/bltinmodule.c:279
#279 0x000055555568ecae in cfunction_call_varargs (func=0x7ffff78f1130, args=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:742
#280 0x0000555555735f58 in PyCFunction_Call (kwargs=0x7ffff779f280, args=0x7ffff77f74c0, func=0x7ffff78f1130) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:772
#281 do_call_core (kwdict=0x7ffff779f280, callargs=0x7ffff77f74c0, func=0x7ffff78f1130, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4983
#282 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#283 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77a0040) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#284 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7ffff786fe00, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78fa440, qualname=0x7ffff78fa440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#285 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7ffff786fdf0, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#286 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff786fdf0, callable=0x7ffff790b4c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#287 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#288 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#289 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff786fc40) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#290 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#291 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#292 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff7866b88, callable=0x7ffff7910310) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#293 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#294 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#295 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff78669f0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#296 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#297 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#298 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=2, args=0x7fffffffc680, callable=0x7ffff79103a0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#299 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#300 object_vacall (base=<optimized out>, callable=0x7ffff79103a0, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#301 0x00005555556dfc35 in _PyObject_CallMethodIdObjArgs (obj=0x0, name=0x5555558b6e60 <PyId__find_and_load.15117>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1244
#302 0x0000555555679dd6 in import_find_and_load (abs_name=0x7ffff779f170) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1698
#303 PyImport_ImportModuleLevelObject (name=0x7ffff779f170, globals=<optimized out>, locals=<optimized out>, fromlist=0x0, level=0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1798
#304 0x0000555555726088 in builtin___import__ (self=<optimized out>, args=<optimized out>, kwds=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/bltinmodule.c:279
#305 0x000055555568ecae in cfunction_call_varargs (func=0x7ffff78f1130, args=<optimized out>, kwargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:742
#306 0x0000555555735f58 in PyCFunction_Call (kwargs=0x7ffff785f700, args=0x7ffff77f7130, func=0x7ffff78f1130) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:772
#307 do_call_core (kwdict=0x7ffff785f700, callargs=0x7ffff77f7130, func=0x7ffff78f1130, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4983
#308 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3559
#309 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff7896c10) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#310 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x7ffff77e4e00, kwcount=0, kwstep=1, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x7ffff78fa440, qualname=0x7ffff78fa440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#311 0x000055555571d574 in _PyFunction_Vectorcall (func=<optimized out>, stack=0x7ffff77e4df0, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:435
#312 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff77e4df0, callable=0x7ffff790b4c0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#313 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#314 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#315 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77e4c40) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#316 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#317 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#318 0x0000555555730a9b in _PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff77fc1d8, callable=0x7ffff7910310) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#319 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x555555910f90) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4963
#320 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:3500
#321 0x000055555571d197 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff77fc040) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#322 function_code_fastcall (globals=<optimized out>, nargs=<optimized out>, args=<optimized out>, co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:283
#323 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:410
#324 0x00005555556952e6 in _PyObject_Vectorcall (kwnames=0x0, nargsf=2, args=0x7fffffffceb0, callable=0x7ffff79103a0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:127
#325 _PyObject_FastCall () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Include/cpython/abstract.h:147
#326 object_vacall (base=<optimized out>, callable=0x7ffff79103a0, vargs=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1186
#327 0x00005555556dfc35 in _PyObject_CallMethodIdObjArgs (obj=0x0, name=0x5555558b6e60 <PyId__find_and_load.15117>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Objects/call.c:1244
#328 0x0000555555679dd6 in import_find_and_load (abs_name=0x7ffff77f5580) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1698
#329 PyImport_ImportModuleLevelObject (name=0x7ffff77f5580, globals=<optimized out>, locals=<optimized out>, fromlist=0x7ffff789bdc0, level=0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/import.c:1798
#330 0x0000555555733be6 in import_name (level=0x5555558e8ce0 <small_ints+160>, fromlist=0x7ffff789bdc0, name=0x7ffff77f5580, f=0x7ffff786f440, tstate=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:5139
#331 _PyEval_EvalFrameDefault (f=<optimized out>, throwflag=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:2993
#332 0x000055555571bff0 in PyEval_EvalFrameEx (throwflag=0, f=0x7ffff786f440) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:741
#333 _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=0, kwstep=2, defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4298
#334 0x000055555571d863 in PyEval_EvalCodeEx (closure=0x0, kwdefs=0x0, defcount=0, defs=0x0, kwcount=0, kws=0x0, argcount=0, args=0x0, locals=<optimized out>, globals=<optimized out>, _co=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:4327
#335 PyEval_EvalCode (co=<optimized out>, globals=<optimized out>, locals=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/ceval.c:718
#336 0x0000555555790d42 in run_eval_code_obj (co=0x7ffff789f660, globals=0x7ffff78d2940, locals=0x7ffff78d2940) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/pythonrun.c:1125
#337 0x00005555557a3b32 in run_mod (mod=<optimized out>, filename=<optimized out>, globals=0x7ffff78d2940, locals=0x7ffff78d2940, flags=<optimized out>, arena=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/pythonrun.c:1147
#338 0x00005555557a6c1e in PyRun_FileExFlags (fp=0x5555558ed440, filename_str=<optimized out>, start=<optimized out>, globals=0x7ffff78d2940, locals=0x7ffff78d2940, closeit=1, flags=0x7fffffffd3d8) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/pythonrun.c:1063
#339 0x00005555557a6e09 in PyRun_SimpleFileExFlags (fp=0x5555558ed440, filename=<optimized out>, closeit=1, flags=0x7fffffffd3d8) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Python/pythonrun.c:428
#340 0x00005555557a72be in pymain_run_file (cf=0x7fffffffd3d8, config=0x555555911780) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Modules/main.c:387
#341 pymain_run_python (exitcode=0x7fffffffd3d0) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Modules/main.c:612
#342 Py_RunMain () at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Modules/main.c:691
#343 0x00005555557a74b9 in Py_BytesMain (argc=<optimized out>, argv=<optimized out>) at /home/conda/feedstock_root/build_artifacts/python_1596159904097/work/Modules/main.c:1123
#344 0x00007ffff7d91002 in __libc_start_main () from /usr/lib/libc.so.6
#345 0x000055555574ac15 in _start ()
(gdb) quit
A debugging session is active.

	Inferior 1 [process 25673] will be killed.


⚠ DesiTorrents has degraded performance

In b1be037, DesiTorrents ($DST) experienced degraded performance:

HTTP code: 200
Response time: 2255 ms

Resolved: DesiTorrents performance has improved in 232f9c4.


Periodically tweet call to actions

On some sort of schedule we should tweet different calls to action:

How many overall contributions are available
Particular repos and the number of issues

As we gather analytics we could additionally tweet:

Which repos are very action
Which repos need attention

There's a lot of room for discussion here, what are we thinking?

I think there could be an alternative approach with the goals of this. We want to alert people for opportunities to contribute in multiple areas as well as with multiple amounts of complexity.
What if we made a family of bots that segmented this in a way that made individualized feeds people could subscribe to that fit their needs and where they were at?
We could have one for overall documentation, one for beginner stuff and low hanging fruit, and one for larger more advanced gigs. We could also have one that shares articles and links that might end up being in the newsletter to get more subscribers involved etc.
I think by segmenting this out into more fine-grained spickets for people to follow would help engagement and lower the  signals-to-noise ratio.
Thoughts?

I'm not thrilled about the idea of having multiple twitter accounts and bots all tweeting a filtered set.  If I subscribe to the beginner bot and grow as a developer I need to unfollow one bot and follow a new one, this seems like a hurdle rather than a feature.  Furthermore, we risk having accounts sit dormant because there simplify aren't issues for those.  If we get to the point that we're making 1000 tweets a day it may make sense to look at how to decrease the noise but for now it seems like a premature optimization that introduces complexity.
The point of the tweets is to call attention to certain issues (those projects that are 100% committed to our labels etc) and general calls to action "We've seen 100 new opportunities this week, looking to get involved?  Check them out at .....".
The most important thing we can do at this particular moment is focus on getting out the MVP and validating people like the idea.  I'm afraid if we start to tack too much we're not going to see the project launch.

Copying this over from @ybur-yug's comments elsewhere:
Implementation

[ ] tweet how many opportunities overall are available and projects + their counts of opportunities individually

[ ] make an opportunity (issue) belong to a project (github repo)
[ ] set up client that exposes interface to all opportunities

[ ] set up interface in client that exposes a project + its opportunities loaded for CTA tweet

[ ] set up scheduler genserver for call to action tweets

[ ] determine template for tweets
[ ] determine interval in which to tweet various messages

Notes
It may be work considering extracting the periodic behaviour of the scheduler right now into its own module that just takes a module/function/arguments and then runs them in the task supervisor like we do in the bot with Github issues now.
I have started the work and got twitters interface exposed and am working on getting the periodic part done after that. Next up will be the schema work, followed by finally figuring out the final copy of the real tweets and then also when to send them out.

Let's hold off on this for now.


failure after reboot

Klaus. I have the simple flow below....
[{"id":"8515c712.d09718","type":"modbus-read","z":"1df3eb3.add9115","name":"PLC Read","showStatusActivities":false,"unitid":"","dataType":"HoldingRegister","adr":"0","quantity":"5","rate":"2","rateUnit":"s","server":"fcde1705.2791e8","x":148.75,"y":199.75,"wires":[["27e42ae6.332f96"],[]]},{"id":"27e42ae6.332f96","type":"modbus-write","z":"1df3eb3.add9115","name":"Arduino Write","showStatusActivities":false,"unitid":"","dataType":"MHoldingRegisters","adr":"0","quantity":"5","server":"f268b976.c7d2","x":423.75,"y":194.75,"wires":[[],[]]},{"id":"fcde1705.2791e8","type":"modbus-client","z":"","name":"TCP","clienttype":"tcp","bufferCommands":true,"stateLogEnabled":false,"tcpHost":"<IP_ADDRESS>","tcpPort":"502","serialPort":"/dev/ttyUSB","serialType":"RTU-BUFFERD","serialBaudrate":"9600","serialDatabits":"8","serialStopbits":"1","serialParity":"none","serialConnectionDelay":"100","unit_id":"1","commandDelay":"1","clientTimeout":"1000","reconnectTimeout":"2000"},{"id":"f268b976.c7d2","type":"modbus-client","z":"","name":"buffered","clienttype":"simpleser","bufferCommands":true,"stateLogEnabled":false,"tcpHost":"<IP_ADDRESS>","tcpPort":"502","serialPort":"/dev/ttyAMA0","serialType":"RTU-BUFFERD","serialBaudrate":"9600","serialDatabits":"8","serialStopbits":"1","serialParity":"none","serialConnectionDelay":"100","unit_id":"1","commandDelay":"1","clientTimeout":"1000","reconnectTimeout":"2000"}]
If I build this from new, it works fine until I reboot the RPI, then the serial connection fails (I am guessing) and get the error message 'FSM not ready to write'.
If I delete everything associated with this flow, serial configurations, TCP configurations and start again as if it was a fresh install of node-red-contrib-modbus and rebuild all of the nodes, it works again until I reboot the RPI. I have 'upgraded and updated' and I still have the same issue.
Once working it will work for days without any problems....no timeouts...nothing. The reboot is causing the failure.
The RPI is reading 5 registers at 2 second intervals from a PLC and sending the result to a XBEE radio connected to the serial port (GPIO) on the RPI. When I have had it working, I have pushed the amount of data to much higher levels without any issues. I have reduced the amount of data here purely to remove 'volume of data' as being a possibility of cause of the failure.
Any clues????

Do you use v1.0.8? Which node.js version is it? How about --unsafe-perm --build-from-source options on npm install?

Did you try just to deploy a bit later instead of rebuilding the whole flow? Does it work, if you push Deploy a minute after reboot? Maybe it takes a bit time to connect the TTY.


Dockerizing proposal

I have a number of proposals that I can make to this project:

Two-stage build in docker. In this way, we will have a build in a reproducible environment.
Optimized linking as much as possible. The image is required for production use. At high loads, even minor optimizations save resources.
Build plugin along with Jaeger source code. In this way, we will influence the optimization of the building of Jaeger. We can use cache or saved docker levels to speed up building.
Use Debian releases instead of Alpine distributive. One of the optimizations is linking with system libraries. Alpine has limited multithreading functionality due to the use of musl instead of libc. But there is no problem supporting both distributions.
Image versioning that includes the Jaeger version, the plugin version, and the label that this container contains the plugin. The same approach is used by snyk. For example, the image will have the following tags:

ghcr.io/jaegertracing/jaeger-collector:1.29.0-clickhouse-0.8.0-stretch
ghcr.io/jaegertracing/jaeger-collector:1.29.0-clickhouse-0.8.0
ghcr.io/jaegertracing/jaeger-collector:1.29.0-clickhouse
ghcr.io/jaegertracing/jaeger-collector:clickhouse-0.8.0-stretch
ghcr.io/jaegertracing/jaeger-collector:clickhouse-0.8.0
ghcr.io/jaegertracing/jaeger-collector:clickhouse

Have a complete set of images of own production: all-in-one, jaeger-agent, jaeger-collector, jaeger-ingester, jaeger-query.
Run E2E-tests using docker-compose. example.

The implementation of part of the above can be found in this project https://github.com/levonet/docker-jaeger.
I'm ready to move this infrastructure and do support by my team during the time of using Jaeger.

For 3/5/6, the jaeger-clickhouse plugin is a standalone binary that gets executed as a subprocess of Jaeger, which is then communicated with over gRPC. As a result of this design, the docker image really only needs to hold the jaeger-clickhouse binary itself, which can then be mounted into the Jaeger container's filesystem. For example see handling in jaeger-operator that sets up access to the binary using an initContainer. Switching to a model of including both Jaeger and the plugin in the same image would require first updating this existing gRPC handling in jaeger-operator to support the combined image structure. There's also be the loss of independent versioning between the plugin itself and Jaeger in a production deployment - e.g. when testing changes to the plugin I am able to pair an arbitrary plugin version with an arbitrary Jaeger version.
But there are still a couple other issues in the existing structure that you point out:

The current Dockerfile copies in a built binary that was created externally on the host, rather than via a controlled builder image. Like you point out in item (1) this makes it harder to get reproducible builds.
As you pointed out in item (4), once the binary is being built internally, it should probably use Debian rather than Alpine for the base images. The current use of Alpine effectively doesn't matter that much since the binary isn't actually being built nor run in that Alpine environment. If we were going to extremes, the main stage's base image could technically be scratch, except that it's often useful to keep the linux utilities available for debugging etc. But once a builder stage has been added, we'd probably want to make sure that there isn't a risk of weird libc vs musl conflicts when the imported plugin binary is being run from a jaeger image.

So I think the main changes to address items 1 and 4 would be:

Update the current Dockerfile (and Makefile) to have an in-docker build stage, instead of doing the build on the host system.
Switch the base images in the Dockerfile from Alpine to Debian, just to reduce the risk of weird musl issues later.
Future/extra credit: Once the build is being done in a docker stage, it should be pretty straightforward to have multiarch builds with architecture coverage on par with the main Jaeger docker images.

Does this make sense?


Fix observable trajectories on HDF5 load

When loading a SimulationResult from an HDF5 file using the .load()
method, observables are left as zeroes (if the observables were not
saved with the results using the include_obs_exprs=True argument).
The issue is due to the fact that Observable species and coefficients
are not saved in the JSON representation, despite the
include_netgen=True argument. This was missed due to a typo in the
SimulationResult unit tests, also fixed here.
This PR addresses the issue by calculating the Observable species
and coefficients using PySB's built-in pattern matching during
the JSON import process. This approach allows existing HDF5 files
to be used without resaving.

Shouldn't the JSON export code be saving observable species and coefficients when include_netgen=True? Or do we always want to regenerate that information on load? Any accidental divergence in our pattern matcher vs. the BNG observable generation logic will lead to hard-to-diagnose issues when reloading these JSON dumps.

Codecov Report

Merging #502 into master will decrease coverage by 0.06%.
The diff coverage is 58.62%.

@@            Coverage Diff             @@
##           master     #502      +/-   ##
==========================================
- Coverage   80.26%   80.20%   -0.07%
==========================================
  Files         101      101
  Lines       10602    10629      +27
==========================================
+ Hits         8510     8525      +15
- Misses       2092     2104      +12

Impacted Files
Coverage Δ

pysb/importers/json.py
85.15% <36.84%> (-8.49%)
:arrow_down:

pysb/export/json.py
98.88% <100.00%> (+0.07%)
:arrow_up:

pysb/tests/test_exporters.py
94.50% <100.00%> (+0.18%)
:arrow_up:

pysb/tests/test_simulationresult.py
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Cool. Will approve after CI passes.


Add logging around nats disconnects

We are seeing intermittent unresponsive agents in following scenario:

upgrade bosh-director
right after upgrade is finished, start recreating a deployment

In some situations there seems to be a race condition in between

the old director going down
Agent on the VM retries sending heartbeets
new director comes up and get's deploy command issued. Sends ping to agent
???? Currently unclear why at this point the agent has not yet reconnected to nats
director doesn't get a response and the deploy command fails.

the issue has been solved in CI by introducing a 3 minute wait after the upgrade
of the director but we would like to get to the root cause of this. It seems that if
the agent DID NOT crash and restart within BOSHs downtime, the connection is
somehow in a bad state. Cunnie suggested it that the arp cache could be the
reason the connection is not successfully re-established after the director comes
back up.
Use nats client handler functions to add additional logging and an arp flush on
connection issues.
In case the arp cache should not be issue at hand, the introduced routine can be
changed to solve the actual problem
Tracker: [ 181274434, #181759211]

Related discussion here: https://github.com/cloudfoundry/bosh/discussions/2372

Should we first add logging as suggested in the title of this pr to understand the issue and afterwards try to fix it? It could be that adding the cleanup of the ARP cache can hide the real issue.

Not sure if it really could hide other Issues. We know that  on a recereate the Mac Address on the Director will change and consequent comms will fail until the clients will renew that IP-Mac mapping. I don't think it will hide any other Issues but it will definitely solve one we're aware of.

I updated the maxReconnectWait to match the default 45 seconds from
https://github.com/cloudfoundry/bosh/blob/main/src/bosh-director/lib/bosh/director/agent_client.rb#L44
This is to avoid having a reconnectWait longer than the director would wait for the agent to respond in the following sequence of events:
t0s: Current wait: 32s. agent reconnect fails. wait-> 64s
t10s: bosh is fully up, receives deploy command, pings agent
t55s: bosh gives up waiting for agent
t64s: agent tries to reconnect but it's too late for the deployment.


Option to always use privacy mode

Depends on #7
Let user set an option that defaults privacy mode to on

This was fixed by commit d35da


Bitcoin Node - Private appears non-functional

The Private Bitcoin Client functionality appears to not work.  No matter what info is put in the URL section the response is always "Network Error".  The URL I'm pointing Caravan at can return the correct output from this command:
curl --user my_uname --data-binary
'{"jsonrpc": "1.0", "id":"curltest", "method": "getblockcount", "params": [] }'
-H 'content-type: text/plain;' --resolve bitcoind.localhost:8080:<IP_ADDRESS> http://bitcoind.localhost:8080
I'm wondering if additional configuration needs to be required to give Caravan network capabilities.  I monitor the traffic from Caravan and I don't see any network traffic when I use the "Test Connection" button.

[ ] Regression (a behavior that used to work and stopped working in a new release)
[X] Bug report
[ ] Feature request
[ ] Documentation issue or request
[ ] Support request

Expected Behavior
Test Connection should return successful with a proper Cors configured Bitcoin node.
Current Behavior
Network Error is returned every time.
Possible Solution
Steps to Reproduce (for bugs)
Any attempt to test the connection to the Cors enabled Bitcoin node returns Network Error.  I've confirmed that my Cors configuration is working correctly with the Curl test.
Environment
This fails with either of these configurations:
bitcoind + configured Nginx + Caravan on the same machine
or
bitcoind + configured Nginx on one machine and Caravan access Nginx (Cors) from another machine

Where are you running caravan:  On desktop or machine running Bitcoind and Nginx with Cors configuration

Operating system: Linux - Ubuntu 20.04

Browser and version: <!-- Chrome or Firefox, current versions
If you are running caravan locally please run the following
and paste your output below:

caravan: 0.3.5
npm: 6.14.4
node: v10.19.0
Linux 5.4.0-65-generic #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

Yeah same here, I can successfully curl to a remote machine on my local network running bitcoind, as per the two test's in the readme.  However, when testing connection, using the address "http://bitcoind.192.x.x.x:8080" (as per curl test) it comes up with network error.

Inspecting the page I get the following error in firefox -
Loading mixed in Request (insecure) active content "http://bitcoind.192.168.1.xxx:8080/" xhr.js :177 (yellow text and box) \ Loading mixed in Request (insecure) active content "http://bitcoind.192.168.1.xxx:8080/" localhost :3000 (yellow text and box) \ Cross-Origin Request Blocked: The Same Origin Policy disallows reading the remote resource at http://bitcoind.192.168.1.xxx:8080/. (Reason: CORS request did not succeed). (red text and box)
I have also tried with self signed certificates, i can CURL using --insecure flag and IP https://bitcoind.192.168.1.xxx:8080/, when doing it firefox i only receive the Red Text CORS fault as described above

This appears to be a CORS issue which is documented in the README. You need to proxy requests to your full-node and inject the right headers there (or just use a script). See this for an nginx solution (or search for "CORS" in the README). https://github.com/unchained-capital/caravan/blob/master/bitcoind.proxy

I'm going to close this ticket.  The private node functionality does work for either testnet or mainnet.  I'm currently using Caravan version 1.0.1.  Perhaps my CORS configuration was broken when I originally opened this issue but I am now able to get "Connection Success!" on testnet or mainnet and I can import my addresses on mainnet.


Fix pages which return only one section (see Antrodemus)

Fixed. Issue was related to a divide by 0 problem.


DNS resolving

Environment

Calico/VPP version: v0.18.1-calicov3.21.2
Kubernetes version: v1.21.1
kind cluster

Issue description
Perhaps it is not related to vpp-dataplane directly, but I found this on Calico-VPP setup.
It seems that DNS resolving is not working properly. I created a simple alpine pod and want to install additional utilities. Called apk update but got an error.
To Reproduce
Steps to reproduce the behavior:

Prepare alpine.yaml:

---
apiVersion: v1
kind: Pod
metadata:
  name: alpine
  labels:
    app: alpine
spec:
  containers:
  - name: alpine
    securityContext:
      privileged: true
    image: alpine:3.15.0
    imagePullPolicy: IfNotPresent
    stdin: true
    tty: true

Apply: kubectl apply -f alpine.yaml
Go to the container: kubectl exec -it alpine sh

/ # apk update
fetch https://dl-cdn.alpinelinux.org/alpine/v3.15/main/x86_64/APKINDEX.tar.gz
ERROR: https://dl-cdn.alpinelinux.org/alpine/v3.15/main: temporary error (try again later)
WARNING: Ignoring https://dl-cdn.alpinelinux.org/alpine/v3.15/main: No such file or directory
fetch https://dl-cdn.alpinelinux.org/alpine/v3.15/community/x86_64/APKINDEX.tar.gz
ERROR: https://dl-cdn.alpinelinux.org/alpine/v3.15/community: temporary error (try again later)
WARNING: Ignoring https://dl-cdn.alpinelinux.org/alpine/v3.15/community: No such file or directory
2 errors; 14 distinct packages available

or
/ # ping dl-cdn.alpinelinux.org
ping: bad address 'dl-cdn.alpinelinux.org'

Additional context
If I run it with default CNI - it works.
If I add nameserver <IP_ADDRESS> to /etc/resolv.conf - it works.
ping <IP_ADDRESS> also always works.
Perhaps something needs to be specified in the configuration?
Thank you

Hi @glazychev-art , what is the status of the coredns pods?
kubectl -n kube-system get pods

Hi @AloysAugustin ,
It seems that everything is working fine:
NAME                                       READY   STATUS    RESTARTS   AGE
calico-kube-controllers-547686d897-shvjz   1/1     Running   1          77m
calico-node-czltm                          1/1     Running   0          77m
calico-node-mv968                          1/1     Running   2          77m
coredns-74ff55c5b-hwlwg                    1/1     Running   0          79m
coredns-74ff55c5b-lgw8p                    1/1     Running   0          79m
etcd-test-master                           1/1     Running   0          79m
kube-apiserver-test-master                 1/1     Running   0          79m
kube-controller-manager-test-master        1/1     Running   0          79m
kube-proxy-2fln5                           1/1     Running   0          79m
kube-proxy-jppr2                           1/1     Running   0          77m
kube-scheduler-test-master                 1/1     Running   0          79m

I can reproduce it not only on kind-cluster, but also on bare-metal on equinix.metal

@glazychev-art, could you try restarting network service?

@onong
Sorry, what exactly do I need to restart?

@glazychev-art
Restart the networking service on the nodes, ie, "systemctl restart systemd-networkd" or whatever is applicable.

@onong
Thank you. Yes, systemctl restart systemd-networkd is applicable.
But unfortunately it didn't help.

This is still a problem.
Additionally, I've run default Calico (without VPP dataplane) - ping and apk update work fine.
Configuration:
kubectl apply -f https://projectcalico.docs.tigera.io/v3.22/manifests/tigera-operator.yaml
kubectl apply -f installation-default.yaml
installation-default.yaml:
# This section includes base Calico installation configuration.
# For more information, see: https://projectcalico.docs.tigera.io/master/reference/installation/api#operator.tigera.io/v1.Installation
apiVersion: operator.tigera.io/v1
kind: Installation
metadata:
  name: default
spec: {}

---

# This section configures the Calico API server.
# For more information, see: https://projectcalico.docs.tigera.io/master/reference/installation/api#operator.tigera.io/v1.APIServer
apiVersion: operator.tigera.io/v1
kind: APIServer
metadata:
  name: default
spec: {}

Any new ideas?

I have similar problem. @sknat Could you please take a look?

@NikitaSkrynnik @glazychev-art
This should be addressed by https://github.com/projectcalico/vpp-dataplane/pull/627
can you check if this work for you ?
You'll need to set this in the configuration :
  CALICOVPP_INITIAL_CONFIG: |-
    {
      "vppStartupSleepSeconds": 1,
      "corePattern": "/var/lib/vpp/vppcore.%e.%p"
      "redirectToHostRules": [
        {
          "proto": "udp",
          "port": 53,
          "ip": "<IP_ADDRESS>"
        },
        {
          "proto": "tcp",
          "port": 53,
          "ip": "<IP_ADDRESS>"
        }
      ]
    }
kind: ConfigMap
metadata:
  name: calico-vpp-config
  namespace: calico-vpp-dataplane

Feel free to re-open the issue if this did not fix it


Tic-Tac-Toe

Tic-Tac-Toe is a zero-sum game, which means that if both players are playing their best, the game will end in a Tie.

@cleveranu I would like to work on it. Can u pls assign me this issue?

Is there any deadline for this project?

I am assigning you this issuse kindly complete it within 1 week.

No,but do it asap!

No,but do it asap!

Can I do a Android application or I have to do it using HTML,CSS and JS

No,but do it asap!

Can I do a Android application or I have to do it using HTML,CSS and JS

@cleveranu Can u pls tell whether an android or web application?

I am assigning you this issue kindly complete it within 1 week.

@cleveranu I have created a pr for tictactoe game.

Something is wrong with that file check once

@cleveranu can u pls help me out with the problem?


Removed preventDefault from controller (PreviewController.ts) and added preventDefault in data-action (preview.html)

Please describe the problem you're fixing here. Include the issue number, if applicable.
Made changes as per comment: https://github.com/wagtail/wagtail/pull/10356#issuecomment-2449240351 of #10356
Ref by @lb- on slack: https://wagtailcms.slack.com/archives/C03QEP69VEK/p1730361510013059
I tested myself, it is working pretty well after the changes.
Without preventDefault  and :prevent
https://github.com/user-attachments/assets/5c1a8e00-b35a-4c54-954e-a0170fe3a3dd
With only :prevent (Same as event.preventDefault() in openPreviewInNewTab)
https://github.com/user-attachments/assets/cd95bad4-7dd4-4e36-8b45-8b8f9afb58c4

@lb- I appreciate you handling the rebase and changelog for this one, and updating the unit tests, I’ll create new branches for future contributions.


Extension 1.3.2 - not worked on Android Emulator

HotReload nuget - 1.3.1
HotReload extension - 1.3.2
Android emulator - OS 9.0
Im use standard code init:
HotReloader.Current.Start(this);

launch emulator;

use adb comand:
adb forward tcp:8000 tcp:8000

run xamarin debug (F5)

In VS2019 change current view XAML

Press Alt + S

...

Not PROFIT. Hot reload not reload

don't post same issue several times. You already comment here that it doesn't work

@stanbav could you please validate this case?

I think it's bug

I can give you more information, but I don't know what

@scriptBoris you tested on android emulator, didn't you?

@AndreiMisiukevich, yeah, android emulator

@scriptBoris this is a bug. Need to find the reason.

@AndreiMisiukevich In the mean time is it possible to download the older version of the vsix file from somewhere?

@AndreiMisiukevich In the mean time is it possible to download the older version of the vsix file from somewhere? Unlike Nuget I guess you can't download an older version from visual studio market place.

@uksreejith unfortunately not. But probably we will create archives and put them on github.
Sorry

@AndreiMisiukevich I think downgrade self extension & nuget package. Need link for download backup

@scriptBoris there is no such possibility on these stupid stores ))
nuget - yes
extension - no

@AndreiMisiukevich from my HDD.
HotReload Extension 1.2:
https://drive.google.com/file/d/1fBacG2enN8e0lOU_aA-O392_RWGz7ps7/view?usp=sharing
IMPORTANT
Disable autoupdate vs extension!

@scriptBoris why is it strange ? )

nuget package is backward compatible with all version of extensions

btw, i recommend to stay on nuget version 1.3.1

@AndreiMisiukevich yeah, im use newest nuget package - no problem.

@AndreiMisiukevich from my HDD.
HotReload Extension 1.2:
https://drive.google.com/file/d/1fBacG2enN8e0lOU_aA-O392_RWGz7ps7/view?usp=sharing
IMPORTANT
Disable autoupdate vs extension!
P.S. Im install extension v1.2 and HotReload is reload XAML code. Strange things, but worked...

Hi, some old extension for VS for mac? Thanks

@move3a oh sorry, i dont have hardware with MacOS

Hey guys, i'm going to resolve this issue today

Hi!
The same happens when the Device is used, if the port is 15001 the changes are not reflected, but with port 15000, everything works.
I'm using
HotReload Nuget - 1.3.1
HotReload Extension - 1.3.2
Device Motorola G6 - Android 9.0

fix will be delivered a little bit later and will require NUGET package update

@AndreiMisiukevich
HotReloader.Current.Start() is Deprected?

@AndreiMisiukevich
HotReloader.Current.Start() is Deprected?

yes, thank you for good question. Yes since now you should use Run

1.4.6 is already delivered and should fix this issue.
Don't forget abou adb forward :)

@AndreiMisiukevich The latest version of nuget(1.4.6) is working fine on emulator also. Thanks for the quick fixes and this awesome project. Keep up the good work!

@uksreejith thank you :) the best way to give thanks - repost message about HotReload to broadcast info to as many people as we can :) https://twitter.com/Andrik_Just4Fun

@AndreiMisiukevich
now with this new version (1.4.6) everything works perfectly! Thanks for the quick fix and for this amazing tool!

@AndreiMisiukevich Excelent, works fine vs for mac.
¡¡Good Job!!

Great guys )


Add caves biome

FastNoise looks somewhat like this (threshold setting is on my branch):

Could also maybe do a wibbly wobbly one but wanted to see how this went as it gives decent range for combat.
:cl:

add: Added a caves biome.

I have been informed it's skringly dingly so will redo

skringly dingly

You may get better results by taking perlin or simplex noise and carving out hallways when the output value is between 0.3 and 0.6 (with the output adjusted from the range (-1,1) to the range (0, 1))


feat: #179 Front end integration with Forest Client API

Thanks Nick! Overall looks good!! Just think we should update the "frontend/public/env.json" file to get the latest cognito credentials for local

And just in the case the forest client number is not correct, the "verify" button is not aligned with the "forest client" input box

Hi @NickSaglioni , could you just have a brief description at this PR, currently it is blank. I am not sure if that would not show up anything at release log...
Thank you.


AAD Template deployment fails due to issue in Trustkeystore validation command

AAD Template deployment fails due to issue in Trustkeystore validation.
The issue is due to a typo issue in the command used for trust keystore validation in aadIntegration.sh
#validate Trust keystore
sudo ${JAVA_HOME}/bin/keytool keytool -list -v -keystore ${DOMAIN_PATH}/${wlsDomainName}/keystores/trust.keystore -storepass ${customTrustKeyStorePassPhrase} -storetype ${customTrustKeyStoreType} | grep 'Entry type:' | grep 'trustedCertEntry'

Closing this issue as it is resolved.


Virtual repos

Re-implementation using the idea of protocols as part of the path to determine behavior. This should limit the tendrils and prove a path toward implementing a virtual filesystem as well.
Inspiration comes from VS Code's implementation of a virtual filesystem.
This could maybe be a reasonable path toward getting this upstream.
Also there's going to be a lot of work to get all the filesystem calls to go through some sort of translation layer :-/
First pass I'll probably try changing the imports and seeing if we can implement the interface for the virtual protocol.

This is working for happy paths pretty well. Probably want to work on the server-side next (it's the biggest pain point), before cleaning this up and making it easier to track upstream...and maybe discuss the idea of getting this into upstream (but we're months out from that).


[BEAM-7481] Revert "[BEAM-5878] add tests for kwonly args in python 3"

Reverts apache/beam#8505
Reason: breaks postcommit tests.

R: @apilloud
cc: @Juta @aaltay

LGTM.

Pure rollback to fix build break. Merging now.


Storage async tests + aiohttp as default

Make aiohttp the default async transport. Import is done locally, so customer can provide their own transport if they want if they donùt have aiohttp installed
Async tests recording using aiohttp POC.

(message created by the CI based on PR content)

Merging since tests code is impacted only, so I don't expect a lot of review needed


The Dark Mode background

What's the style idea?
We need to improve the fonts of writings so that they pop out in both dark as well as light modes.
Checklist

[X] I have checked the existing issues.
[X] I follow Contributing Guidelines of this project.
[ ] I am a IWOC'24 contributor.
[ ] I am a JWOC'24 contributor.
[ ] I am a SWOC'24 contributor.

Code of Conduct

[X] I agree to follow this project's "Code of Conduct".

Kindly assign me this under GSSOC'24

What do you want to improve, pls provide the supporting screenshot @KaushikaDash04


Add spaceship project

Adds a section about the ionic mobile app that will be developed in the near future.

@Splaktar Do you want to merge this PR?

Looks good, but I don't have access to merge PRs in this project.


Passive/Active Buzzer Support

Most likely a PWM component, possibly a multipin PWM.

[ ] Need a Controller class with separate functions for Active and Passive Buzzer
[ ] Need a Helper class that should have functions for Buzz, BuzzStop, changeFrequency etc,.

Implementing the two buzzers separately


Fix CMake target name for abseil

It looks like a typo.

ImosCI, test this please.

Jenkins CI test (for commit 1c58c1bbfc8ec92ce6c7fea8e5a368595f887fed, target branch master) failed with status FAILURE.

Jenkins issue seems unrelated. Merging.


unicorn:model clearing field when request sent

Overview
I'm working on integrating Unicorn into a new project and want to build a form with it to remove the need for lots of JS.
I'm in the very early stages of setup and I'm running into an error with unicorn:model.
When typing in an input field unicorn sends a request as is intended. However, once the request is sent ( or received, I'm not too sure) the input which I have added unicorn:model="name" to the field gets emptied.
See the gif below for a demo of what's happening. I used the example Hello World component from the docs to create the example.

The request seems okay:
Payload
{
    "id":"RGxVEpbj",
    "data":{"name":"World"},
    "checksum":"GnzWzCxX",
    "actionQueue":[
        {
            "type":"syncInput",
            "payload":{"name":"name","value":"s"},
            "partials":[]
        }
    ],
    "epoch":1661435333662,
    "hash":"nRebsfnn"
}

Response
{
    "id":"RGxVEpbj",
    "data":{
        "name":"Worlds"
    },
    "errors":{},
    "calls":[],
    "checksum":"A4EcPwSS",
    "dom":"<div unicorn:checksum=\"A4EcPwSS\" unicorn:id=\"RGxVEpbj\" unicorn:key=\"\" unicorn:name=\"hello-world\">\n<input id=\"text\" type=\"text\" unicorn:model=\"name\"/><br/>\n    Hello S\n  </div>",
    "hash":"QXEkesKJ"
}

If you need any more information let me know! I'm sure this is user error rather than something wrong with the framework but I thought it worth reporting.

Edit: I just created a brand new project to test this out completely isolated and got the same result
Edit 2: It also happens when I have a forked version of this repo set up locally and use the built in examples in the local dev environment
Edit 3: This seems to be a Chrome specific bug as the code works fine in safari!

That's really strange. I do tend to use FireFox for all my development, but I just double-checked in Chrome (version 104.0.5112.101 on a M1 Mac) and it seems to be working ok for me.
https://user-images.githubusercontent.com/317045/187029976-c972ee65-63b5-454f-bfd9-18e3fe85dfa9.mp4
What version of Chrome are you on, what OS, and do you see any web dev console errors or anything? Sorry you are running into this problem!

One other thought: can you open a new new incognito window in Chrome and/or disable all plugins? I wonder if something weird is happening there?

Hey @adamghill, That actually seems to have solved the issue. It works just fine in an incognito window but after clearing the cache in my regular browser the issue persists. It also works fine in a different chrome profile.
My best guess at the moment is that it's clashing with a chrome extension I have installed so I'll try and deduce what it could be and report back!
Thanks so much for your help

OK! I know Bitwarden has caused issues in the past, too. I can take a look at Nordpass and see if I can figure out a way to prevent it from clobbering Unicorn.

Testing on http://localhost:8080/text-inputs with the Nordpass extension is interesting to see what breaks and what doesn't. 😂 I'm wondering if the issues might be related to morphdom which is the library I use to actual update the DOM under the hood. While I'm testing, it looks like using a key makes things work as expected.

Fantastic 😂 Glad to hear about the work around, means I can get on with my site! Thanks so much for your help with this. I'll just use a key for now, do you reckon this will be something that has to be fixed in morphdom or will a PR to this repo help get round it?

I don't think there has been an updated release of morphdom for the past 2 years from what I can tell on https://www.npmjs.com/package/morphdom. I could try to dig into the morphdom source, but I've also been looking at some other implementations similar to morphdom:

https://alpinejs.dev/plugins/morph
https://github.com/choojs/nanomorph
https://github.com/bigskysoftware/idiomorph

I have the same issue with Bitwarden.

While I'm testing, it looks like using a key makes things work as expected.

How did you manage to make it work ? I don't understand how to replace the model:search/updated_search() behavior with a key (same pattern as this example). I don't really understand how unicorn:key work and what it does.


Buy SOV page

BUY SOV v1.0.xd.zip
@creed-victor I am now working on the transaction flow

BUY SOV v1.0.xd.zip
@creed-victor I am working on the transaction flow for this now

obvs the "top up" button is disabled when no wallet is connected

BUY SOV v1.2.zip
@creed-victor

Uploading BUY SOV v1.2.zip…

@creed-victor yago just requested the "from" and "to"

BUY SOV v1.3.zip
@creed-victor obvs the numbers are just placeholders I do not know what any of the numbers are but will get yago to post this in the channel

BUY SOV v1.4.zip
https://xd.adobe.com/view/d101a739-8251-42cd-6b6f-6de79e4768b2-a730/

BUY SOV v1.4.zip
https://xd.adobe.com/view/d101a739-8251-42cd-6b6f-6de79e4768b2-a730/

Uploading BUY SOV v1.5.xd.zip…
@creed-victor with all the final images

BUY SOV v1.5.xd.zip


Acceptance Tests for #43 Delete Roles

User story
#43
Acceptance criteria checklist

[x] Administrators can delete roles from the system

[x] When a role is deleted, it is disassociated from all of its linked users.

Tested and approved.


NumeRe.NumeRe version <IP_ADDRESS>2

[x] Have you signed the Contributor License Agreement?
[x] Have you checked that there aren't other open pull requests for the same manifest update/change?
[x] This PR only modifies one (1) manifest
[x] Have you validated your manifest locally with winget validate --manifest <path>?

[ ] Does your manifest conform to the 1.5 schema?

Note: <path> is the name of the directory containing the manifest you're submitting.

Microsoft Reviewers: Open in CodeFlow

/AzurePipelines run

/AzurePipelines run

Hi @stephengillie, is there something, which I can do to solve the pipeline error or at least improve the situation?


Best way for item margins?

I noticed that all of the item elements have position: absolute, so margins are ignored when positioning them within the virtual list wrapper.
For example, if you try changing the Y margins (i.e. margin-top or margin-bottom) of .row in https://svelte.dev/repl/e3811b44f311461dbbc7c2df830cde68?version=3.48.0, nothing happens.
Is there a way to allow for margins between elements in the virtual list?

Hey there!
Sorry for the late reply....
The best solution I've found is something like this:
https://svelte.dev/repl/9a97af105471434882a1beafac372e46?version=3.48.0
It's not the most beautiful solution (we basically use padding and an inner element), but it might be enough for your use case.
Feel free to ask if you have any further questions. Unfortunately this is a limitation of the library...

Closing this due to inactivity. Hopefully my solution helped you a bit.