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datacorpus

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santoshphilip/eppy
eppy/hvacbuilder.py
getfieldnamesendswith
def getfieldnamesendswith(idfobject, endswith): """get the filednames for the idfobject based on endswith""" objls = idfobject.objls tmp = [name for name in objls if name.endswith(endswith)] if tmp == []: pass return [name for name in objls if name.endswith(endswith)]
python
def getfieldnamesendswith(idfobject, endswith): """get the filednames for the idfobject based on endswith""" objls = idfobject.objls tmp = [name for name in objls if name.endswith(endswith)] if tmp == []: pass return [name for name in objls if name.endswith(endswith)]
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get the filednames for the idfobject based on endswith
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L164-L170
train
santoshphilip/eppy
eppy/hvacbuilder.py
getnodefieldname
def getnodefieldname(idfobject, endswith, fluid=None, startswith=None): """return the field name of the node fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water""" if startswith is None: startswith = '' if fluid is None: fluid = '' nodenames = getfieldnamesendswith(idfobject, endswith) nodenames = [name for name in nodenames if name.startswith(startswith)] fnodenames = [nd for nd in nodenames if nd.find(fluid) != -1] fnodenames = [name for name in fnodenames if name.startswith(startswith)] if len(fnodenames) == 0: nodename = nodenames[0] else: nodename = fnodenames[0] return nodename
python
def getnodefieldname(idfobject, endswith, fluid=None, startswith=None): """return the field name of the node fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water""" if startswith is None: startswith = '' if fluid is None: fluid = '' nodenames = getfieldnamesendswith(idfobject, endswith) nodenames = [name for name in nodenames if name.startswith(startswith)] fnodenames = [nd for nd in nodenames if nd.find(fluid) != -1] fnodenames = [name for name in fnodenames if name.startswith(startswith)] if len(fnodenames) == 0: nodename = nodenames[0] else: nodename = fnodenames[0] return nodename
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return the field name of the node fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L172-L189
train
santoshphilip/eppy
eppy/hvacbuilder.py
connectcomponents
def connectcomponents(idf, components, fluid=None): """rename nodes so that the components get connected fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water""" if fluid is None: fluid = '' if len(components) == 1: thiscomp, thiscompnode = components[0] initinletoutlet(idf, thiscomp, thiscompnode, force=False) outletnodename = getnodefieldname(thiscomp, "Outlet_Node_Name", fluid=fluid, startswith=thiscompnode) thiscomp[outletnodename] = [thiscomp[outletnodename], thiscomp[outletnodename]] # inletnodename = getnodefieldname(nextcomp, "Inlet_Node_Name", fluid) # nextcomp[inletnodename] = [nextcomp[inletnodename], betweennodename] return components for i in range(len(components) - 1): thiscomp, thiscompnode = components[i] nextcomp, nextcompnode = components[i + 1] initinletoutlet(idf, thiscomp, thiscompnode, force=False) initinletoutlet(idf, nextcomp, nextcompnode, force=False) betweennodename = "%s_%s_node" % (thiscomp.Name, nextcomp.Name) outletnodename = getnodefieldname(thiscomp, "Outlet_Node_Name", fluid=fluid, startswith=thiscompnode) thiscomp[outletnodename] = [thiscomp[outletnodename], betweennodename] inletnodename = getnodefieldname(nextcomp, "Inlet_Node_Name", fluid) nextcomp[inletnodename] = [nextcomp[inletnodename], betweennodename] return components
python
def connectcomponents(idf, components, fluid=None): """rename nodes so that the components get connected fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water""" if fluid is None: fluid = '' if len(components) == 1: thiscomp, thiscompnode = components[0] initinletoutlet(idf, thiscomp, thiscompnode, force=False) outletnodename = getnodefieldname(thiscomp, "Outlet_Node_Name", fluid=fluid, startswith=thiscompnode) thiscomp[outletnodename] = [thiscomp[outletnodename], thiscomp[outletnodename]] # inletnodename = getnodefieldname(nextcomp, "Inlet_Node_Name", fluid) # nextcomp[inletnodename] = [nextcomp[inletnodename], betweennodename] return components for i in range(len(components) - 1): thiscomp, thiscompnode = components[i] nextcomp, nextcompnode = components[i + 1] initinletoutlet(idf, thiscomp, thiscompnode, force=False) initinletoutlet(idf, nextcomp, nextcompnode, force=False) betweennodename = "%s_%s_node" % (thiscomp.Name, nextcomp.Name) outletnodename = getnodefieldname(thiscomp, "Outlet_Node_Name", fluid=fluid, startswith=thiscompnode) thiscomp[outletnodename] = [thiscomp[outletnodename], betweennodename] inletnodename = getnodefieldname(nextcomp, "Inlet_Node_Name", fluid) nextcomp[inletnodename] = [nextcomp[inletnodename], betweennodename] return components
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rename nodes so that the components get connected fluid is only needed if there are air and water nodes fluid is Air or Water or ''. if the fluid is Steam, use Water
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L192-L220
train
santoshphilip/eppy
eppy/hvacbuilder.py
initinletoutlet
def initinletoutlet(idf, idfobject, thisnode, force=False): """initialze values for all the inlet outlet nodes for the object. if force == False, it willl init only if field = '' """ def blankfield(fieldvalue): """test for blank field""" try: if fieldvalue.strip() == '': return True else: return False except AttributeError: # field may be a list return False def trimfields(fields, thisnode): if len(fields) > 1: if thisnode is not None: fields = [field for field in fields if field.startswith(thisnode)] return fields else: print("Where should this loop connect ?") print("%s - %s" % (idfobject.key, idfobject.Name)) print([field.split("Inlet_Node_Name")[0] for field in inletfields]) raise WhichLoopError else: return fields inletfields = getfieldnamesendswith(idfobject, "Inlet_Node_Name") inletfields = trimfields(inletfields, thisnode) # or warn with exception for inletfield in inletfields: if blankfield(idfobject[inletfield]) == True or force == True: idfobject[inletfield] = "%s_%s" % (idfobject.Name, inletfield) outletfields = getfieldnamesendswith(idfobject, "Outlet_Node_Name") outletfields = trimfields(outletfields, thisnode) # or warn with exception for outletfield in outletfields: if blankfield(idfobject[outletfield]) == True or force == True: idfobject[outletfield] = "%s_%s" % (idfobject.Name, outletfield) return idfobject
python
def initinletoutlet(idf, idfobject, thisnode, force=False): """initialze values for all the inlet outlet nodes for the object. if force == False, it willl init only if field = '' """ def blankfield(fieldvalue): """test for blank field""" try: if fieldvalue.strip() == '': return True else: return False except AttributeError: # field may be a list return False def trimfields(fields, thisnode): if len(fields) > 1: if thisnode is not None: fields = [field for field in fields if field.startswith(thisnode)] return fields else: print("Where should this loop connect ?") print("%s - %s" % (idfobject.key, idfobject.Name)) print([field.split("Inlet_Node_Name")[0] for field in inletfields]) raise WhichLoopError else: return fields inletfields = getfieldnamesendswith(idfobject, "Inlet_Node_Name") inletfields = trimfields(inletfields, thisnode) # or warn with exception for inletfield in inletfields: if blankfield(idfobject[inletfield]) == True or force == True: idfobject[inletfield] = "%s_%s" % (idfobject.Name, inletfield) outletfields = getfieldnamesendswith(idfobject, "Outlet_Node_Name") outletfields = trimfields(outletfields, thisnode) # or warn with exception for outletfield in outletfields: if blankfield(idfobject[outletfield]) == True or force == True: idfobject[outletfield] = "%s_%s" % (idfobject.Name, outletfield) return idfobject
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initialze values for all the inlet outlet nodes for the object. if force == False, it willl init only if field = ''
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L223-L260
train
santoshphilip/eppy
eppy/hvacbuilder.py
componentsintobranch
def componentsintobranch(idf, branch, listofcomponents, fluid=None): """insert a list of components into a branch fluid is only needed if there are air and water nodes in same object fluid is Air or Water or ''. if the fluid is Steam, use Water""" if fluid is None: fluid = '' componentlist = [item[0] for item in listofcomponents] # assumes that the nodes of the component connect to each other # empty branch if it has existing components thebranchname = branch.Name thebranch = idf.removeextensibles('BRANCH', thebranchname) # empty the branch # fill in the new components with the node names into this branch # find the first extensible field and fill in the data in obj. e_index = idf.getextensibleindex('BRANCH', thebranchname) theobj = thebranch.obj modeleditor.extendlist(theobj, e_index) # just being careful here for comp, compnode in listofcomponents: theobj.append(comp.key) theobj.append(comp.Name) inletnodename = getnodefieldname(comp, "Inlet_Node_Name", fluid=fluid, startswith=compnode) theobj.append(comp[inletnodename]) outletnodename = getnodefieldname(comp, "Outlet_Node_Name", fluid=fluid, startswith=compnode) theobj.append(comp[outletnodename]) theobj.append('') return thebranch
python
def componentsintobranch(idf, branch, listofcomponents, fluid=None): """insert a list of components into a branch fluid is only needed if there are air and water nodes in same object fluid is Air or Water or ''. if the fluid is Steam, use Water""" if fluid is None: fluid = '' componentlist = [item[0] for item in listofcomponents] # assumes that the nodes of the component connect to each other # empty branch if it has existing components thebranchname = branch.Name thebranch = idf.removeextensibles('BRANCH', thebranchname) # empty the branch # fill in the new components with the node names into this branch # find the first extensible field and fill in the data in obj. e_index = idf.getextensibleindex('BRANCH', thebranchname) theobj = thebranch.obj modeleditor.extendlist(theobj, e_index) # just being careful here for comp, compnode in listofcomponents: theobj.append(comp.key) theobj.append(comp.Name) inletnodename = getnodefieldname(comp, "Inlet_Node_Name", fluid=fluid, startswith=compnode) theobj.append(comp[inletnodename]) outletnodename = getnodefieldname(comp, "Outlet_Node_Name", fluid=fluid, startswith=compnode) theobj.append(comp[outletnodename]) theobj.append('') return thebranch
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L262-L290
train
santoshphilip/eppy
eppy/hvacbuilder.py
makeairloop
def makeairloop(idf, loopname, sloop, dloop, testing=None): """make an airloop""" # -------- testing --------- testn = 0 # -------- testing --------- newairloop = idf.newidfobject("AirLoopHVAC".upper(), Name=loopname) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- fields = SomeFields.a_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = ['Branches', 'Connectors', 'Supply Inlet', 'Demand Outlet', 'Demand Inlet', 'Supply Outlet'] # old TODO : pop connectors if no parallel branches # make fieldnames in the air loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newairloop[thefield] = fieldname # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make the branch lists for this air loop sbranchlist = idf.newidfobject("BRANCHLIST", Name=newairloop[flnames[0]]) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # supply side sbranchs = [] for bname in sbranchnames: branch = makeductbranch(idf, bname) sbranchs.append(branch) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Supply_Side_Inlet_Node_Name" # TODO : change ? sbranchs[0][anode] = newairloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Supply_Side_Outlet_Node_Names" # TODO : change ? sbranchs[-1][anode] = newairloop[sameoutnode] # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # rename inlet outlet of endpoints of loop - rename in pipe dname = sbranchs[0]['Component_1_Name'] # get the duct name aduct = idf.getobject('duct'.upper(), dname) # get duct aduct.Inlet_Node_Name = newairloop[sameinnode] dname = sbranchs[-1]['Component_1_Name'] # get the duct name aduct = idf.getobject('duct'.upper(), dname) # get duct aduct.Outlet_Node_Name = newairloop[sameoutnode] # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # # # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject("CONNECTORLIST", Name=newairloop.Connector_List_Name) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make splitters and mixers s_splitter = idf.newidfobject("CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject("CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # demand side loop for airloop is made below # ZoneHVAC:EquipmentConnections for zone in dloop: equipconn = idf.newidfobject("ZoneHVAC:EquipmentConnections".upper()) equipconn.Zone_Name = zone fldname = "Zone_Conditioning_Equipment_List_Name" equipconn[fldname] = "%s equip list" % (zone,) fldname = "Zone_Air_Inlet_Node_or_NodeList_Name" equipconn[fldname] = "%s Inlet Node" % (zone,) fldname = "Zone_Air_Node_Name" equipconn[fldname] = "%s Node" % (zone,) fldname = "Zone_Return_Air_Node_Name" equipconn[fldname] = "%s Outlet Node" % (zone,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make ZoneHVAC:EquipmentList for zone in dloop: z_equiplst = idf.newidfobject("ZoneHVAC:EquipmentList".upper()) z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] z_equiplst.Name = z_equipconn.Zone_Conditioning_Equipment_List_Name fld = "Zone_Equipment_1_Object_Type" z_equiplst[fld] = "AirTerminal:SingleDuct:Uncontrolled" z_equiplst.Zone_Equipment_1_Name = "%sDirectAir" % (zone,) z_equiplst.Zone_Equipment_1_Cooling_Sequence = 1 z_equiplst.Zone_Equipment_1_Heating_or_NoLoad_Sequence = 1 # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirTerminal:SingleDuct:Uncontrolled for zone in dloop: z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] key = "AirTerminal:SingleDuct:Uncontrolled".upper() z_airterm = idf.newidfobject(key) z_airterm.Name = "%sDirectAir" % (zone,) fld1 = "Zone_Supply_Air_Node_Name" fld2 = "Zone_Air_Inlet_Node_or_NodeList_Name" z_airterm[fld1] = z_equipconn[fld2] z_airterm.Maximum_Air_Flow_Rate = 'autosize' # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # MAKE AirLoopHVAC:ZoneSplitter # zone = dloop[0] key = "AirLoopHVAC:ZoneSplitter".upper() z_splitter = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_splitter.Name = "%s Demand Side Splitter" % (loopname,) z_splitter.Inlet_Node_Name = newairloop.Demand_Side_Inlet_Node_Names for i, zone in enumerate(dloop): z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] fld = "Outlet_%s_Node_Name" % (i + 1,) z_splitter[fld] = z_equipconn.Zone_Air_Inlet_Node_or_NodeList_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:SupplyPath key = "AirLoopHVAC:SupplyPath".upper() z_supplypth = idf.newidfobject(key) z_supplypth.Name = "%sSupplyPath" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- fld1 = "Supply_Air_Path_Inlet_Node_Name" fld2 = "Demand_Side_Inlet_Node_Names" z_supplypth[fld1] = newairloop[fld2] z_supplypth.Component_1_Object_Type = "AirLoopHVAC:ZoneSplitter" z_supplypth.Component_1_Name = z_splitter.Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:ZoneMixer key = "AirLoopHVAC:ZoneMixer".upper() z_mixer = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_mixer.Name = "%s Demand Side Mixer" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_mixer.Outlet_Node_Name = newairloop.Demand_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- for i, zone in enumerate(dloop): z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] fld = "Inlet_%s_Node_Name" % (i + 1,) z_mixer[fld] = z_equipconn.Zone_Return_Air_Node_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:ReturnPath key = "AirLoopHVAC:ReturnPath".upper() z_returnpth = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_returnpth.Name = "%sReturnPath" % (loopname,) z_returnpth.Return_Air_Path_Outlet_Node_Name = newairloop.Demand_Side_Outlet_Node_Name z_returnpth.Component_1_Object_Type = "AirLoopHVAC:ZoneMixer" z_returnpth.Component_1_Name = z_mixer.Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- return newairloop
python
def makeairloop(idf, loopname, sloop, dloop, testing=None): """make an airloop""" # -------- testing --------- testn = 0 # -------- testing --------- newairloop = idf.newidfobject("AirLoopHVAC".upper(), Name=loopname) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- fields = SomeFields.a_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = ['Branches', 'Connectors', 'Supply Inlet', 'Demand Outlet', 'Demand Inlet', 'Supply Outlet'] # old TODO : pop connectors if no parallel branches # make fieldnames in the air loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newairloop[thefield] = fieldname # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make the branch lists for this air loop sbranchlist = idf.newidfobject("BRANCHLIST", Name=newairloop[flnames[0]]) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # supply side sbranchs = [] for bname in sbranchnames: branch = makeductbranch(idf, bname) sbranchs.append(branch) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Supply_Side_Inlet_Node_Name" # TODO : change ? sbranchs[0][anode] = newairloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Supply_Side_Outlet_Node_Names" # TODO : change ? sbranchs[-1][anode] = newairloop[sameoutnode] # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # rename inlet outlet of endpoints of loop - rename in pipe dname = sbranchs[0]['Component_1_Name'] # get the duct name aduct = idf.getobject('duct'.upper(), dname) # get duct aduct.Inlet_Node_Name = newairloop[sameinnode] dname = sbranchs[-1]['Component_1_Name'] # get the duct name aduct = idf.getobject('duct'.upper(), dname) # get duct aduct.Outlet_Node_Name = newairloop[sameoutnode] # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # # # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject("CONNECTORLIST", Name=newairloop.Connector_List_Name) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make splitters and mixers s_splitter = idf.newidfobject("CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject("CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # demand side loop for airloop is made below # ZoneHVAC:EquipmentConnections for zone in dloop: equipconn = idf.newidfobject("ZoneHVAC:EquipmentConnections".upper()) equipconn.Zone_Name = zone fldname = "Zone_Conditioning_Equipment_List_Name" equipconn[fldname] = "%s equip list" % (zone,) fldname = "Zone_Air_Inlet_Node_or_NodeList_Name" equipconn[fldname] = "%s Inlet Node" % (zone,) fldname = "Zone_Air_Node_Name" equipconn[fldname] = "%s Node" % (zone,) fldname = "Zone_Return_Air_Node_Name" equipconn[fldname] = "%s Outlet Node" % (zone,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make ZoneHVAC:EquipmentList for zone in dloop: z_equiplst = idf.newidfobject("ZoneHVAC:EquipmentList".upper()) z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] z_equiplst.Name = z_equipconn.Zone_Conditioning_Equipment_List_Name fld = "Zone_Equipment_1_Object_Type" z_equiplst[fld] = "AirTerminal:SingleDuct:Uncontrolled" z_equiplst.Zone_Equipment_1_Name = "%sDirectAir" % (zone,) z_equiplst.Zone_Equipment_1_Cooling_Sequence = 1 z_equiplst.Zone_Equipment_1_Heating_or_NoLoad_Sequence = 1 # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirTerminal:SingleDuct:Uncontrolled for zone in dloop: z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] key = "AirTerminal:SingleDuct:Uncontrolled".upper() z_airterm = idf.newidfobject(key) z_airterm.Name = "%sDirectAir" % (zone,) fld1 = "Zone_Supply_Air_Node_Name" fld2 = "Zone_Air_Inlet_Node_or_NodeList_Name" z_airterm[fld1] = z_equipconn[fld2] z_airterm.Maximum_Air_Flow_Rate = 'autosize' # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # MAKE AirLoopHVAC:ZoneSplitter # zone = dloop[0] key = "AirLoopHVAC:ZoneSplitter".upper() z_splitter = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_splitter.Name = "%s Demand Side Splitter" % (loopname,) z_splitter.Inlet_Node_Name = newairloop.Demand_Side_Inlet_Node_Names for i, zone in enumerate(dloop): z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] fld = "Outlet_%s_Node_Name" % (i + 1,) z_splitter[fld] = z_equipconn.Zone_Air_Inlet_Node_or_NodeList_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:SupplyPath key = "AirLoopHVAC:SupplyPath".upper() z_supplypth = idf.newidfobject(key) z_supplypth.Name = "%sSupplyPath" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- fld1 = "Supply_Air_Path_Inlet_Node_Name" fld2 = "Demand_Side_Inlet_Node_Names" z_supplypth[fld1] = newairloop[fld2] z_supplypth.Component_1_Object_Type = "AirLoopHVAC:ZoneSplitter" z_supplypth.Component_1_Name = z_splitter.Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:ZoneMixer key = "AirLoopHVAC:ZoneMixer".upper() z_mixer = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_mixer.Name = "%s Demand Side Mixer" % (loopname,) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_mixer.Outlet_Node_Name = newairloop.Demand_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- for i, zone in enumerate(dloop): z_equipconn = modeleditor.getobjects( idf.idfobjects, idf.model, idf.idd_info, "ZoneHVAC:EquipmentConnections".upper(), # places=7, **dict(Zone_Name=zone))[0] fld = "Inlet_%s_Node_Name" % (i + 1,) z_mixer[fld] = z_equipconn.Zone_Return_Air_Node_Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- # make AirLoopHVAC:ReturnPath key = "AirLoopHVAC:ReturnPath".upper() z_returnpth = idf.newidfobject(key) # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- z_returnpth.Name = "%sReturnPath" % (loopname,) z_returnpth.Return_Air_Path_Outlet_Node_Name = newairloop.Demand_Side_Outlet_Node_Name z_returnpth.Component_1_Object_Type = "AirLoopHVAC:ZoneMixer" z_returnpth.Component_1_Name = z_mixer.Name # -------- testing --------- testn = doingtesting(testing, testn, newairloop) if testn == None: returnnone() # -------- testing --------- return newairloop
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-------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "# make AirTerminal:SingleDuct:Uncontrolled", "for", "zone", "in", "dloop", ":", "z_equipconn", "=", "modeleditor", ".", "getobjects", "(", "idf", ".", "idfobjects", ",", "idf", ".", "model", ",", "idf", ".", "idd_info", ",", "\"ZoneHVAC:EquipmentConnections\"", ".", "upper", "(", ")", ",", "# places=7,", "*", "*", "dict", "(", "Zone_Name", "=", "zone", ")", ")", "[", "0", "]", "key", "=", "\"AirTerminal:SingleDuct:Uncontrolled\"", ".", "upper", "(", ")", "z_airterm", "=", "idf", ".", "newidfobject", "(", "key", ")", "z_airterm", ".", "Name", "=", "\"%sDirectAir\"", "%", "(", "zone", ",", ")", "fld1", "=", "\"Zone_Supply_Air_Node_Name\"", "fld2", "=", "\"Zone_Air_Inlet_Node_or_NodeList_Name\"", "z_airterm", "[", "fld1", "]", "=", "z_equipconn", "[", "fld2", "]", "z_airterm", ".", "Maximum_Air_Flow_Rate", "=", "'autosize'", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "# MAKE AirLoopHVAC:ZoneSplitter", "# zone = dloop[0]", "key", "=", "\"AirLoopHVAC:ZoneSplitter\"", ".", "upper", "(", ")", "z_splitter", "=", "idf", ".", "newidfobject", "(", "key", ")", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "z_splitter", ".", "Name", "=", "\"%s Demand Side Splitter\"", "%", "(", "loopname", ",", ")", "z_splitter", ".", "Inlet_Node_Name", "=", "newairloop", ".", "Demand_Side_Inlet_Node_Names", "for", "i", ",", "zone", "in", "enumerate", "(", "dloop", ")", ":", "z_equipconn", "=", "modeleditor", ".", "getobjects", "(", "idf", ".", "idfobjects", ",", "idf", ".", "model", ",", "idf", ".", "idd_info", ",", "\"ZoneHVAC:EquipmentConnections\"", ".", "upper", "(", ")", ",", "# places=7,", "*", "*", "dict", "(", "Zone_Name", "=", "zone", ")", ")", "[", "0", "]", "fld", "=", "\"Outlet_%s_Node_Name\"", "%", "(", "i", "+", "1", ",", ")", "z_splitter", "[", "fld", "]", "=", "z_equipconn", ".", "Zone_Air_Inlet_Node_or_NodeList_Name", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "# make AirLoopHVAC:SupplyPath", "key", "=", "\"AirLoopHVAC:SupplyPath\"", ".", "upper", "(", ")", "z_supplypth", "=", "idf", ".", "newidfobject", "(", "key", ")", "z_supplypth", ".", "Name", "=", "\"%sSupplyPath\"", "%", "(", "loopname", ",", ")", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "fld1", "=", "\"Supply_Air_Path_Inlet_Node_Name\"", "fld2", "=", "\"Demand_Side_Inlet_Node_Names\"", "z_supplypth", "[", "fld1", "]", "=", "newairloop", "[", "fld2", "]", "z_supplypth", ".", "Component_1_Object_Type", "=", "\"AirLoopHVAC:ZoneSplitter\"", "z_supplypth", ".", "Component_1_Name", "=", "z_splitter", ".", "Name", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "# make AirLoopHVAC:ZoneMixer", "key", "=", "\"AirLoopHVAC:ZoneMixer\"", ".", "upper", "(", ")", "z_mixer", "=", "idf", ".", "newidfobject", "(", "key", ")", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "z_mixer", ".", "Name", "=", "\"%s Demand Side Mixer\"", "%", "(", "loopname", ",", ")", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "z_mixer", ".", "Outlet_Node_Name", "=", "newairloop", ".", "Demand_Side_Outlet_Node_Name", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "for", "i", ",", "zone", "in", "enumerate", "(", "dloop", ")", ":", "z_equipconn", "=", "modeleditor", ".", "getobjects", "(", "idf", ".", "idfobjects", ",", "idf", ".", "model", ",", "idf", ".", "idd_info", ",", "\"ZoneHVAC:EquipmentConnections\"", ".", "upper", "(", ")", ",", "# places=7,", "*", "*", "dict", "(", "Zone_Name", "=", "zone", ")", ")", "[", "0", "]", "fld", "=", "\"Inlet_%s_Node_Name\"", "%", "(", "i", "+", "1", ",", ")", "z_mixer", "[", "fld", "]", "=", "z_equipconn", ".", "Zone_Return_Air_Node_Name", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "# make AirLoopHVAC:ReturnPath", "key", "=", "\"AirLoopHVAC:ReturnPath\"", ".", "upper", "(", ")", "z_returnpth", "=", "idf", ".", "newidfobject", "(", "key", ")", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "z_returnpth", ".", "Name", "=", "\"%sReturnPath\"", "%", "(", "loopname", ",", ")", "z_returnpth", ".", "Return_Air_Path_Outlet_Node_Name", "=", "newairloop", ".", "Demand_Side_Outlet_Node_Name", "z_returnpth", ".", "Component_1_Object_Type", "=", "\"AirLoopHVAC:ZoneMixer\"", "z_returnpth", ".", "Component_1_Name", "=", "z_mixer", ".", "Name", "# -------- testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newairloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing ---------", "return", "newairloop" ]
make an airloop
[ "make", "an", "airloop" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L305-L573
train
santoshphilip/eppy
eppy/hvacbuilder.py
makeplantloop
def makeplantloop(idf, loopname, sloop, dloop, testing=None): """make plant loop with pip components""" # -------- <testing --------- testn = 0 # -------- testing> --------- newplantloop = idf.newidfobject("PLANTLOOP", Name=loopname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- fields = SomeFields.p_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = [field.replace('Plant Side', 'Supply') for field in fields] fields1 = [field.replace('Demand Side', 'Demand') for field in fields1] fields1 = [field[:field.find('Name') - 1] for field in fields1] fields1 = [field.replace(' Node', '') for field in fields1] fields1 = [field.replace(' List', 's') for field in fields1] # TODO : pop connectors if no parallel branches # make fieldnames in the plant loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newplantloop[thefield] = fieldname # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make the branch lists for this plant loop sbranchlist = idf.newidfobject( "BRANCHLIST", Name=newplantloop.Plant_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- dbranchlist = idf.newidfobject( "BRANCHLIST", Name=newplantloop.Demand_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- dbranchnames = flattencopy(dloop) # dbranchnames = dloop[1] for branchname in dbranchnames: dbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make a pipe branch for all branches in the loop # supply side sbranchs = [] for bname in sbranchnames: branch = makepipebranch(idf, bname) sbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Plant_Side_Inlet_Node_Name" sbranchs[0][anode] = newplantloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Plant_Side_Outlet_Node_Name" sbranchs[-1][anode] = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = sbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newplantloop[sameinnode] pname = sbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # demand side dbranchs = [] for bname in dbranchnames: branch = makepipebranch(idf, bname) dbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in branch anode = "Component_1_Inlet_Node_Name" sameinnode = "Demand_Side_Inlet_Node_Name" dbranchs[0][anode] = newplantloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Demand_Side_Outlet_Node_Name" dbranchs[-1][anode] = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = dbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newplantloop[sameinnode] pname = dbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject( "CONNECTORLIST", Name=newplantloop.Plant_Side_Connector_List_Name) sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) dconnlist = idf.newidfobject( "CONNECTORLIST", Name=newplantloop.Demand_Side_Connector_List_Name) dconnlist.Connector_1_Object_Type = "Connector:Splitter" dconnlist.Connector_1_Name = "%s_demand_splitter" % (loopname,) dconnlist.Connector_2_Object_Type = "Connector:Mixer" dconnlist.Connector_2_Name = "%s_demand_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make splitters and mixers s_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # - d_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=dconnlist.Connector_1_Name) d_splitter.obj.extend([dloop[0]] + dloop[1]) d_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=dconnlist.Connector_2_Name) d_mixer.obj.extend([dloop[-1]] + dloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- return newplantloop
python
def makeplantloop(idf, loopname, sloop, dloop, testing=None): """make plant loop with pip components""" # -------- <testing --------- testn = 0 # -------- testing> --------- newplantloop = idf.newidfobject("PLANTLOOP", Name=loopname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- fields = SomeFields.p_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = [field.replace('Plant Side', 'Supply') for field in fields] fields1 = [field.replace('Demand Side', 'Demand') for field in fields1] fields1 = [field[:field.find('Name') - 1] for field in fields1] fields1 = [field.replace(' Node', '') for field in fields1] fields1 = [field.replace(' List', 's') for field in fields1] # TODO : pop connectors if no parallel branches # make fieldnames in the plant loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newplantloop[thefield] = fieldname # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make the branch lists for this plant loop sbranchlist = idf.newidfobject( "BRANCHLIST", Name=newplantloop.Plant_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- dbranchlist = idf.newidfobject( "BRANCHLIST", Name=newplantloop.Demand_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- dbranchnames = flattencopy(dloop) # dbranchnames = dloop[1] for branchname in dbranchnames: dbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make a pipe branch for all branches in the loop # supply side sbranchs = [] for bname in sbranchnames: branch = makepipebranch(idf, bname) sbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Plant_Side_Inlet_Node_Name" sbranchs[0][anode] = newplantloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Plant_Side_Outlet_Node_Name" sbranchs[-1][anode] = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = sbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newplantloop[sameinnode] pname = sbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # demand side dbranchs = [] for bname in dbranchnames: branch = makepipebranch(idf, bname) dbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in branch anode = "Component_1_Inlet_Node_Name" sameinnode = "Demand_Side_Inlet_Node_Name" dbranchs[0][anode] = newplantloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Demand_Side_Outlet_Node_Name" dbranchs[-1][anode] = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = dbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newplantloop[sameinnode] pname = dbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newplantloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject( "CONNECTORLIST", Name=newplantloop.Plant_Side_Connector_List_Name) sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) dconnlist = idf.newidfobject( "CONNECTORLIST", Name=newplantloop.Demand_Side_Connector_List_Name) dconnlist.Connector_1_Object_Type = "Connector:Splitter" dconnlist.Connector_1_Name = "%s_demand_splitter" % (loopname,) dconnlist.Connector_2_Object_Type = "Connector:Mixer" dconnlist.Connector_2_Name = "%s_demand_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- # make splitters and mixers s_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # - d_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=dconnlist.Connector_1_Name) d_splitter.obj.extend([dloop[0]] + dloop[1]) d_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=dconnlist.Connector_2_Name) d_mixer.obj.extend([dloop[-1]] + dloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newplantloop) if testn == None: returnnone() # -------- testing> --------- return newplantloop
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rename in pipe", "pname", "=", "sbranchs", "[", "0", "]", "[", "'Component_1_Name'", "]", "# get the pipe name", "apipe", "=", "idf", ".", "getobject", "(", "'Pipe:Adiabatic'", ".", "upper", "(", ")", ",", "pname", ")", "# get pipe", "apipe", ".", "Inlet_Node_Name", "=", "newplantloop", "[", "sameinnode", "]", "pname", "=", "sbranchs", "[", "-", "1", "]", "[", "'Component_1_Name'", "]", "# get the pipe name", "apipe", "=", "idf", ".", "getobject", "(", "'Pipe:Adiabatic'", ".", "upper", "(", ")", ",", "pname", ")", "# get pipe", "apipe", ".", "Outlet_Node_Name", "=", "newplantloop", "[", "sameoutnode", "]", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "# demand side", "dbranchs", "=", "[", "]", "for", "bname", "in", "dbranchnames", ":", "branch", "=", "makepipebranch", "(", "idf", ",", "bname", ")", "dbranchs", ".", "append", "(", "branch", ")", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "# rename inlet outlet of endpoints of loop - rename in branch", "anode", "=", "\"Component_1_Inlet_Node_Name\"", "sameinnode", "=", "\"Demand_Side_Inlet_Node_Name\"", "dbranchs", "[", "0", "]", "[", "anode", "]", "=", "newplantloop", "[", "sameinnode", "]", "anode", "=", "\"Component_1_Outlet_Node_Name\"", "sameoutnode", "=", "\"Demand_Side_Outlet_Node_Name\"", "dbranchs", "[", "-", "1", "]", "[", "anode", "]", "=", "newplantloop", "[", "sameoutnode", "]", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "# rename inlet outlet of endpoints of loop - rename in pipe", "pname", "=", "dbranchs", "[", "0", "]", "[", "'Component_1_Name'", "]", "# get the pipe name", "apipe", "=", "idf", ".", "getobject", "(", "'Pipe:Adiabatic'", ".", "upper", "(", ")", ",", "pname", ")", "# get pipe", "apipe", ".", "Inlet_Node_Name", "=", "newplantloop", "[", "sameinnode", "]", "pname", "=", "dbranchs", "[", "-", "1", "]", "[", "'Component_1_Name'", "]", "# get the pipe name", "apipe", "=", "idf", ".", "getobject", "(", "'Pipe:Adiabatic'", ".", "upper", "(", ")", ",", "pname", ")", "# get pipe", "apipe", ".", "Outlet_Node_Name", "=", "newplantloop", "[", "sameoutnode", "]", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "# TODO : test if there are parallel branches", "# make the connectorlist an fill fields", "sconnlist", "=", "idf", ".", "newidfobject", "(", "\"CONNECTORLIST\"", ",", "Name", "=", "newplantloop", ".", "Plant_Side_Connector_List_Name", ")", "sconnlist", ".", "Connector_1_Object_Type", "=", "\"Connector:Splitter\"", "sconnlist", ".", "Connector_1_Name", "=", "\"%s_supply_splitter\"", "%", "(", "loopname", ",", ")", "sconnlist", ".", "Connector_2_Object_Type", "=", "\"Connector:Mixer\"", "sconnlist", ".", "Connector_2_Name", "=", "\"%s_supply_mixer\"", "%", "(", "loopname", ",", ")", "dconnlist", "=", "idf", ".", "newidfobject", "(", "\"CONNECTORLIST\"", ",", "Name", "=", "newplantloop", ".", "Demand_Side_Connector_List_Name", ")", "dconnlist", ".", "Connector_1_Object_Type", "=", "\"Connector:Splitter\"", "dconnlist", ".", "Connector_1_Name", "=", "\"%s_demand_splitter\"", "%", "(", "loopname", ",", ")", "dconnlist", ".", "Connector_2_Object_Type", "=", "\"Connector:Mixer\"", "dconnlist", ".", "Connector_2_Name", "=", "\"%s_demand_mixer\"", "%", "(", "loopname", ",", ")", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "# make splitters and mixers", "s_splitter", "=", "idf", ".", "newidfobject", "(", "\"CONNECTOR:SPLITTER\"", ",", "Name", "=", "sconnlist", ".", "Connector_1_Name", ")", "s_splitter", ".", "obj", ".", "extend", "(", "[", "sloop", "[", "0", "]", "]", "+", "sloop", "[", "1", "]", ")", "s_mixer", "=", "idf", ".", "newidfobject", "(", "\"CONNECTOR:MIXER\"", ",", "Name", "=", "sconnlist", ".", "Connector_2_Name", ")", "s_mixer", ".", "obj", ".", "extend", "(", "[", "sloop", "[", "-", "1", "]", "]", "+", "sloop", "[", "1", "]", ")", "# -", "d_splitter", "=", "idf", ".", "newidfobject", "(", "\"CONNECTOR:SPLITTER\"", ",", "Name", "=", "dconnlist", ".", "Connector_1_Name", ")", "d_splitter", ".", "obj", ".", "extend", "(", "[", "dloop", "[", "0", "]", "]", "+", "dloop", "[", "1", "]", ")", "d_mixer", "=", "idf", ".", "newidfobject", "(", "\"CONNECTOR:MIXER\"", ",", "Name", "=", "dconnlist", ".", "Connector_2_Name", ")", "d_mixer", ".", "obj", ".", "extend", "(", "[", "dloop", "[", "-", "1", "]", "]", "+", "dloop", "[", "1", "]", ")", "# -------- <testing ---------", "testn", "=", "doingtesting", "(", "testing", ",", "testn", ",", "newplantloop", ")", "if", "testn", "==", "None", ":", "returnnone", "(", ")", "# -------- testing> ---------", "return", "newplantloop" ]
make plant loop with pip components
[ "make", "plant", "loop", "with", "pip", "components" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L575-L765
train
santoshphilip/eppy
eppy/hvacbuilder.py
makecondenserloop
def makecondenserloop(idf, loopname, sloop, dloop, testing=None): """make condenser loop with pipe components""" # -------- <testing --------- testn = 0 # -------- testing> --------- newcondenserloop = idf.newidfobject("CondenserLoop".upper(), Name=loopname) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- fields = SomeFields.c_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = [field.replace( 'Condenser Side', 'Cond_Supply') for field in fields] fields1 = [field.replace('Demand Side', 'Demand') for field in fields1] fields1 = [field[:field.find('Name') - 1] for field in fields1] fields1 = [field.replace(' Node', '') for field in fields1] fields1 = [field.replace(' List', 's') for field in fields1] # old TODO : pop connectors if no parallel branches # make fieldnames in the condenser loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newcondenserloop[thefield] = fieldname # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make the branch lists for this condenser loop sbranchlist = idf.newidfobject( "BRANCHLIST", Name=newcondenserloop.Condenser_Side_Branch_List_Name) dbranchlist = idf.newidfobject( "BRANCHLIST", Name=newcondenserloop.Condenser_Demand_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) dbranchnames = flattencopy(dloop) # dbranchnames = dloop[1] for branchname in dbranchnames: dbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make a pipe branch for all branches in the loop # supply side sbranchs = [] for bname in sbranchnames: branch = makepipebranch(idf, bname) sbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Condenser_Side_Inlet_Node_Name" # TODO : change ? sbranchs[0][anode] = newcondenserloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Condenser_Side_Outlet_Node_Name" # TODO : change ? sbranchs[-1][anode] = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = sbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newcondenserloop[sameinnode] pname = sbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # demand side dbranchs = [] for bname in dbranchnames: branch = makepipebranch(idf, bname) dbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in branch anode = "Component_1_Inlet_Node_Name" sameinnode = "Demand_Side_Inlet_Node_Name" # TODO : change ? dbranchs[0][anode] = newcondenserloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Demand_Side_Outlet_Node_Name" # TODO : change ? dbranchs[-1][anode] = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = dbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newcondenserloop[sameinnode] pname = dbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject( "CONNECTORLIST", Name=newcondenserloop.Condenser_Side_Connector_List_Name) sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- dconnlist = idf.newidfobject( "CONNECTORLIST", Name=newcondenserloop.Condenser_Demand_Side_Connector_List_Name) dconnlist.Connector_1_Object_Type = "Connector:Splitter" dconnlist.Connector_1_Name = "%s_demand_splitter" % (loopname,) dconnlist.Connector_2_Object_Type = "Connector:Mixer" dconnlist.Connector_2_Name = "%s_demand_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make splitters and mixers s_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # - d_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=dconnlist.Connector_1_Name) d_splitter.obj.extend([dloop[0]] + dloop[1]) d_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=dconnlist.Connector_2_Name) d_mixer.obj.extend([dloop[-1]] + dloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- return newcondenserloop
python
def makecondenserloop(idf, loopname, sloop, dloop, testing=None): """make condenser loop with pipe components""" # -------- <testing --------- testn = 0 # -------- testing> --------- newcondenserloop = idf.newidfobject("CondenserLoop".upper(), Name=loopname) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- fields = SomeFields.c_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] # simplify naming fields1 = [field.replace( 'Condenser Side', 'Cond_Supply') for field in fields] fields1 = [field.replace('Demand Side', 'Demand') for field in fields1] fields1 = [field[:field.find('Name') - 1] for field in fields1] fields1 = [field.replace(' Node', '') for field in fields1] fields1 = [field.replace(' List', 's') for field in fields1] # old TODO : pop connectors if no parallel branches # make fieldnames in the condenser loop fieldnames = ['%s %s' % (loopname, field) for field in fields1] for fieldname, thefield in zip(fieldnames, flnames): newcondenserloop[thefield] = fieldname # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make the branch lists for this condenser loop sbranchlist = idf.newidfobject( "BRANCHLIST", Name=newcondenserloop.Condenser_Side_Branch_List_Name) dbranchlist = idf.newidfobject( "BRANCHLIST", Name=newcondenserloop.Condenser_Demand_Side_Branch_List_Name) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # add branch names to the branchlist sbranchnames = flattencopy(sloop) # sbranchnames = sloop[1] for branchname in sbranchnames: sbranchlist.obj.append(branchname) dbranchnames = flattencopy(dloop) # dbranchnames = dloop[1] for branchname in dbranchnames: dbranchlist.obj.append(branchname) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make a pipe branch for all branches in the loop # supply side sbranchs = [] for bname in sbranchnames: branch = makepipebranch(idf, bname) sbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop anode = "Component_1_Inlet_Node_Name" sameinnode = "Condenser_Side_Inlet_Node_Name" # TODO : change ? sbranchs[0][anode] = newcondenserloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Condenser_Side_Outlet_Node_Name" # TODO : change ? sbranchs[-1][anode] = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = sbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newcondenserloop[sameinnode] pname = sbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # demand side dbranchs = [] for bname in dbranchnames: branch = makepipebranch(idf, bname) dbranchs.append(branch) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in branch anode = "Component_1_Inlet_Node_Name" sameinnode = "Demand_Side_Inlet_Node_Name" # TODO : change ? dbranchs[0][anode] = newcondenserloop[sameinnode] anode = "Component_1_Outlet_Node_Name" sameoutnode = "Demand_Side_Outlet_Node_Name" # TODO : change ? dbranchs[-1][anode] = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # rename inlet outlet of endpoints of loop - rename in pipe pname = dbranchs[0]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Inlet_Node_Name = newcondenserloop[sameinnode] pname = dbranchs[-1]['Component_1_Name'] # get the pipe name apipe = idf.getobject('Pipe:Adiabatic'.upper(), pname) # get pipe apipe.Outlet_Node_Name = newcondenserloop[sameoutnode] # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # TODO : test if there are parallel branches # make the connectorlist an fill fields sconnlist = idf.newidfobject( "CONNECTORLIST", Name=newcondenserloop.Condenser_Side_Connector_List_Name) sconnlist.Connector_1_Object_Type = "Connector:Splitter" sconnlist.Connector_1_Name = "%s_supply_splitter" % (loopname,) sconnlist.Connector_2_Object_Type = "Connector:Mixer" sconnlist.Connector_2_Name = "%s_supply_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- dconnlist = idf.newidfobject( "CONNECTORLIST", Name=newcondenserloop.Condenser_Demand_Side_Connector_List_Name) dconnlist.Connector_1_Object_Type = "Connector:Splitter" dconnlist.Connector_1_Name = "%s_demand_splitter" % (loopname,) dconnlist.Connector_2_Object_Type = "Connector:Mixer" dconnlist.Connector_2_Name = "%s_demand_mixer" % (loopname,) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # make splitters and mixers s_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=sconnlist.Connector_1_Name) s_splitter.obj.extend([sloop[0]] + sloop[1]) s_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=sconnlist.Connector_2_Name) s_mixer.obj.extend([sloop[-1]] + sloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- # - d_splitter = idf.newidfobject( "CONNECTOR:SPLITTER", Name=dconnlist.Connector_1_Name) d_splitter.obj.extend([dloop[0]] + dloop[1]) d_mixer = idf.newidfobject( "CONNECTOR:MIXER", Name=dconnlist.Connector_2_Name) d_mixer.obj.extend([dloop[-1]] + dloop[1]) # -------- <testing --------- testn = doingtesting(testing, testn, newcondenserloop) if testn == None: returnnone() # -------- testing> --------- return newcondenserloop
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make condenser loop with pipe components
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L767-L960
train
santoshphilip/eppy
eppy/hvacbuilder.py
_clean_listofcomponents
def _clean_listofcomponents(listofcomponents): """force it to be a list of tuples""" def totuple(item): """return a tuple""" if isinstance(item, (tuple, list)): return item else: return (item, None) return [totuple(item) for item in listofcomponents]
python
def _clean_listofcomponents(listofcomponents): """force it to be a list of tuples""" def totuple(item): """return a tuple""" if isinstance(item, (tuple, list)): return item else: return (item, None) return [totuple(item) for item in listofcomponents]
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force it to be a list of tuples
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L962-L970
train
santoshphilip/eppy
eppy/hvacbuilder.py
_clean_listofcomponents_tuples
def _clean_listofcomponents_tuples(listofcomponents_tuples): """force 3 items in the tuple""" def to3tuple(item): """return a 3 item tuple""" if len(item) == 3: return item else: return (item[0], item[1], None) return [to3tuple(item) for item in listofcomponents_tuples]
python
def _clean_listofcomponents_tuples(listofcomponents_tuples): """force 3 items in the tuple""" def to3tuple(item): """return a 3 item tuple""" if len(item) == 3: return item else: return (item[0], item[1], None) return [to3tuple(item) for item in listofcomponents_tuples]
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force 3 items in the tuple
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L972-L980
train
santoshphilip/eppy
eppy/hvacbuilder.py
getmakeidfobject
def getmakeidfobject(idf, key, name): """get idfobject or make it if it does not exist""" idfobject = idf.getobject(key, name) if not idfobject: return idf.newidfobject(key, Name=name) else: return idfobject
python
def getmakeidfobject(idf, key, name): """get idfobject or make it if it does not exist""" idfobject = idf.getobject(key, name) if not idfobject: return idf.newidfobject(key, Name=name) else: return idfobject
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get idfobject or make it if it does not exist
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L982-L988
train
santoshphilip/eppy
eppy/hvacbuilder.py
replacebranch1
def replacebranch1(idf, loop, branchname, listofcomponents_tuples, fluid=None, debugsave=False): """do I even use this ? .... yup! I do""" if fluid is None: fluid = '' listofcomponents_tuples = _clean_listofcomponents_tuples(listofcomponents_tuples) branch = idf.getobject('BRANCH', branchname) # args are (key, name) listofcomponents = [] for comp_type, comp_name, compnode in listofcomponents_tuples: comp = getmakeidfobject(idf, comp_type.upper(), comp_name) listofcomponents.append((comp, compnode)) newbr = replacebranch(idf, loop, branch, listofcomponents, debugsave=debugsave, fluid=fluid) return newbr
python
def replacebranch1(idf, loop, branchname, listofcomponents_tuples, fluid=None, debugsave=False): """do I even use this ? .... yup! I do""" if fluid is None: fluid = '' listofcomponents_tuples = _clean_listofcomponents_tuples(listofcomponents_tuples) branch = idf.getobject('BRANCH', branchname) # args are (key, name) listofcomponents = [] for comp_type, comp_name, compnode in listofcomponents_tuples: comp = getmakeidfobject(idf, comp_type.upper(), comp_name) listofcomponents.append((comp, compnode)) newbr = replacebranch(idf, loop, branch, listofcomponents, debugsave=debugsave, fluid=fluid) return newbr
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do I even use this ? .... yup! I do
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L990-L1003
train
santoshphilip/eppy
eppy/hvacbuilder.py
replacebranch
def replacebranch(idf, loop, branch, listofcomponents, fluid=None, debugsave=False, testing=None): """It will replace the components in the branch with components in listofcomponents""" if fluid is None: fluid = '' # -------- testing --------- testn = 0 # -------- testing --------- # join them into a branch # ----------------------- # np1_inlet -> np1 -> np1_np2_node -> np2 -> np2_outlet # change the node names in the component # empty the old branch # fill in the new components with the node names into this branch listofcomponents = _clean_listofcomponents(listofcomponents) components = [item[0] for item in listofcomponents] connectcomponents(idf, listofcomponents, fluid=fluid) if debugsave: idf.savecopy("hhh3.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- fields = SomeFields.a_fields thebranch = branch componentsintobranch(idf, thebranch, listofcomponents, fluid=fluid) if debugsave: idf.savecopy("hhh4.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- # # gather all renamed nodes # # do the renaming renamenodes(idf, 'node') if debugsave: idf.savecopy("hhh7.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- # check for the end nodes of the loop if loop.key == 'AIRLOOPHVAC': fields = SomeFields.a_fields if loop.key == 'PLANTLOOP': fields = SomeFields.p_fields if loop.key == 'CONDENSERLOOP': fields = SomeFields.c_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] if fluid.upper() == 'WATER': supplyconlistname = loop[flnames[3]] # Plant_Side_Connector_List_Name or Condenser_Side_Connector_List_Name elif fluid.upper() == 'AIR': supplyconlistname = loop[flnames[1]] # Connector_List_Name' supplyconlist = idf.getobject('CONNECTORLIST', supplyconlistname) for i in range(1, 100000): # large range to hit end try: fieldname = 'Connector_%s_Object_Type' % (i,) ctype = supplyconlist[fieldname] except bunch_subclass.BadEPFieldError: break if ctype.strip() == '': break fieldname = 'Connector_%s_Name' % (i,) cname = supplyconlist[fieldname] connector = idf.getobject(ctype.upper(), cname) if connector.key == 'CONNECTOR:SPLITTER': firstbranchname = connector.Inlet_Branch_Name cbranchname = firstbranchname isfirst = True if connector.key == 'CONNECTOR:MIXER': lastbranchname = connector.Outlet_Branch_Name cbranchname = lastbranchname isfirst = False if cbranchname == thebranch.Name: # rename end nodes comps = getbranchcomponents(idf, thebranch) if isfirst: comp = comps[0] inletnodename = getnodefieldname( comp, "Inlet_Node_Name", fluid) comp[inletnodename] = [ comp[inletnodename], loop[flnames[0]]] # Plant_Side_Inlet_Node_Name else: comp = comps[-1] outletnodename = getnodefieldname( comp, "Outlet_Node_Name", fluid) comp[outletnodename] = [ comp[outletnodename], loop[flnames[1]]] # .Plant_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if fluid.upper() == 'WATER': demandconlistname = loop[flnames[7]] # .Demand_Side_Connector_List_Name demandconlist = idf.getobject('CONNECTORLIST', demandconlistname) for i in range(1, 100000): # large range to hit end try: fieldname = 'Connector_%s_Object_Type' % (i,) ctype = demandconlist[fieldname] except bunch_subclass.BadEPFieldError: break if ctype.strip() == '': break fieldname = 'Connector_%s_Name' % (i,) cname = demandconlist[fieldname] connector = idf.getobject(ctype.upper(), cname) if connector.key == 'CONNECTOR:SPLITTER': firstbranchname = connector.Inlet_Branch_Name cbranchname = firstbranchname isfirst = True if connector.key == 'CONNECTOR:MIXER': lastbranchname = connector.Outlet_Branch_Name cbranchname = lastbranchname isfirst = False if cbranchname == thebranch.Name: # rename end nodes comps = getbranchcomponents(idf, thebranch) if isfirst: comp = comps[0] inletnodename = getnodefieldname( comp, "Inlet_Node_Name", fluid) comp[inletnodename] = [ comp[inletnodename], loop[flnames[4]]] # .Demand_Side_Inlet_Node_Name if not isfirst: comp = comps[-1] outletnodename = getnodefieldname( comp, "Outlet_Node_Name", fluid) comp[outletnodename] = [ comp[outletnodename], loop[flnames[5]]] # .Demand_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if debugsave: idf.savecopy("hhh8.idf") # # gather all renamed nodes # # do the renaming renamenodes(idf, 'node') # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if debugsave: idf.savecopy("hhh9.idf") return thebranch
python
def replacebranch(idf, loop, branch, listofcomponents, fluid=None, debugsave=False, testing=None): """It will replace the components in the branch with components in listofcomponents""" if fluid is None: fluid = '' # -------- testing --------- testn = 0 # -------- testing --------- # join them into a branch # ----------------------- # np1_inlet -> np1 -> np1_np2_node -> np2 -> np2_outlet # change the node names in the component # empty the old branch # fill in the new components with the node names into this branch listofcomponents = _clean_listofcomponents(listofcomponents) components = [item[0] for item in listofcomponents] connectcomponents(idf, listofcomponents, fluid=fluid) if debugsave: idf.savecopy("hhh3.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- fields = SomeFields.a_fields thebranch = branch componentsintobranch(idf, thebranch, listofcomponents, fluid=fluid) if debugsave: idf.savecopy("hhh4.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- # # gather all renamed nodes # # do the renaming renamenodes(idf, 'node') if debugsave: idf.savecopy("hhh7.idf") # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- # check for the end nodes of the loop if loop.key == 'AIRLOOPHVAC': fields = SomeFields.a_fields if loop.key == 'PLANTLOOP': fields = SomeFields.p_fields if loop.key == 'CONDENSERLOOP': fields = SomeFields.c_fields # for use in bunch flnames = [field.replace(' ', '_') for field in fields] if fluid.upper() == 'WATER': supplyconlistname = loop[flnames[3]] # Plant_Side_Connector_List_Name or Condenser_Side_Connector_List_Name elif fluid.upper() == 'AIR': supplyconlistname = loop[flnames[1]] # Connector_List_Name' supplyconlist = idf.getobject('CONNECTORLIST', supplyconlistname) for i in range(1, 100000): # large range to hit end try: fieldname = 'Connector_%s_Object_Type' % (i,) ctype = supplyconlist[fieldname] except bunch_subclass.BadEPFieldError: break if ctype.strip() == '': break fieldname = 'Connector_%s_Name' % (i,) cname = supplyconlist[fieldname] connector = idf.getobject(ctype.upper(), cname) if connector.key == 'CONNECTOR:SPLITTER': firstbranchname = connector.Inlet_Branch_Name cbranchname = firstbranchname isfirst = True if connector.key == 'CONNECTOR:MIXER': lastbranchname = connector.Outlet_Branch_Name cbranchname = lastbranchname isfirst = False if cbranchname == thebranch.Name: # rename end nodes comps = getbranchcomponents(idf, thebranch) if isfirst: comp = comps[0] inletnodename = getnodefieldname( comp, "Inlet_Node_Name", fluid) comp[inletnodename] = [ comp[inletnodename], loop[flnames[0]]] # Plant_Side_Inlet_Node_Name else: comp = comps[-1] outletnodename = getnodefieldname( comp, "Outlet_Node_Name", fluid) comp[outletnodename] = [ comp[outletnodename], loop[flnames[1]]] # .Plant_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if fluid.upper() == 'WATER': demandconlistname = loop[flnames[7]] # .Demand_Side_Connector_List_Name demandconlist = idf.getobject('CONNECTORLIST', demandconlistname) for i in range(1, 100000): # large range to hit end try: fieldname = 'Connector_%s_Object_Type' % (i,) ctype = demandconlist[fieldname] except bunch_subclass.BadEPFieldError: break if ctype.strip() == '': break fieldname = 'Connector_%s_Name' % (i,) cname = demandconlist[fieldname] connector = idf.getobject(ctype.upper(), cname) if connector.key == 'CONNECTOR:SPLITTER': firstbranchname = connector.Inlet_Branch_Name cbranchname = firstbranchname isfirst = True if connector.key == 'CONNECTOR:MIXER': lastbranchname = connector.Outlet_Branch_Name cbranchname = lastbranchname isfirst = False if cbranchname == thebranch.Name: # rename end nodes comps = getbranchcomponents(idf, thebranch) if isfirst: comp = comps[0] inletnodename = getnodefieldname( comp, "Inlet_Node_Name", fluid) comp[inletnodename] = [ comp[inletnodename], loop[flnames[4]]] # .Demand_Side_Inlet_Node_Name if not isfirst: comp = comps[-1] outletnodename = getnodefieldname( comp, "Outlet_Node_Name", fluid) comp[outletnodename] = [ comp[outletnodename], loop[flnames[5]]] # .Demand_Side_Outlet_Node_Name # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if debugsave: idf.savecopy("hhh8.idf") # # gather all renamed nodes # # do the renaming renamenodes(idf, 'node') # -------- testing --------- testn = doingtesting(testing, testn) if testn == None: returnnone() # -------- testing --------- if debugsave: idf.savecopy("hhh9.idf") return thebranch
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It will replace the components in the branch with components in listofcomponents
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L1005-L1178
train
santoshphilip/eppy
eppy/hvacbuilder.py
main
def main(): """the main routine""" from six import StringIO import eppy.iddv7 as iddv7 IDF.setiddname(StringIO(iddv7.iddtxt)) idf1 = IDF(StringIO('')) loopname = "p_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # makeplantloop(idf1, loopname, sloop, dloop) loopname = "c_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # makecondenserloop(idf1, loopname, sloop, dloop) loopname = "a_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['zone1', 'zone2', 'zone3'] makeairloop(idf1, loopname, sloop, dloop) idf1.savecopy("hh1.idf")
python
def main(): """the main routine""" from six import StringIO import eppy.iddv7 as iddv7 IDF.setiddname(StringIO(iddv7.iddtxt)) idf1 = IDF(StringIO('')) loopname = "p_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # makeplantloop(idf1, loopname, sloop, dloop) loopname = "c_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # makecondenserloop(idf1, loopname, sloop, dloop) loopname = "a_loop" sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] dloop = ['zone1', 'zone2', 'zone3'] makeairloop(idf1, loopname, sloop, dloop) idf1.savecopy("hh1.idf")
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the main routine
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/hvacbuilder.py#L1180-L1198
train
santoshphilip/eppy
eppy/geometry/surface.py
area
def area(poly): """Area of a polygon poly""" if len(poly) < 3: # not a plane - no area return 0 total = [0, 0, 0] num = len(poly) for i in range(num): vi1 = poly[i] vi2 = poly[(i+1) % num] prod = np.cross(vi1, vi2) total[0] += prod[0] total[1] += prod[1] total[2] += prod[2] if total == [0, 0, 0]: # points are in a straight line - no area return 0 result = np.dot(total, unit_normal(poly[0], poly[1], poly[2])) return abs(result/2)
python
def area(poly): """Area of a polygon poly""" if len(poly) < 3: # not a plane - no area return 0 total = [0, 0, 0] num = len(poly) for i in range(num): vi1 = poly[i] vi2 = poly[(i+1) % num] prod = np.cross(vi1, vi2) total[0] += prod[0] total[1] += prod[1] total[2] += prod[2] if total == [0, 0, 0]: # points are in a straight line - no area return 0 result = np.dot(total, unit_normal(poly[0], poly[1], poly[2])) return abs(result/2)
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Area of a polygon poly
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L32-L48
train
santoshphilip/eppy
eppy/geometry/surface.py
unit_normal
def unit_normal(pt_a, pt_b, pt_c): """unit normal vector of plane defined by points pt_a, pt_b, and pt_c""" x_val = np.linalg.det([[1, pt_a[1], pt_a[2]], [1, pt_b[1], pt_b[2]], [1, pt_c[1], pt_c[2]]]) y_val = np.linalg.det([[pt_a[0], 1, pt_a[2]], [pt_b[0], 1, pt_b[2]], [pt_c[0], 1, pt_c[2]]]) z_val = np.linalg.det([[pt_a[0], pt_a[1], 1], [pt_b[0], pt_b[1], 1], [pt_c[0], pt_c[1], 1]]) magnitude = (x_val**2 + y_val**2 + z_val**2)**.5 mag = (x_val/magnitude, y_val/magnitude, z_val/magnitude) if magnitude < 0.00000001: mag = (0, 0, 0) return mag
python
def unit_normal(pt_a, pt_b, pt_c): """unit normal vector of plane defined by points pt_a, pt_b, and pt_c""" x_val = np.linalg.det([[1, pt_a[1], pt_a[2]], [1, pt_b[1], pt_b[2]], [1, pt_c[1], pt_c[2]]]) y_val = np.linalg.det([[pt_a[0], 1, pt_a[2]], [pt_b[0], 1, pt_b[2]], [pt_c[0], 1, pt_c[2]]]) z_val = np.linalg.det([[pt_a[0], pt_a[1], 1], [pt_b[0], pt_b[1], 1], [pt_c[0], pt_c[1], 1]]) magnitude = (x_val**2 + y_val**2 + z_val**2)**.5 mag = (x_val/magnitude, y_val/magnitude, z_val/magnitude) if magnitude < 0.00000001: mag = (0, 0, 0) return mag
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unit normal vector of plane defined by points pt_a, pt_b, and pt_c
[ "unit", "normal", "vector", "of", "plane", "defined", "by", "points", "pt_a", "pt_b", "and", "pt_c" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L50-L59
train
santoshphilip/eppy
eppy/geometry/surface.py
width
def width(poly): """Width of a polygon poly""" num = len(poly) - 1 if abs(poly[num][2] - poly[0][2]) < abs(poly[1][2] - poly[0][2]): return dist(poly[num], poly[0]) elif abs(poly[num][2] - poly[0][2]) > abs(poly[1][2] - poly[0][2]): return dist(poly[1], poly[0]) else: return max(dist(poly[num], poly[0]), dist(poly[1], poly[0]))
python
def width(poly): """Width of a polygon poly""" num = len(poly) - 1 if abs(poly[num][2] - poly[0][2]) < abs(poly[1][2] - poly[0][2]): return dist(poly[num], poly[0]) elif abs(poly[num][2] - poly[0][2]) > abs(poly[1][2] - poly[0][2]): return dist(poly[1], poly[0]) else: return max(dist(poly[num], poly[0]), dist(poly[1], poly[0]))
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Width of a polygon poly
[ "Width", "of", "a", "polygon", "poly" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L68-L75
train
santoshphilip/eppy
eppy/geometry/surface.py
height
def height(poly): """Height of a polygon poly""" num = len(poly) - 1 if abs(poly[num][2] - poly[0][2]) > abs(poly[1][2] - poly[0][2]): return dist(poly[num], poly[0]) elif abs(poly[num][2] - poly[0][2]) < abs(poly[1][2] - poly[0][2]): return dist(poly[1], poly[0]) else: return min(dist(poly[num], poly[0]), dist(poly[1], poly[0]))
python
def height(poly): """Height of a polygon poly""" num = len(poly) - 1 if abs(poly[num][2] - poly[0][2]) > abs(poly[1][2] - poly[0][2]): return dist(poly[num], poly[0]) elif abs(poly[num][2] - poly[0][2]) < abs(poly[1][2] - poly[0][2]): return dist(poly[1], poly[0]) else: return min(dist(poly[num], poly[0]), dist(poly[1], poly[0]))
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Height of a polygon poly
[ "Height", "of", "a", "polygon", "poly" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L78-L86
train
santoshphilip/eppy
eppy/geometry/surface.py
angle2vecs
def angle2vecs(vec1, vec2): """angle between two vectors""" # vector a * vector b = |a|*|b|* cos(angle between vector a and vector b) dot = np.dot(vec1, vec2) vec1_modulus = np.sqrt(np.multiply(vec1, vec1).sum()) vec2_modulus = np.sqrt(np.multiply(vec2, vec2).sum()) if (vec1_modulus * vec2_modulus) == 0: cos_angle = 1 else: cos_angle = dot / (vec1_modulus * vec2_modulus) return math.degrees(acos(cos_angle))
python
def angle2vecs(vec1, vec2): """angle between two vectors""" # vector a * vector b = |a|*|b|* cos(angle between vector a and vector b) dot = np.dot(vec1, vec2) vec1_modulus = np.sqrt(np.multiply(vec1, vec1).sum()) vec2_modulus = np.sqrt(np.multiply(vec2, vec2).sum()) if (vec1_modulus * vec2_modulus) == 0: cos_angle = 1 else: cos_angle = dot / (vec1_modulus * vec2_modulus) return math.degrees(acos(cos_angle))
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angle between two vectors
[ "angle", "between", "two", "vectors" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L89-L98
train
santoshphilip/eppy
eppy/geometry/surface.py
azimuth
def azimuth(poly): """Azimuth of a polygon poly""" num = len(poly) - 1 vec = unit_normal(poly[0], poly[1], poly[num]) vec_azi = np.array([vec[0], vec[1], 0]) vec_n = np.array([0, 1, 0]) # update by Santosh # angle2vecs gives the smallest angle between the vectors # so for a west wall angle2vecs will give 90 # the following 'if' statement will make sure 270 is returned x_vector = vec_azi[0] if x_vector < 0: return 360 - angle2vecs(vec_azi, vec_n) else: return angle2vecs(vec_azi, vec_n)
python
def azimuth(poly): """Azimuth of a polygon poly""" num = len(poly) - 1 vec = unit_normal(poly[0], poly[1], poly[num]) vec_azi = np.array([vec[0], vec[1], 0]) vec_n = np.array([0, 1, 0]) # update by Santosh # angle2vecs gives the smallest angle between the vectors # so for a west wall angle2vecs will give 90 # the following 'if' statement will make sure 270 is returned x_vector = vec_azi[0] if x_vector < 0: return 360 - angle2vecs(vec_azi, vec_n) else: return angle2vecs(vec_azi, vec_n)
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Azimuth of a polygon poly
[ "Azimuth", "of", "a", "polygon", "poly" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L101-L115
train
santoshphilip/eppy
eppy/geometry/surface.py
tilt
def tilt(poly): """Tilt of a polygon poly""" num = len(poly) - 1 vec = unit_normal(poly[0], poly[1], poly[num]) vec_alt = np.array([vec[0], vec[1], vec[2]]) vec_z = np.array([0, 0, 1]) # return (90 - angle2vecs(vec_alt, vec_z)) # update by Santosh return angle2vecs(vec_alt, vec_z)
python
def tilt(poly): """Tilt of a polygon poly""" num = len(poly) - 1 vec = unit_normal(poly[0], poly[1], poly[num]) vec_alt = np.array([vec[0], vec[1], vec[2]]) vec_z = np.array([0, 0, 1]) # return (90 - angle2vecs(vec_alt, vec_z)) # update by Santosh return angle2vecs(vec_alt, vec_z)
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Tilt of a polygon poly
[ "Tilt", "of", "a", "polygon", "poly" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/geometry/surface.py#L117-L124
train
santoshphilip/eppy
eppy/iddgaps.py
getfields
def getfields(comm): """get all the fields that have the key 'field' """ fields = [] for field in comm: if 'field' in field: fields.append(field) return fields
python
def getfields(comm): """get all the fields that have the key 'field' """ fields = [] for field in comm: if 'field' in field: fields.append(field) return fields
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get all the fields that have the key 'field'
[ "get", "all", "the", "fields", "that", "have", "the", "key", "field" ]
55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/iddgaps.py#L63-L69
train
santoshphilip/eppy
eppy/iddgaps.py
repeatingfieldsnames
def repeatingfieldsnames(fields): """get the names of the repeating fields""" fnames = [field['field'][0] for field in fields] fnames = [bunchhelpers.onlylegalchar(fname) for fname in fnames] fnames = [fname for fname in fnames if bunchhelpers.intinlist(fname.split())] fnames = [(bunchhelpers.replaceint(fname), None) for fname in fnames] dct = dict(fnames) repnames = fnames[:len(list(dct.keys()))] return repnames
python
def repeatingfieldsnames(fields): """get the names of the repeating fields""" fnames = [field['field'][0] for field in fields] fnames = [bunchhelpers.onlylegalchar(fname) for fname in fnames] fnames = [fname for fname in fnames if bunchhelpers.intinlist(fname.split())] fnames = [(bunchhelpers.replaceint(fname), None) for fname in fnames] dct = dict(fnames) repnames = fnames[:len(list(dct.keys()))] return repnames
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get the names of the repeating fields
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/iddgaps.py#L71-L79
train
santoshphilip/eppy
eppy/iddgaps.py
missingkeys_standard
def missingkeys_standard(commdct, dtls, skiplist=None): """put missing keys in commdct for standard objects return a list of keys where it is unable to do so commdct is not returned, but is updated""" if skiplist == None: skiplist = [] # find objects where all the fields are not named gkeys = [dtls[i] for i in range(len(dtls)) if commdct[i].count({}) > 2] nofirstfields = [] # operatie on those fields for key_txt in gkeys: if key_txt in skiplist: continue # print key_txt # for a function, pass comm as a variable key_i = dtls.index(key_txt.upper()) comm = commdct[key_i] # get all fields fields = getfields(comm) # get repeating field names repnames = repeatingfieldsnames(fields) try: first = repnames[0][0] % (1, ) except IndexError: nofirstfields.append(key_txt) continue # print first # get all comments of the first repeating field names firstnames = [repname[0] % (1, ) for repname in repnames] fcomments = [field for field in fields if bunchhelpers.onlylegalchar(field['field'][0]) in firstnames] fcomments = [dict(fcomment) for fcomment in fcomments] for cmt in fcomments: fld = cmt['field'][0] fld = bunchhelpers.onlylegalchar(fld) fld = bunchhelpers.replaceint(fld) cmt['field'] = [fld] for i, cmt in enumerate(comm[1:]): thefield = cmt['field'][0] thefield = bunchhelpers.onlylegalchar(thefield) if thefield == first: break first_i = i + 1 newfields = [] for i in range(1, len(comm[first_i:]) // len(repnames) + 1): for fcomment in fcomments: nfcomment = dict(fcomment) fld = nfcomment['field'][0] fld = fld % (i, ) nfcomment['field'] = [fld] newfields.append(nfcomment) for i, cmt in enumerate(comm): if i < first_i: continue else: afield = newfields.pop(0) comm[i] = afield commdct[key_i] = comm return nofirstfields
python
def missingkeys_standard(commdct, dtls, skiplist=None): """put missing keys in commdct for standard objects return a list of keys where it is unable to do so commdct is not returned, but is updated""" if skiplist == None: skiplist = [] # find objects where all the fields are not named gkeys = [dtls[i] for i in range(len(dtls)) if commdct[i].count({}) > 2] nofirstfields = [] # operatie on those fields for key_txt in gkeys: if key_txt in skiplist: continue # print key_txt # for a function, pass comm as a variable key_i = dtls.index(key_txt.upper()) comm = commdct[key_i] # get all fields fields = getfields(comm) # get repeating field names repnames = repeatingfieldsnames(fields) try: first = repnames[0][0] % (1, ) except IndexError: nofirstfields.append(key_txt) continue # print first # get all comments of the first repeating field names firstnames = [repname[0] % (1, ) for repname in repnames] fcomments = [field for field in fields if bunchhelpers.onlylegalchar(field['field'][0]) in firstnames] fcomments = [dict(fcomment) for fcomment in fcomments] for cmt in fcomments: fld = cmt['field'][0] fld = bunchhelpers.onlylegalchar(fld) fld = bunchhelpers.replaceint(fld) cmt['field'] = [fld] for i, cmt in enumerate(comm[1:]): thefield = cmt['field'][0] thefield = bunchhelpers.onlylegalchar(thefield) if thefield == first: break first_i = i + 1 newfields = [] for i in range(1, len(comm[first_i:]) // len(repnames) + 1): for fcomment in fcomments: nfcomment = dict(fcomment) fld = nfcomment['field'][0] fld = fld % (i, ) nfcomment['field'] = [fld] newfields.append(nfcomment) for i, cmt in enumerate(comm): if i < first_i: continue else: afield = newfields.pop(0) comm[i] = afield commdct[key_i] = comm return nofirstfields
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put missing keys in commdct for standard objects return a list of keys where it is unable to do so commdct is not returned, but is updated
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/iddgaps.py#L82-L150
train
santoshphilip/eppy
eppy/iddgaps.py
missingkeys_nonstandard
def missingkeys_nonstandard(block, commdct, dtls, objectlist, afield='afiled %s'): """This is an object list where thre is no first field name to give a hint of what the first field name should be""" afield = 'afield %s' for key_txt in objectlist: key_i = dtls.index(key_txt.upper()) comm = commdct[key_i] if block: blk = block[key_i] for i, cmt in enumerate(comm): if cmt == {}: first_i = i break for i, cmt in enumerate(comm): if i >= first_i: if block: comm[i]['field'] = ['%s' % (blk[i])] else: comm[i]['field'] = [afield % (i - first_i + 1,),]
python
def missingkeys_nonstandard(block, commdct, dtls, objectlist, afield='afiled %s'): """This is an object list where thre is no first field name to give a hint of what the first field name should be""" afield = 'afield %s' for key_txt in objectlist: key_i = dtls.index(key_txt.upper()) comm = commdct[key_i] if block: blk = block[key_i] for i, cmt in enumerate(comm): if cmt == {}: first_i = i break for i, cmt in enumerate(comm): if i >= first_i: if block: comm[i]['field'] = ['%s' % (blk[i])] else: comm[i]['field'] = [afield % (i - first_i + 1,),]
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This is an object list where thre is no first field name to give a hint of what the first field name should be
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55410ff7c11722f35bc4331ff5e00a0b86f787e1
https://github.com/santoshphilip/eppy/blob/55410ff7c11722f35bc4331ff5e00a0b86f787e1/eppy/iddgaps.py#L152-L170
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
get_event_loop
def get_event_loop(): """Return a EventLoop instance. A new instance is created for each new HTTP request. We determine that we're in a new request by inspecting os.environ, which is reset at the start of each request. Also, each thread gets its own loop. """ ev = _state.event_loop if not os.getenv(_EVENT_LOOP_KEY) and ev is not None: ev.clear() _state.event_loop = None ev = None if ev is None: ev = EventLoop() _state.event_loop = ev os.environ[_EVENT_LOOP_KEY] = '1' return ev
python
def get_event_loop(): """Return a EventLoop instance. A new instance is created for each new HTTP request. We determine that we're in a new request by inspecting os.environ, which is reset at the start of each request. Also, each thread gets its own loop. """ ev = _state.event_loop if not os.getenv(_EVENT_LOOP_KEY) and ev is not None: ev.clear() _state.event_loop = None ev = None if ev is None: ev = EventLoop() _state.event_loop = ev os.environ[_EVENT_LOOP_KEY] = '1' return ev
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Return a EventLoop instance. A new instance is created for each new HTTP request. We determine that we're in a new request by inspecting os.environ, which is reset at the start of each request. Also, each thread gets its own loop.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L293-L309
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.clear
def clear(self): """Remove all pending events without running any.""" while self.current or self.idlers or self.queue or self.rpcs: current = self.current idlers = self.idlers queue = self.queue rpcs = self.rpcs _logging_debug('Clearing stale EventLoop instance...') if current: _logging_debug(' current = %s', current) if idlers: _logging_debug(' idlers = %s', idlers) if queue: _logging_debug(' queue = %s', queue) if rpcs: _logging_debug(' rpcs = %s', rpcs) self.__init__() current.clear() idlers.clear() queue[:] = [] rpcs.clear() _logging_debug('Cleared')
python
def clear(self): """Remove all pending events without running any.""" while self.current or self.idlers or self.queue or self.rpcs: current = self.current idlers = self.idlers queue = self.queue rpcs = self.rpcs _logging_debug('Clearing stale EventLoop instance...') if current: _logging_debug(' current = %s', current) if idlers: _logging_debug(' idlers = %s', idlers) if queue: _logging_debug(' queue = %s', queue) if rpcs: _logging_debug(' rpcs = %s', rpcs) self.__init__() current.clear() idlers.clear() queue[:] = [] rpcs.clear() _logging_debug('Cleared')
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Remove all pending events without running any.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L90-L111
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.insort_event_right
def insort_event_right(self, event, lo=0, hi=None): """Insert event in queue, and keep it sorted assuming queue is sorted. If event is already in queue, insert it to the right of the rightmost event (to keep FIFO order). Optional args lo (default 0) and hi (default len(a)) bound the slice of a to be searched. Args: event: a (time in sec since unix epoch, callback, args, kwds) tuple. """ if lo < 0: raise ValueError('lo must be non-negative') if hi is None: hi = len(self.queue) while lo < hi: mid = (lo + hi) // 2 if event[0] < self.queue[mid][0]: hi = mid else: lo = mid + 1 self.queue.insert(lo, event)
python
def insort_event_right(self, event, lo=0, hi=None): """Insert event in queue, and keep it sorted assuming queue is sorted. If event is already in queue, insert it to the right of the rightmost event (to keep FIFO order). Optional args lo (default 0) and hi (default len(a)) bound the slice of a to be searched. Args: event: a (time in sec since unix epoch, callback, args, kwds) tuple. """ if lo < 0: raise ValueError('lo must be non-negative') if hi is None: hi = len(self.queue) while lo < hi: mid = (lo + hi) // 2 if event[0] < self.queue[mid][0]: hi = mid else: lo = mid + 1 self.queue.insert(lo, event)
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Insert event in queue, and keep it sorted assuming queue is sorted. If event is already in queue, insert it to the right of the rightmost event (to keep FIFO order). Optional args lo (default 0) and hi (default len(a)) bound the slice of a to be searched. Args: event: a (time in sec since unix epoch, callback, args, kwds) tuple.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L113-L136
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.queue_call
def queue_call(self, delay, callback, *args, **kwds): """Schedule a function call at a specific time in the future.""" if delay is None: self.current.append((callback, args, kwds)) return if delay < 1e9: when = delay + self.clock.now() else: # Times over a billion seconds are assumed to be absolute. when = delay self.insort_event_right((when, callback, args, kwds))
python
def queue_call(self, delay, callback, *args, **kwds): """Schedule a function call at a specific time in the future.""" if delay is None: self.current.append((callback, args, kwds)) return if delay < 1e9: when = delay + self.clock.now() else: # Times over a billion seconds are assumed to be absolute. when = delay self.insort_event_right((when, callback, args, kwds))
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Schedule a function call at a specific time in the future.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L138-L148
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.queue_rpc
def queue_rpc(self, rpc, callback=None, *args, **kwds): """Schedule an RPC with an optional callback. The caller must have previously sent the call to the service. The optional callback is called with the remaining arguments. NOTE: If the rpc is a MultiRpc, the callback will be called once for each sub-RPC. TODO: Is this a good idea? """ if rpc is None: return if rpc.state not in (_RUNNING, _FINISHING): raise RuntimeError('rpc must be sent to service before queueing') if isinstance(rpc, datastore_rpc.MultiRpc): rpcs = rpc.rpcs if len(rpcs) > 1: # Don't call the callback until all sub-rpcs have completed. rpc.__done = False def help_multi_rpc_along(r=rpc, c=callback, a=args, k=kwds): if r.state == _FINISHING and not r.__done: r.__done = True c(*a, **k) # TODO: And again, what about exceptions? callback = help_multi_rpc_along args = () kwds = {} else: rpcs = [rpc] for rpc in rpcs: self.rpcs[rpc] = (callback, args, kwds)
python
def queue_rpc(self, rpc, callback=None, *args, **kwds): """Schedule an RPC with an optional callback. The caller must have previously sent the call to the service. The optional callback is called with the remaining arguments. NOTE: If the rpc is a MultiRpc, the callback will be called once for each sub-RPC. TODO: Is this a good idea? """ if rpc is None: return if rpc.state not in (_RUNNING, _FINISHING): raise RuntimeError('rpc must be sent to service before queueing') if isinstance(rpc, datastore_rpc.MultiRpc): rpcs = rpc.rpcs if len(rpcs) > 1: # Don't call the callback until all sub-rpcs have completed. rpc.__done = False def help_multi_rpc_along(r=rpc, c=callback, a=args, k=kwds): if r.state == _FINISHING and not r.__done: r.__done = True c(*a, **k) # TODO: And again, what about exceptions? callback = help_multi_rpc_along args = () kwds = {} else: rpcs = [rpc] for rpc in rpcs: self.rpcs[rpc] = (callback, args, kwds)
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Schedule an RPC with an optional callback. The caller must have previously sent the call to the service. The optional callback is called with the remaining arguments. NOTE: If the rpc is a MultiRpc, the callback will be called once for each sub-RPC. TODO: Is this a good idea?
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L150-L180
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.add_idle
def add_idle(self, callback, *args, **kwds): """Add an idle callback. An idle callback can return True, False or None. These mean: - None: remove the callback (don't reschedule) - False: the callback did no work; reschedule later - True: the callback did some work; reschedule soon If the callback raises an exception, the traceback is logged and the callback is removed. """ self.idlers.append((callback, args, kwds))
python
def add_idle(self, callback, *args, **kwds): """Add an idle callback. An idle callback can return True, False or None. These mean: - None: remove the callback (don't reschedule) - False: the callback did no work; reschedule later - True: the callback did some work; reschedule soon If the callback raises an exception, the traceback is logged and the callback is removed. """ self.idlers.append((callback, args, kwds))
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Add an idle callback. An idle callback can return True, False or None. These mean: - None: remove the callback (don't reschedule) - False: the callback did no work; reschedule later - True: the callback did some work; reschedule soon If the callback raises an exception, the traceback is logged and the callback is removed.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L182-L194
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.run_idle
def run_idle(self): """Run one of the idle callbacks. Returns: True if one was called, False if no idle callback was called. """ if not self.idlers or self.inactive >= len(self.idlers): return False idler = self.idlers.popleft() callback, args, kwds = idler _logging_debug('idler: %s', callback.__name__) res = callback(*args, **kwds) # See add_idle() for the meaning of the callback return value. if res is not None: if res: self.inactive = 0 else: self.inactive += 1 self.idlers.append(idler) else: _logging_debug('idler %s removed', callback.__name__) return True
python
def run_idle(self): """Run one of the idle callbacks. Returns: True if one was called, False if no idle callback was called. """ if not self.idlers or self.inactive >= len(self.idlers): return False idler = self.idlers.popleft() callback, args, kwds = idler _logging_debug('idler: %s', callback.__name__) res = callback(*args, **kwds) # See add_idle() for the meaning of the callback return value. if res is not None: if res: self.inactive = 0 else: self.inactive += 1 self.idlers.append(idler) else: _logging_debug('idler %s removed', callback.__name__) return True
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Run one of the idle callbacks. Returns: True if one was called, False if no idle callback was called.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L196-L217
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.run0
def run0(self): """Run one item (a callback or an RPC wait_any). Returns: A time to sleep if something happened (may be 0); None if all queues are empty. """ if self.current: self.inactive = 0 callback, args, kwds = self.current.popleft() _logging_debug('nowevent: %s', callback.__name__) callback(*args, **kwds) return 0 if self.run_idle(): return 0 delay = None if self.queue: delay = self.queue[0][0] - self.clock.now() if delay <= 0: self.inactive = 0 _, callback, args, kwds = self.queue.pop(0) _logging_debug('event: %s', callback.__name__) callback(*args, **kwds) # TODO: What if it raises an exception? return 0 if self.rpcs: self.inactive = 0 rpc = datastore_rpc.MultiRpc.wait_any(self.rpcs) if rpc is not None: _logging_debug('rpc: %s.%s', rpc.service, rpc.method) # Yes, wait_any() may return None even for a non-empty argument. # But no, it won't ever return an RPC not in its argument. if rpc not in self.rpcs: raise RuntimeError('rpc %r was not given to wait_any as a choice %r' % (rpc, self.rpcs)) callback, args, kwds = self.rpcs[rpc] del self.rpcs[rpc] if callback is not None: callback(*args, **kwds) # TODO: Again, what about exceptions? return 0 return delay
python
def run0(self): """Run one item (a callback or an RPC wait_any). Returns: A time to sleep if something happened (may be 0); None if all queues are empty. """ if self.current: self.inactive = 0 callback, args, kwds = self.current.popleft() _logging_debug('nowevent: %s', callback.__name__) callback(*args, **kwds) return 0 if self.run_idle(): return 0 delay = None if self.queue: delay = self.queue[0][0] - self.clock.now() if delay <= 0: self.inactive = 0 _, callback, args, kwds = self.queue.pop(0) _logging_debug('event: %s', callback.__name__) callback(*args, **kwds) # TODO: What if it raises an exception? return 0 if self.rpcs: self.inactive = 0 rpc = datastore_rpc.MultiRpc.wait_any(self.rpcs) if rpc is not None: _logging_debug('rpc: %s.%s', rpc.service, rpc.method) # Yes, wait_any() may return None even for a non-empty argument. # But no, it won't ever return an RPC not in its argument. if rpc not in self.rpcs: raise RuntimeError('rpc %r was not given to wait_any as a choice %r' % (rpc, self.rpcs)) callback, args, kwds = self.rpcs[rpc] del self.rpcs[rpc] if callback is not None: callback(*args, **kwds) # TODO: Again, what about exceptions? return 0 return delay
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L219-L260
train
GoogleCloudPlatform/datastore-ndb-python
ndb/eventloop.py
EventLoop.run1
def run1(self): """Run one item (a callback or an RPC wait_any) or sleep. Returns: True if something happened; False if all queues are empty. """ delay = self.run0() if delay is None: return False if delay > 0: self.clock.sleep(delay) return True
python
def run1(self): """Run one item (a callback or an RPC wait_any) or sleep. Returns: True if something happened; False if all queues are empty. """ delay = self.run0() if delay is None: return False if delay > 0: self.clock.sleep(delay) return True
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Run one item (a callback or an RPC wait_any) or sleep. Returns: True if something happened; False if all queues are empty.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/eventloop.py#L262-L273
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
_args_to_val
def _args_to_val(func, args): """Helper for GQL parsing to extract values from GQL expressions. This can extract the value from a GQL literal, return a Parameter for a GQL bound parameter (:1 or :foo), and interprets casts like KEY(...) and plain lists of values like (1, 2, 3). Args: func: A string indicating what kind of thing this is. args: One or more GQL values, each integer, string, or GQL literal. """ from .google_imports import gql # Late import, to avoid name conflict. vals = [] for arg in args: if isinstance(arg, (int, long, basestring)): val = Parameter(arg) elif isinstance(arg, gql.Literal): val = arg.Get() else: raise TypeError('Unexpected arg (%r)' % arg) vals.append(val) if func == 'nop': if len(vals) != 1: raise TypeError('"nop" requires exactly one value') return vals[0] # May be a Parameter pfunc = ParameterizedFunction(func, vals) if pfunc.is_parameterized(): return pfunc else: return pfunc.resolve({}, {})
python
def _args_to_val(func, args): """Helper for GQL parsing to extract values from GQL expressions. This can extract the value from a GQL literal, return a Parameter for a GQL bound parameter (:1 or :foo), and interprets casts like KEY(...) and plain lists of values like (1, 2, 3). Args: func: A string indicating what kind of thing this is. args: One or more GQL values, each integer, string, or GQL literal. """ from .google_imports import gql # Late import, to avoid name conflict. vals = [] for arg in args: if isinstance(arg, (int, long, basestring)): val = Parameter(arg) elif isinstance(arg, gql.Literal): val = arg.Get() else: raise TypeError('Unexpected arg (%r)' % arg) vals.append(val) if func == 'nop': if len(vals) != 1: raise TypeError('"nop" requires exactly one value') return vals[0] # May be a Parameter pfunc = ParameterizedFunction(func, vals) if pfunc.is_parameterized(): return pfunc else: return pfunc.resolve({}, {})
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Helper for GQL parsing to extract values from GQL expressions. This can extract the value from a GQL literal, return a Parameter for a GQL bound parameter (:1 or :foo), and interprets casts like KEY(...) and plain lists of values like (1, 2, 3). Args: func: A string indicating what kind of thing this is. args: One or more GQL values, each integer, string, or GQL literal.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L700-L729
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
_get_prop_from_modelclass
def _get_prop_from_modelclass(modelclass, name): """Helper for FQL parsing to turn a property name into a property object. Args: modelclass: The model class specified in the query. name: The property name. This may contain dots which indicate sub-properties of structured properties. Returns: A Property object. Raises: KeyError if the property doesn't exist and the model clas doesn't derive from Expando. """ if name == '__key__': return modelclass._key parts = name.split('.') part, more = parts[0], parts[1:] prop = modelclass._properties.get(part) if prop is None: if issubclass(modelclass, model.Expando): prop = model.GenericProperty(part) else: raise TypeError('Model %s has no property named %r' % (modelclass._get_kind(), part)) while more: part = more.pop(0) if not isinstance(prop, model.StructuredProperty): raise TypeError('Model %s has no property named %r' % (modelclass._get_kind(), part)) maybe = getattr(prop, part, None) if isinstance(maybe, model.Property) and maybe._name == part: prop = maybe else: maybe = prop._modelclass._properties.get(part) if maybe is not None: # Must get it this way to get the copy with the long name. # (See StructuredProperty.__getattr__() for details.) prop = getattr(prop, maybe._code_name) else: if issubclass(prop._modelclass, model.Expando) and not more: prop = model.GenericProperty() prop._name = name # Bypass the restriction on dots. else: raise KeyError('Model %s has no property named %r' % (prop._modelclass._get_kind(), part)) return prop
python
def _get_prop_from_modelclass(modelclass, name): """Helper for FQL parsing to turn a property name into a property object. Args: modelclass: The model class specified in the query. name: The property name. This may contain dots which indicate sub-properties of structured properties. Returns: A Property object. Raises: KeyError if the property doesn't exist and the model clas doesn't derive from Expando. """ if name == '__key__': return modelclass._key parts = name.split('.') part, more = parts[0], parts[1:] prop = modelclass._properties.get(part) if prop is None: if issubclass(modelclass, model.Expando): prop = model.GenericProperty(part) else: raise TypeError('Model %s has no property named %r' % (modelclass._get_kind(), part)) while more: part = more.pop(0) if not isinstance(prop, model.StructuredProperty): raise TypeError('Model %s has no property named %r' % (modelclass._get_kind(), part)) maybe = getattr(prop, part, None) if isinstance(maybe, model.Property) and maybe._name == part: prop = maybe else: maybe = prop._modelclass._properties.get(part) if maybe is not None: # Must get it this way to get the copy with the long name. # (See StructuredProperty.__getattr__() for details.) prop = getattr(prop, maybe._code_name) else: if issubclass(prop._modelclass, model.Expando) and not more: prop = model.GenericProperty() prop._name = name # Bypass the restriction on dots. else: raise KeyError('Model %s has no property named %r' % (prop._modelclass._get_kind(), part)) return prop
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L732-L782
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
gql
def gql(query_string, *args, **kwds): """Parse a GQL query string. Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. *args, **kwds: If present, used to call bind(). Returns: An instance of query_class. """ qry = _gql(query_string) if args or kwds: qry = qry._bind(args, kwds) return qry
python
def gql(query_string, *args, **kwds): """Parse a GQL query string. Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. *args, **kwds: If present, used to call bind(). Returns: An instance of query_class. """ qry = _gql(query_string) if args or kwds: qry = qry._bind(args, kwds) return qry
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Parse a GQL query string. Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. *args, **kwds: If present, used to call bind(). Returns: An instance of query_class.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1501-L1514
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
_gql
def _gql(query_string, query_class=Query): """Parse a GQL query string (internal version). Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. query_class: Optional class to use, default Query. Returns: An instance of query_class. """ from .google_imports import gql # Late import, to avoid name conflict. gql_qry = gql.GQL(query_string) kind = gql_qry.kind() if kind is None: # The query must be lacking a "FROM <kind>" class. Let Expando # stand in for the model class (it won't actually be used to # construct the results). modelclass = model.Expando else: modelclass = model.Model._lookup_model( kind, tasklets.get_context()._conn.adapter.default_model) # Adjust kind to the kind of the model class. kind = modelclass._get_kind() ancestor = None flt = gql_qry.filters() filters = list(modelclass._default_filters()) for name_op in sorted(flt): name, op = name_op values = flt[name_op] op = op.lower() if op == 'is' and name == gql.GQL._GQL__ANCESTOR: if len(values) != 1: raise ValueError('"is" requires exactly one value') [(func, args)] = values ancestor = _args_to_val(func, args) continue if op not in _OPS: raise NotImplementedError('Operation %r is not supported.' % op) for (func, args) in values: val = _args_to_val(func, args) prop = _get_prop_from_modelclass(modelclass, name) if prop._name != name: raise RuntimeError('Whoa! _get_prop_from_modelclass(%s, %r) ' 'returned a property whose name is %r?!' % (modelclass.__name__, name, prop._name)) if isinstance(val, ParameterizedThing): node = ParameterNode(prop, op, val) elif op == 'in': node = prop._IN(val) else: node = prop._comparison(op, val) filters.append(node) if filters: filters = ConjunctionNode(*filters) else: filters = None orders = _orderings_to_orders(gql_qry.orderings(), modelclass) offset = gql_qry.offset() limit = gql_qry.limit() if limit < 0: limit = None keys_only = gql_qry._keys_only if not keys_only: keys_only = None options = QueryOptions(offset=offset, limit=limit, keys_only=keys_only) projection = gql_qry.projection() if gql_qry.is_distinct(): group_by = projection else: group_by = None qry = query_class(kind=kind, ancestor=ancestor, filters=filters, orders=orders, default_options=options, projection=projection, group_by=group_by) return qry
python
def _gql(query_string, query_class=Query): """Parse a GQL query string (internal version). Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. query_class: Optional class to use, default Query. Returns: An instance of query_class. """ from .google_imports import gql # Late import, to avoid name conflict. gql_qry = gql.GQL(query_string) kind = gql_qry.kind() if kind is None: # The query must be lacking a "FROM <kind>" class. Let Expando # stand in for the model class (it won't actually be used to # construct the results). modelclass = model.Expando else: modelclass = model.Model._lookup_model( kind, tasklets.get_context()._conn.adapter.default_model) # Adjust kind to the kind of the model class. kind = modelclass._get_kind() ancestor = None flt = gql_qry.filters() filters = list(modelclass._default_filters()) for name_op in sorted(flt): name, op = name_op values = flt[name_op] op = op.lower() if op == 'is' and name == gql.GQL._GQL__ANCESTOR: if len(values) != 1: raise ValueError('"is" requires exactly one value') [(func, args)] = values ancestor = _args_to_val(func, args) continue if op not in _OPS: raise NotImplementedError('Operation %r is not supported.' % op) for (func, args) in values: val = _args_to_val(func, args) prop = _get_prop_from_modelclass(modelclass, name) if prop._name != name: raise RuntimeError('Whoa! _get_prop_from_modelclass(%s, %r) ' 'returned a property whose name is %r?!' % (modelclass.__name__, name, prop._name)) if isinstance(val, ParameterizedThing): node = ParameterNode(prop, op, val) elif op == 'in': node = prop._IN(val) else: node = prop._comparison(op, val) filters.append(node) if filters: filters = ConjunctionNode(*filters) else: filters = None orders = _orderings_to_orders(gql_qry.orderings(), modelclass) offset = gql_qry.offset() limit = gql_qry.limit() if limit < 0: limit = None keys_only = gql_qry._keys_only if not keys_only: keys_only = None options = QueryOptions(offset=offset, limit=limit, keys_only=keys_only) projection = gql_qry.projection() if gql_qry.is_distinct(): group_by = projection else: group_by = None qry = query_class(kind=kind, ancestor=ancestor, filters=filters, orders=orders, default_options=options, projection=projection, group_by=group_by) return qry
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Parse a GQL query string (internal version). Args: query_string: Full GQL query, e.g. 'SELECT * FROM Kind WHERE prop = 1'. query_class: Optional class to use, default Query. Returns: An instance of query_class.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1518-L1596
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
RepeatedStructuredPropertyPredicate._apply
def _apply(self, key_value_map): """Apply the filter to values extracted from an entity. Think of self.match_keys and self.match_values as representing a table with one row. For example: match_keys = ('name', 'age', 'rank') match_values = ('Joe', 24, 5) (Except that in reality, the values are represented by tuples produced by datastore_types.PropertyValueToKeyValue().) represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | Think of key_value_map as a table with the same structure but (potentially) many rows. This represents a repeated structured property of a single entity. For example: {'name': ['Joe', 'Jane', 'Dick'], 'age': [24, 21, 23], 'rank': [5, 1, 2]} represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | | 'Jane' | 21 | 1 | | 'Dick' | 23 | 2 | We must determine wheter at least one row of the second table exactly matches the first table. We need this class because the datastore, when asked to find an entity with name 'Joe', age 24 and rank 5, will include entities that have 'Joe' somewhere in the name column, 24 somewhere in the age column, and 5 somewhere in the rank column, but not all aligned on a single row. Such an entity should not be considered a match. """ columns = [] for key in self.match_keys: column = key_value_map.get(key) if not column: # None, or an empty list. return False # If any column is empty there can be no match. columns.append(column) # Use izip to transpose the columns into rows. return self.match_values in itertools.izip(*columns)
python
def _apply(self, key_value_map): """Apply the filter to values extracted from an entity. Think of self.match_keys and self.match_values as representing a table with one row. For example: match_keys = ('name', 'age', 'rank') match_values = ('Joe', 24, 5) (Except that in reality, the values are represented by tuples produced by datastore_types.PropertyValueToKeyValue().) represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | Think of key_value_map as a table with the same structure but (potentially) many rows. This represents a repeated structured property of a single entity. For example: {'name': ['Joe', 'Jane', 'Dick'], 'age': [24, 21, 23], 'rank': [5, 1, 2]} represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | | 'Jane' | 21 | 1 | | 'Dick' | 23 | 2 | We must determine wheter at least one row of the second table exactly matches the first table. We need this class because the datastore, when asked to find an entity with name 'Joe', age 24 and rank 5, will include entities that have 'Joe' somewhere in the name column, 24 somewhere in the age column, and 5 somewhere in the rank column, but not all aligned on a single row. Such an entity should not be considered a match. """ columns = [] for key in self.match_keys: column = key_value_map.get(key) if not column: # None, or an empty list. return False # If any column is empty there can be no match. columns.append(column) # Use izip to transpose the columns into rows. return self.match_values in itertools.izip(*columns)
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Apply the filter to values extracted from an entity. Think of self.match_keys and self.match_values as representing a table with one row. For example: match_keys = ('name', 'age', 'rank') match_values = ('Joe', 24, 5) (Except that in reality, the values are represented by tuples produced by datastore_types.PropertyValueToKeyValue().) represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | Think of key_value_map as a table with the same structure but (potentially) many rows. This represents a repeated structured property of a single entity. For example: {'name': ['Joe', 'Jane', 'Dick'], 'age': [24, 21, 23], 'rank': [5, 1, 2]} represents this table: | name | age | rank | +---------+-------+--------+ | 'Joe' | 24 | 5 | | 'Jane' | 21 | 1 | | 'Dick' | 23 | 2 | We must determine wheter at least one row of the second table exactly matches the first table. We need this class because the datastore, when asked to find an entity with name 'Joe', age 24 and rank 5, will include entities that have 'Joe' somewhere in the name column, 24 somewhere in the age column, and 5 somewhere in the rank column, but not all aligned on a single row. Such an entity should not be considered a match.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L208-L257
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._fix_namespace
def _fix_namespace(self): """Internal helper to fix the namespace. This is called to ensure that for queries without an explicit namespace, the namespace used by async calls is the one in effect at the time the async call is made, not the one in effect when the the request is actually generated. """ if self.namespace is not None: return self namespace = namespace_manager.get_namespace() return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=self.filters, orders=self.orders, app=self.app, namespace=namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
python
def _fix_namespace(self): """Internal helper to fix the namespace. This is called to ensure that for queries without an explicit namespace, the namespace used by async calls is the one in effect at the time the async call is made, not the one in effect when the the request is actually generated. """ if self.namespace is not None: return self namespace = namespace_manager.get_namespace() return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=self.filters, orders=self.orders, app=self.app, namespace=namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
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Internal helper to fix the namespace. This is called to ensure that for queries without an explicit namespace, the namespace used by async calls is the one in effect at the time the async call is made, not the one in effect when the the request is actually generated.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L912-L927
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.run_to_queue
def run_to_queue(self, queue, conn, options=None, dsquery=None): """Run this query, putting entities into the given queue.""" try: multiquery = self._maybe_multi_query() if multiquery is not None: yield multiquery.run_to_queue(queue, conn, options=options) return if dsquery is None: dsquery = self._get_query(conn) rpc = dsquery.run_async(conn, options) while rpc is not None: batch = yield rpc if (batch.skipped_results and datastore_query.FetchOptions.offset(options)): offset = options.offset - batch.skipped_results options = datastore_query.FetchOptions(offset=offset, config=options) rpc = batch.next_batch_async(options) for i, result in enumerate(batch.results): queue.putq((batch, i, result)) queue.complete() except GeneratorExit: raise except Exception: if not queue.done(): _, e, tb = sys.exc_info() queue.set_exception(e, tb) raise
python
def run_to_queue(self, queue, conn, options=None, dsquery=None): """Run this query, putting entities into the given queue.""" try: multiquery = self._maybe_multi_query() if multiquery is not None: yield multiquery.run_to_queue(queue, conn, options=options) return if dsquery is None: dsquery = self._get_query(conn) rpc = dsquery.run_async(conn, options) while rpc is not None: batch = yield rpc if (batch.skipped_results and datastore_query.FetchOptions.offset(options)): offset = options.offset - batch.skipped_results options = datastore_query.FetchOptions(offset=offset, config=options) rpc = batch.next_batch_async(options) for i, result in enumerate(batch.results): queue.putq((batch, i, result)) queue.complete() except GeneratorExit: raise except Exception: if not queue.done(): _, e, tb = sys.exc_info() queue.set_exception(e, tb) raise
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Run this query, putting entities into the given queue.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L957-L985
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.is_distinct
def is_distinct(self): """True if results are guaranteed to contain a unique set of property values. This happens when every property in the group_by is also in the projection. """ return bool(self.__group_by and set(self._to_property_names(self.__group_by)) <= set(self._to_property_names(self.__projection)))
python
def is_distinct(self): """True if results are guaranteed to contain a unique set of property values. This happens when every property in the group_by is also in the projection. """ return bool(self.__group_by and set(self._to_property_names(self.__group_by)) <= set(self._to_property_names(self.__projection)))
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True if results are guaranteed to contain a unique set of property values. This happens when every property in the group_by is also in the projection.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1074-L1082
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.filter
def filter(self, *args): """Return a new Query with additional filter(s) applied.""" if not args: return self preds = [] f = self.filters if f: preds.append(f) for arg in args: if not isinstance(arg, Node): raise TypeError('Cannot filter a non-Node argument; received %r' % arg) preds.append(arg) if not preds: pred = None elif len(preds) == 1: pred = preds[0] else: pred = ConjunctionNode(*preds) return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=pred, orders=self.orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
python
def filter(self, *args): """Return a new Query with additional filter(s) applied.""" if not args: return self preds = [] f = self.filters if f: preds.append(f) for arg in args: if not isinstance(arg, Node): raise TypeError('Cannot filter a non-Node argument; received %r' % arg) preds.append(arg) if not preds: pred = None elif len(preds) == 1: pred = preds[0] else: pred = ConjunctionNode(*preds) return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=pred, orders=self.orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
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Return a new Query with additional filter(s) applied.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1084-L1106
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.order
def order(self, *args): """Return a new Query with additional sort order(s) applied.""" # q.order(Employee.name, -Employee.age) if not args: return self orders = [] o = self.orders if o: orders.append(o) for arg in args: if isinstance(arg, model.Property): orders.append(datastore_query.PropertyOrder(arg._name, _ASC)) elif isinstance(arg, datastore_query.Order): orders.append(arg) else: raise TypeError('order() expects a Property or query Order; ' 'received %r' % arg) if not orders: orders = None elif len(orders) == 1: orders = orders[0] else: orders = datastore_query.CompositeOrder(orders) return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=self.filters, orders=orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
python
def order(self, *args): """Return a new Query with additional sort order(s) applied.""" # q.order(Employee.name, -Employee.age) if not args: return self orders = [] o = self.orders if o: orders.append(o) for arg in args: if isinstance(arg, model.Property): orders.append(datastore_query.PropertyOrder(arg._name, _ASC)) elif isinstance(arg, datastore_query.Order): orders.append(arg) else: raise TypeError('order() expects a Property or query Order; ' 'received %r' % arg) if not orders: orders = None elif len(orders) == 1: orders = orders[0] else: orders = datastore_query.CompositeOrder(orders) return self.__class__(kind=self.kind, ancestor=self.ancestor, filters=self.filters, orders=orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
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Return a new Query with additional sort order(s) applied.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1108-L1135
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.map
def map(self, callback, pass_batch_into_callback=None, merge_future=None, **q_options): """Map a callback function or tasklet over the query results. Args: callback: A function or tasklet to be applied to each result; see below. merge_future: Optional Future subclass; see below. **q_options: All query options keyword arguments are supported. Callback signature: The callback is normally called with an entity as argument. However if keys_only=True is given, it is called with a Key. Also, when pass_batch_into_callback is True, it is called with three arguments: the current batch, the index within the batch, and the entity or Key at that index. The callback can return whatever it wants. If the callback is None, a trivial callback is assumed that just returns the entity or key passed in (ignoring produce_cursors). Optional merge future: The merge_future is an advanced argument that can be used to override how the callback results are combined into the overall map() return value. By default a list of callback return values is produced. By substituting one of a small number of specialized alternatives you can arrange otherwise. See tasklets.MultiFuture for the default implementation and a description of the protocol the merge_future object must implement the default. Alternatives from the same module include QueueFuture, SerialQueueFuture and ReducingFuture. Returns: When the query has run to completion and all callbacks have returned, map() returns a list of the results of all callbacks. (But see 'optional merge future' above.) """ return self.map_async(callback, pass_batch_into_callback=pass_batch_into_callback, merge_future=merge_future, **q_options).get_result()
python
def map(self, callback, pass_batch_into_callback=None, merge_future=None, **q_options): """Map a callback function or tasklet over the query results. Args: callback: A function or tasklet to be applied to each result; see below. merge_future: Optional Future subclass; see below. **q_options: All query options keyword arguments are supported. Callback signature: The callback is normally called with an entity as argument. However if keys_only=True is given, it is called with a Key. Also, when pass_batch_into_callback is True, it is called with three arguments: the current batch, the index within the batch, and the entity or Key at that index. The callback can return whatever it wants. If the callback is None, a trivial callback is assumed that just returns the entity or key passed in (ignoring produce_cursors). Optional merge future: The merge_future is an advanced argument that can be used to override how the callback results are combined into the overall map() return value. By default a list of callback return values is produced. By substituting one of a small number of specialized alternatives you can arrange otherwise. See tasklets.MultiFuture for the default implementation and a description of the protocol the merge_future object must implement the default. Alternatives from the same module include QueueFuture, SerialQueueFuture and ReducingFuture. Returns: When the query has run to completion and all callbacks have returned, map() returns a list of the results of all callbacks. (But see 'optional merge future' above.) """ return self.map_async(callback, pass_batch_into_callback=pass_batch_into_callback, merge_future=merge_future, **q_options).get_result()
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Map a callback function or tasklet over the query results. Args: callback: A function or tasklet to be applied to each result; see below. merge_future: Optional Future subclass; see below. **q_options: All query options keyword arguments are supported. Callback signature: The callback is normally called with an entity as argument. However if keys_only=True is given, it is called with a Key. Also, when pass_batch_into_callback is True, it is called with three arguments: the current batch, the index within the batch, and the entity or Key at that index. The callback can return whatever it wants. If the callback is None, a trivial callback is assumed that just returns the entity or key passed in (ignoring produce_cursors). Optional merge future: The merge_future is an advanced argument that can be used to override how the callback results are combined into the overall map() return value. By default a list of callback return values is produced. By substituting one of a small number of specialized alternatives you can arrange otherwise. See tasklets.MultiFuture for the default implementation and a description of the protocol the merge_future object must implement the default. Alternatives from the same module include QueueFuture, SerialQueueFuture and ReducingFuture. Returns: When the query has run to completion and all callbacks have returned, map() returns a list of the results of all callbacks. (But see 'optional merge future' above.)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1154-L1190
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.map_async
def map_async(self, callback, pass_batch_into_callback=None, merge_future=None, **q_options): """Map a callback function or tasklet over the query results. This is the asynchronous version of Query.map(). """ qry = self._fix_namespace() return tasklets.get_context().map_query( qry, callback, pass_batch_into_callback=pass_batch_into_callback, options=self._make_options(q_options), merge_future=merge_future)
python
def map_async(self, callback, pass_batch_into_callback=None, merge_future=None, **q_options): """Map a callback function or tasklet over the query results. This is the asynchronous version of Query.map(). """ qry = self._fix_namespace() return tasklets.get_context().map_query( qry, callback, pass_batch_into_callback=pass_batch_into_callback, options=self._make_options(q_options), merge_future=merge_future)
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Map a callback function or tasklet over the query results. This is the asynchronous version of Query.map().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1193-L1205
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.fetch_async
def fetch_async(self, limit=None, **q_options): """Fetch a list of query results, up to a limit. This is the asynchronous version of Query.fetch(). """ if limit is None: default_options = self._make_options(q_options) if default_options is not None and default_options.limit is not None: limit = default_options.limit else: limit = _MAX_LIMIT q_options['limit'] = limit q_options.setdefault('batch_size', limit) if self._needs_multi_query(): return self.map_async(None, **q_options) # Optimization using direct batches. options = self._make_options(q_options) qry = self._fix_namespace() return qry._run_to_list([], options=options)
python
def fetch_async(self, limit=None, **q_options): """Fetch a list of query results, up to a limit. This is the asynchronous version of Query.fetch(). """ if limit is None: default_options = self._make_options(q_options) if default_options is not None and default_options.limit is not None: limit = default_options.limit else: limit = _MAX_LIMIT q_options['limit'] = limit q_options.setdefault('batch_size', limit) if self._needs_multi_query(): return self.map_async(None, **q_options) # Optimization using direct batches. options = self._make_options(q_options) qry = self._fix_namespace() return qry._run_to_list([], options=options)
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Fetch a list of query results, up to a limit. This is the asynchronous version of Query.fetch().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1221-L1239
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._get_async
def _get_async(self, **q_options): """Internal version of get_async().""" res = yield self.fetch_async(1, **q_options) if not res: raise tasklets.Return(None) raise tasklets.Return(res[0])
python
def _get_async(self, **q_options): """Internal version of get_async().""" res = yield self.fetch_async(1, **q_options) if not res: raise tasklets.Return(None) raise tasklets.Return(res[0])
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Internal version of get_async().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1264-L1269
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.count_async
def count_async(self, limit=None, **q_options): """Count the number of query results, up to a limit. This is the asynchronous version of Query.count(). """ qry = self._fix_namespace() return qry._count_async(limit=limit, **q_options)
python
def count_async(self, limit=None, **q_options): """Count the number of query results, up to a limit. This is the asynchronous version of Query.count(). """ qry = self._fix_namespace() return qry._count_async(limit=limit, **q_options)
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Count the number of query results, up to a limit. This is the asynchronous version of Query.count().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1290-L1296
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._count_async
def _count_async(self, limit=None, **q_options): """Internal version of count_async().""" # TODO: Support offset by incorporating it to the limit. if 'offset' in q_options: raise NotImplementedError('.count() and .count_async() do not support ' 'offsets at present.') if 'limit' in q_options: raise TypeError('Cannot specify limit as a non-keyword argument and as a ' 'keyword argument simultaneously.') elif limit is None: limit = _MAX_LIMIT if self._needs_multi_query(): # _MultiQuery does not support iterating over result batches, # so just fetch results and count them. # TODO: Use QueryIterator to avoid materializing the results list. q_options.setdefault('batch_size', limit) q_options.setdefault('keys_only', True) results = yield self.fetch_async(limit, **q_options) raise tasklets.Return(len(results)) # Issue a special query requesting 0 results at a given offset. # The skipped_results count will tell us how many hits there were # before that offset without fetching the items. q_options['offset'] = limit q_options['limit'] = 0 options = self._make_options(q_options) conn = tasklets.get_context()._conn dsquery = self._get_query(conn) rpc = dsquery.run_async(conn, options) total = 0 while rpc is not None: batch = yield rpc options = QueryOptions(offset=options.offset - batch.skipped_results, config=options) rpc = batch.next_batch_async(options) total += batch.skipped_results raise tasklets.Return(total)
python
def _count_async(self, limit=None, **q_options): """Internal version of count_async().""" # TODO: Support offset by incorporating it to the limit. if 'offset' in q_options: raise NotImplementedError('.count() and .count_async() do not support ' 'offsets at present.') if 'limit' in q_options: raise TypeError('Cannot specify limit as a non-keyword argument and as a ' 'keyword argument simultaneously.') elif limit is None: limit = _MAX_LIMIT if self._needs_multi_query(): # _MultiQuery does not support iterating over result batches, # so just fetch results and count them. # TODO: Use QueryIterator to avoid materializing the results list. q_options.setdefault('batch_size', limit) q_options.setdefault('keys_only', True) results = yield self.fetch_async(limit, **q_options) raise tasklets.Return(len(results)) # Issue a special query requesting 0 results at a given offset. # The skipped_results count will tell us how many hits there were # before that offset without fetching the items. q_options['offset'] = limit q_options['limit'] = 0 options = self._make_options(q_options) conn = tasklets.get_context()._conn dsquery = self._get_query(conn) rpc = dsquery.run_async(conn, options) total = 0 while rpc is not None: batch = yield rpc options = QueryOptions(offset=options.offset - batch.skipped_results, config=options) rpc = batch.next_batch_async(options) total += batch.skipped_results raise tasklets.Return(total)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1299-L1335
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.fetch_page_async
def fetch_page_async(self, page_size, **q_options): """Fetch a page of results. This is the asynchronous version of Query.fetch_page(). """ qry = self._fix_namespace() return qry._fetch_page_async(page_size, **q_options)
python
def fetch_page_async(self, page_size, **q_options): """Fetch a page of results. This is the asynchronous version of Query.fetch_page(). """ qry = self._fix_namespace() return qry._fetch_page_async(page_size, **q_options)
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Fetch a page of results. This is the asynchronous version of Query.fetch_page().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1365-L1371
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._fetch_page_async
def _fetch_page_async(self, page_size, **q_options): """Internal version of fetch_page_async().""" q_options.setdefault('batch_size', page_size) q_options.setdefault('produce_cursors', True) it = self.iter(limit=page_size + 1, **q_options) results = [] while (yield it.has_next_async()): results.append(it.next()) if len(results) >= page_size: break try: cursor = it.cursor_after() except datastore_errors.BadArgumentError: cursor = None raise tasklets.Return(results, cursor, it.probably_has_next())
python
def _fetch_page_async(self, page_size, **q_options): """Internal version of fetch_page_async().""" q_options.setdefault('batch_size', page_size) q_options.setdefault('produce_cursors', True) it = self.iter(limit=page_size + 1, **q_options) results = [] while (yield it.has_next_async()): results.append(it.next()) if len(results) >= page_size: break try: cursor = it.cursor_after() except datastore_errors.BadArgumentError: cursor = None raise tasklets.Return(results, cursor, it.probably_has_next())
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Internal version of fetch_page_async().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1374-L1388
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._make_options
def _make_options(self, q_options): """Helper to construct a QueryOptions object from keyword arguments. Args: q_options: a dict of keyword arguments. Note that either 'options' or 'config' can be used to pass another QueryOptions object, but not both. If another QueryOptions object is given it provides default values. If self.default_options is set, it is used to provide defaults, which have a lower precedence than options set in q_options. Returns: A QueryOptions object, or None if q_options is empty. """ if not (q_options or self.__projection): return self.default_options if 'options' in q_options: # Move 'options' to 'config' since that is what QueryOptions() uses. if 'config' in q_options: raise TypeError('You cannot use config= and options= at the same time') q_options['config'] = q_options.pop('options') if q_options.get('projection'): try: q_options['projection'] = self._to_property_names( q_options['projection']) except TypeError, e: raise datastore_errors.BadArgumentError(e) self._check_properties(q_options['projection']) options = QueryOptions(**q_options) # Populate projection if it hasn't been overridden. if (options.keys_only is None and options.projection is None and self.__projection): options = QueryOptions( projection=self._to_property_names(self.__projection), config=options) # Populate default options if self.default_options is not None: options = self.default_options.merge(options) return options
python
def _make_options(self, q_options): """Helper to construct a QueryOptions object from keyword arguments. Args: q_options: a dict of keyword arguments. Note that either 'options' or 'config' can be used to pass another QueryOptions object, but not both. If another QueryOptions object is given it provides default values. If self.default_options is set, it is used to provide defaults, which have a lower precedence than options set in q_options. Returns: A QueryOptions object, or None if q_options is empty. """ if not (q_options or self.__projection): return self.default_options if 'options' in q_options: # Move 'options' to 'config' since that is what QueryOptions() uses. if 'config' in q_options: raise TypeError('You cannot use config= and options= at the same time') q_options['config'] = q_options.pop('options') if q_options.get('projection'): try: q_options['projection'] = self._to_property_names( q_options['projection']) except TypeError, e: raise datastore_errors.BadArgumentError(e) self._check_properties(q_options['projection']) options = QueryOptions(**q_options) # Populate projection if it hasn't been overridden. if (options.keys_only is None and options.projection is None and self.__projection): options = QueryOptions( projection=self._to_property_names(self.__projection), config=options) # Populate default options if self.default_options is not None: options = self.default_options.merge(options) return options
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Helper to construct a QueryOptions object from keyword arguments. Args: q_options: a dict of keyword arguments. Note that either 'options' or 'config' can be used to pass another QueryOptions object, but not both. If another QueryOptions object is given it provides default values. If self.default_options is set, it is used to provide defaults, which have a lower precedence than options set in q_options. Returns: A QueryOptions object, or None if q_options is empty.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1390-L1432
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query.analyze
def analyze(self): """Return a list giving the parameters required by a query.""" class MockBindings(dict): def __contains__(self, key): self[key] = None return True bindings = MockBindings() used = {} ancestor = self.ancestor if isinstance(ancestor, ParameterizedThing): ancestor = ancestor.resolve(bindings, used) filters = self.filters if filters is not None: filters = filters.resolve(bindings, used) return sorted(used)
python
def analyze(self): """Return a list giving the parameters required by a query.""" class MockBindings(dict): def __contains__(self, key): self[key] = None return True bindings = MockBindings() used = {} ancestor = self.ancestor if isinstance(ancestor, ParameterizedThing): ancestor = ancestor.resolve(bindings, used) filters = self.filters if filters is not None: filters = filters.resolve(bindings, used) return sorted(used)
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Return a list giving the parameters required by a query.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1453-L1468
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
Query._bind
def _bind(self, args, kwds): """Bind parameter values. Returns a new Query object.""" bindings = dict(kwds) for i, arg in enumerate(args): bindings[i + 1] = arg used = {} ancestor = self.ancestor if isinstance(ancestor, ParameterizedThing): ancestor = ancestor.resolve(bindings, used) filters = self.filters if filters is not None: filters = filters.resolve(bindings, used) unused = [] for i in xrange(1, 1 + len(args)): if i not in used: unused.append(i) if unused: raise datastore_errors.BadArgumentError( 'Positional arguments %s were given but not used.' % ', '.join(str(i) for i in unused)) return self.__class__(kind=self.kind, ancestor=ancestor, filters=filters, orders=self.orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
python
def _bind(self, args, kwds): """Bind parameter values. Returns a new Query object.""" bindings = dict(kwds) for i, arg in enumerate(args): bindings[i + 1] = arg used = {} ancestor = self.ancestor if isinstance(ancestor, ParameterizedThing): ancestor = ancestor.resolve(bindings, used) filters = self.filters if filters is not None: filters = filters.resolve(bindings, used) unused = [] for i in xrange(1, 1 + len(args)): if i not in used: unused.append(i) if unused: raise datastore_errors.BadArgumentError( 'Positional arguments %s were given but not used.' % ', '.join(str(i) for i in unused)) return self.__class__(kind=self.kind, ancestor=ancestor, filters=filters, orders=self.orders, app=self.app, namespace=self.namespace, default_options=self.default_options, projection=self.projection, group_by=self.group_by)
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Bind parameter values. Returns a new Query object.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1474-L1498
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
QueryIterator.cursor_before
def cursor_before(self): """Return the cursor before the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item. """ if self._exhausted: return self.cursor_after() if isinstance(self._cursor_before, BaseException): raise self._cursor_before return self._cursor_before
python
def cursor_before(self): """Return the cursor before the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item. """ if self._exhausted: return self.cursor_after() if isinstance(self._cursor_before, BaseException): raise self._cursor_before return self._cursor_before
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Return the cursor before the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1693-L1707
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
QueryIterator.cursor_after
def cursor_after(self): """Return the cursor after the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item. """ if isinstance(self._cursor_after, BaseException): raise self._cursor_after return self._cursor_after
python
def cursor_after(self): """Return the cursor after the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item. """ if isinstance(self._cursor_after, BaseException): raise self._cursor_after return self._cursor_after
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Return the cursor after the current item. You must pass a QueryOptions object with produce_cursors=True for this to work. If there is no cursor or no current item, raise BadArgumentError. Before next() has returned there is no cursor. Once the loop is exhausted, this returns the cursor after the last item.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1709-L1721
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
QueryIterator.has_next_async
def has_next_async(self): """Return a Future whose result will say whether a next item is available. See the module docstring for the usage pattern. """ if self._fut is None: self._fut = self._iter.getq() flag = True try: yield self._fut except EOFError: flag = False raise tasklets.Return(flag)
python
def has_next_async(self): """Return a Future whose result will say whether a next item is available. See the module docstring for the usage pattern. """ if self._fut is None: self._fut = self._iter.getq() flag = True try: yield self._fut except EOFError: flag = False raise tasklets.Return(flag)
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Return a Future whose result will say whether a next item is available. See the module docstring for the usage pattern.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1784-L1796
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
QueryIterator.next
def next(self): """Iterator protocol: get next item or raise StopIteration.""" if self._fut is None: self._fut = self._iter.getq() try: try: # The future result is set by this class's _extended_callback # method. # pylint: disable=unpacking-non-sequence (ent, self._cursor_before, self._cursor_after, self._more_results) = self._fut.get_result() return ent except EOFError: self._exhausted = True raise StopIteration finally: self._fut = None
python
def next(self): """Iterator protocol: get next item or raise StopIteration.""" if self._fut is None: self._fut = self._iter.getq() try: try: # The future result is set by this class's _extended_callback # method. # pylint: disable=unpacking-non-sequence (ent, self._cursor_before, self._cursor_after, self._more_results) = self._fut.get_result() return ent except EOFError: self._exhausted = True raise StopIteration finally: self._fut = None
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Iterator protocol: get next item or raise StopIteration.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1798-L1816
train
GoogleCloudPlatform/datastore-ndb-python
ndb/query.py
_MultiQuery.run_to_queue
def run_to_queue(self, queue, conn, options=None): """Run this query, putting entities into the given queue.""" if options is None: # Default options. offset = None limit = None keys_only = None else: # Capture options we need to simulate. offset = options.offset limit = options.limit keys_only = options.keys_only # Cursors are supported for certain orders only. if (options.start_cursor or options.end_cursor or options.produce_cursors): names = set() if self.__orders is not None: names = self.__orders._get_prop_names() if '__key__' not in names: raise datastore_errors.BadArgumentError( '_MultiQuery with cursors requires __key__ order') # Decide if we need to modify the options passed to subqueries. # NOTE: It would seem we can sometimes let Cloud Datastore handle # the offset natively, but this would thwart the duplicate key # detection, so we always have to emulate the offset here. # We can set the limit we pass along to offset + limit though, # since that is the maximum number of results from a single # subquery we will ever have to consider. modifiers = {} if offset: modifiers['offset'] = None if limit is not None: modifiers['limit'] = min(_MAX_LIMIT, offset + limit) if keys_only and self.__orders is not None: modifiers['keys_only'] = None if modifiers: options = QueryOptions(config=options, **modifiers) if offset is None: offset = 0 if limit is None: limit = _MAX_LIMIT if self.__orders is None: # Run the subqueries sequentially; there is no order to keep. keys_seen = set() for subq in self.__subqueries: if limit <= 0: break subit = tasklets.SerialQueueFuture('_MultiQuery.run_to_queue[ser]') subq.run_to_queue(subit, conn, options=options) while limit > 0: try: batch, index, result = yield subit.getq() except EOFError: break if keys_only: key = result else: key = result._key if key not in keys_seen: keys_seen.add(key) if offset > 0: offset -= 1 else: limit -= 1 queue.putq((None, None, result)) queue.complete() return # This with-statement causes the adapter to set _orig_pb on all # entities it converts from protobuf. # TODO: Does this interact properly with the cache? with conn.adapter: # Start running all the sub-queries. todo = [] # List of (subit, dsquery) tuples. for subq in self.__subqueries: dsquery = subq._get_query(conn) subit = tasklets.SerialQueueFuture('_MultiQuery.run_to_queue[par]') subq.run_to_queue(subit, conn, options=options, dsquery=dsquery) todo.append((subit, dsquery)) # Create a list of (first-entity, subquery-iterator) tuples. state = [] # List of _SubQueryIteratorState instances. for subit, dsquery in todo: try: thing = yield subit.getq() except EOFError: continue else: state.append(_SubQueryIteratorState(thing, subit, dsquery, self.__orders)) # Now turn it into a sorted heap. The heapq module claims that # calling heapify() is more efficient than calling heappush() for # each item. heapq.heapify(state) # Repeatedly yield the lowest entity from the state vector, # filtering duplicates. This is essentially a multi-way merge # sort. One would think it should be possible to filter # duplicates simply by dropping other entities already in the # state vector that are equal to the lowest entity, but because of # the weird sorting of repeated properties, we have to explicitly # keep a set of all keys, so we can remove later occurrences. # Note that entities will still be sorted correctly, within the # constraints given by the sort order. keys_seen = set() while state and limit > 0: item = heapq.heappop(state) batch = item.batch index = item.index entity = item.entity key = entity._key if key not in keys_seen: keys_seen.add(key) if offset > 0: offset -= 1 else: limit -= 1 if keys_only: queue.putq((batch, index, key)) else: queue.putq((batch, index, entity)) subit = item.iterator try: batch, index, entity = yield subit.getq() except EOFError: pass else: item.batch = batch item.index = index item.entity = entity heapq.heappush(state, item) queue.complete()
python
def run_to_queue(self, queue, conn, options=None): """Run this query, putting entities into the given queue.""" if options is None: # Default options. offset = None limit = None keys_only = None else: # Capture options we need to simulate. offset = options.offset limit = options.limit keys_only = options.keys_only # Cursors are supported for certain orders only. if (options.start_cursor or options.end_cursor or options.produce_cursors): names = set() if self.__orders is not None: names = self.__orders._get_prop_names() if '__key__' not in names: raise datastore_errors.BadArgumentError( '_MultiQuery with cursors requires __key__ order') # Decide if we need to modify the options passed to subqueries. # NOTE: It would seem we can sometimes let Cloud Datastore handle # the offset natively, but this would thwart the duplicate key # detection, so we always have to emulate the offset here. # We can set the limit we pass along to offset + limit though, # since that is the maximum number of results from a single # subquery we will ever have to consider. modifiers = {} if offset: modifiers['offset'] = None if limit is not None: modifiers['limit'] = min(_MAX_LIMIT, offset + limit) if keys_only and self.__orders is not None: modifiers['keys_only'] = None if modifiers: options = QueryOptions(config=options, **modifiers) if offset is None: offset = 0 if limit is None: limit = _MAX_LIMIT if self.__orders is None: # Run the subqueries sequentially; there is no order to keep. keys_seen = set() for subq in self.__subqueries: if limit <= 0: break subit = tasklets.SerialQueueFuture('_MultiQuery.run_to_queue[ser]') subq.run_to_queue(subit, conn, options=options) while limit > 0: try: batch, index, result = yield subit.getq() except EOFError: break if keys_only: key = result else: key = result._key if key not in keys_seen: keys_seen.add(key) if offset > 0: offset -= 1 else: limit -= 1 queue.putq((None, None, result)) queue.complete() return # This with-statement causes the adapter to set _orig_pb on all # entities it converts from protobuf. # TODO: Does this interact properly with the cache? with conn.adapter: # Start running all the sub-queries. todo = [] # List of (subit, dsquery) tuples. for subq in self.__subqueries: dsquery = subq._get_query(conn) subit = tasklets.SerialQueueFuture('_MultiQuery.run_to_queue[par]') subq.run_to_queue(subit, conn, options=options, dsquery=dsquery) todo.append((subit, dsquery)) # Create a list of (first-entity, subquery-iterator) tuples. state = [] # List of _SubQueryIteratorState instances. for subit, dsquery in todo: try: thing = yield subit.getq() except EOFError: continue else: state.append(_SubQueryIteratorState(thing, subit, dsquery, self.__orders)) # Now turn it into a sorted heap. The heapq module claims that # calling heapify() is more efficient than calling heappush() for # each item. heapq.heapify(state) # Repeatedly yield the lowest entity from the state vector, # filtering duplicates. This is essentially a multi-way merge # sort. One would think it should be possible to filter # duplicates simply by dropping other entities already in the # state vector that are equal to the lowest entity, but because of # the weird sorting of repeated properties, we have to explicitly # keep a set of all keys, so we can remove later occurrences. # Note that entities will still be sorted correctly, within the # constraints given by the sort order. keys_seen = set() while state and limit > 0: item = heapq.heappop(state) batch = item.batch index = item.index entity = item.entity key = entity._key if key not in keys_seen: keys_seen.add(key) if offset > 0: offset -= 1 else: limit -= 1 if keys_only: queue.putq((batch, index, key)) else: queue.putq((batch, index, entity)) subit = item.iterator try: batch, index, entity = yield subit.getq() except EOFError: pass else: item.batch = batch item.index = index item.entity = entity heapq.heappush(state, item) queue.complete()
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Run this query, putting entities into the given queue.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/query.py#L1910-L2047
train
GoogleCloudPlatform/datastore-ndb-python
ndb/memcache_client.py
MemcacheClient.memcache_get
def memcache_get(self, key, for_cas=False, namespace=None, use_cache=False, deadline=None): """An auto-batching wrapper for memcache.get() or .get_multi(). Args: key: Key to set. This must be a string; no prefix is applied. for_cas: If True, request and store CAS ids on the Context. namespace: Optional namespace. deadline: Optional deadline for the RPC. Returns: A Future (!) whose return value is the value retrieved from memcache, or None. """ if not isinstance(key, basestring): raise TypeError('key must be a string; received %r' % key) if not isinstance(for_cas, bool): raise TypeError('for_cas must be a bool; received %r' % for_cas) if namespace is None: namespace = namespace_manager.get_namespace() options = (for_cas, namespace, deadline) batcher = self.memcache_get_batcher if use_cache: return batcher.add_once(key, options) else: return batcher.add(key, options)
python
def memcache_get(self, key, for_cas=False, namespace=None, use_cache=False, deadline=None): """An auto-batching wrapper for memcache.get() or .get_multi(). Args: key: Key to set. This must be a string; no prefix is applied. for_cas: If True, request and store CAS ids on the Context. namespace: Optional namespace. deadline: Optional deadline for the RPC. Returns: A Future (!) whose return value is the value retrieved from memcache, or None. """ if not isinstance(key, basestring): raise TypeError('key must be a string; received %r' % key) if not isinstance(for_cas, bool): raise TypeError('for_cas must be a bool; received %r' % for_cas) if namespace is None: namespace = namespace_manager.get_namespace() options = (for_cas, namespace, deadline) batcher = self.memcache_get_batcher if use_cache: return batcher.add_once(key, options) else: return batcher.add(key, options)
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An auto-batching wrapper for memcache.get() or .get_multi(). Args: key: Key to set. This must be a string; no prefix is applied. for_cas: If True, request and store CAS ids on the Context. namespace: Optional namespace. deadline: Optional deadline for the RPC. Returns: A Future (!) whose return value is the value retrieved from memcache, or None.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/memcache_client.py#L103-L128
train
GoogleCloudPlatform/datastore-ndb-python
ndb/utils.py
positional
def positional(max_pos_args): """A decorator to declare that only the first N arguments may be positional. Note that for methods, n includes 'self'. """ __ndb_debug__ = 'SKIP' def positional_decorator(wrapped): if not DEBUG: return wrapped __ndb_debug__ = 'SKIP' @wrapping(wrapped) def positional_wrapper(*args, **kwds): __ndb_debug__ = 'SKIP' if len(args) > max_pos_args: plural_s = '' if max_pos_args != 1: plural_s = 's' raise TypeError( '%s() takes at most %d positional argument%s (%d given)' % (wrapped.__name__, max_pos_args, plural_s, len(args))) return wrapped(*args, **kwds) return positional_wrapper return positional_decorator
python
def positional(max_pos_args): """A decorator to declare that only the first N arguments may be positional. Note that for methods, n includes 'self'. """ __ndb_debug__ = 'SKIP' def positional_decorator(wrapped): if not DEBUG: return wrapped __ndb_debug__ = 'SKIP' @wrapping(wrapped) def positional_wrapper(*args, **kwds): __ndb_debug__ = 'SKIP' if len(args) > max_pos_args: plural_s = '' if max_pos_args != 1: plural_s = 's' raise TypeError( '%s() takes at most %d positional argument%s (%d given)' % (wrapped.__name__, max_pos_args, plural_s, len(args))) return wrapped(*args, **kwds) return positional_wrapper return positional_decorator
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A decorator to declare that only the first N arguments may be positional. Note that for methods, n includes 'self'.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/utils.py#L138-L162
train
GoogleCloudPlatform/datastore-ndb-python
ndb/utils.py
decorator
def decorator(wrapped_decorator): """Converts a function into a decorator that optionally accepts keyword arguments in its declaration. Example usage: @utils.decorator def decorator(func, args, kwds, op1=None): ... apply op1 ... return func(*args, **kwds) # Form (1), vanilla @decorator foo(...) ... # Form (2), with options @decorator(op1=5) foo(...) ... Args: wrapped_decorator: A function that accepts positional args (func, args, kwds) and any additional supported keyword arguments. Returns: A decorator with an additional 'wrapped_decorator' property that is set to the original function. """ def helper(_func=None, **options): def outer_wrapper(func): @wrapping(func) def inner_wrapper(*args, **kwds): return wrapped_decorator(func, args, kwds, **options) return inner_wrapper if _func is None: # Form (2), with options. return outer_wrapper # Form (1), vanilla. if options: # Don't allow @decorator(foo, op1=5). raise TypeError('positional arguments not supported') return outer_wrapper(_func) helper.wrapped_decorator = wrapped_decorator return helper
python
def decorator(wrapped_decorator): """Converts a function into a decorator that optionally accepts keyword arguments in its declaration. Example usage: @utils.decorator def decorator(func, args, kwds, op1=None): ... apply op1 ... return func(*args, **kwds) # Form (1), vanilla @decorator foo(...) ... # Form (2), with options @decorator(op1=5) foo(...) ... Args: wrapped_decorator: A function that accepts positional args (func, args, kwds) and any additional supported keyword arguments. Returns: A decorator with an additional 'wrapped_decorator' property that is set to the original function. """ def helper(_func=None, **options): def outer_wrapper(func): @wrapping(func) def inner_wrapper(*args, **kwds): return wrapped_decorator(func, args, kwds, **options) return inner_wrapper if _func is None: # Form (2), with options. return outer_wrapper # Form (1), vanilla. if options: # Don't allow @decorator(foo, op1=5). raise TypeError('positional arguments not supported') return outer_wrapper(_func) helper.wrapped_decorator = wrapped_decorator return helper
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/utils.py#L165-L210
train
GoogleCloudPlatform/datastore-ndb-python
demo/app/fibo.py
fibonacci
def fibonacci(n): """A recursive Fibonacci to exercise task switching.""" if n <= 1: raise ndb.Return(n) a, b = yield fibonacci(n - 1), fibonacci(n - 2) raise ndb.Return(a + b)
python
def fibonacci(n): """A recursive Fibonacci to exercise task switching.""" if n <= 1: raise ndb.Return(n) a, b = yield fibonacci(n - 1), fibonacci(n - 2) raise ndb.Return(a + b)
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A recursive Fibonacci to exercise task switching.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/app/fibo.py#L32-L37
train
GoogleCloudPlatform/datastore-ndb-python
demo/app/fibo.py
memoizing_fibonacci
def memoizing_fibonacci(n): """A memoizing recursive Fibonacci to exercise RPCs.""" if n <= 1: raise ndb.Return(n) key = ndb.Key(FibonacciMemo, str(n)) memo = yield key.get_async(ndb_should_cache=False) if memo is not None: assert memo.arg == n logging.info('memo hit: %d -> %d', n, memo.value) raise ndb.Return(memo.value) logging.info('memo fail: %d', n) a = yield memoizing_fibonacci(n - 1) b = yield memoizing_fibonacci(n - 2) ans = a + b memo = FibonacciMemo(key=key, arg=n, value=ans) logging.info('memo write: %d -> %d', n, memo.value) yield memo.put_async(ndb_should_cache=False) raise ndb.Return(ans)
python
def memoizing_fibonacci(n): """A memoizing recursive Fibonacci to exercise RPCs.""" if n <= 1: raise ndb.Return(n) key = ndb.Key(FibonacciMemo, str(n)) memo = yield key.get_async(ndb_should_cache=False) if memo is not None: assert memo.arg == n logging.info('memo hit: %d -> %d', n, memo.value) raise ndb.Return(memo.value) logging.info('memo fail: %d', n) a = yield memoizing_fibonacci(n - 1) b = yield memoizing_fibonacci(n - 2) ans = a + b memo = FibonacciMemo(key=key, arg=n, value=ans) logging.info('memo write: %d -> %d', n, memo.value) yield memo.put_async(ndb_should_cache=False) raise ndb.Return(ans)
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A memoizing recursive Fibonacci to exercise RPCs.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/app/fibo.py#L46-L63
train
GoogleCloudPlatform/datastore-ndb-python
ndb/autobatcher.py
AutoBatcher.run_queue
def run_queue(self, options, todo): """Actually run the _todo_tasklet.""" utils.logging_debug('AutoBatcher(%s): %d items', self._todo_tasklet.__name__, len(todo)) batch_fut = self._todo_tasklet(todo, options) self._running.append(batch_fut) # Add a callback when we're done. batch_fut.add_callback(self._finished_callback, batch_fut, todo)
python
def run_queue(self, options, todo): """Actually run the _todo_tasklet.""" utils.logging_debug('AutoBatcher(%s): %d items', self._todo_tasklet.__name__, len(todo)) batch_fut = self._todo_tasklet(todo, options) self._running.append(batch_fut) # Add a callback when we're done. batch_fut.add_callback(self._finished_callback, batch_fut, todo)
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Actually run the _todo_tasklet.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/autobatcher.py#L71-L78
train
GoogleCloudPlatform/datastore-ndb-python
ndb/autobatcher.py
AutoBatcher.add
def add(self, arg, options=None): """Adds an arg and gets back a future. Args: arg: one argument for _todo_tasklet. options: rpc options. Return: An instance of future, representing the result of running _todo_tasklet without batching. """ fut = tasklets.Future('%s.add(%s, %s)' % (self, arg, options)) todo = self._queues.get(options) if todo is None: utils.logging_debug('AutoBatcher(%s): creating new queue for %r', self._todo_tasklet.__name__, options) if not self._queues: eventloop.add_idle(self._on_idle) todo = self._queues[options] = [] todo.append((fut, arg)) if len(todo) >= self._limit: del self._queues[options] self.run_queue(options, todo) return fut
python
def add(self, arg, options=None): """Adds an arg and gets back a future. Args: arg: one argument for _todo_tasklet. options: rpc options. Return: An instance of future, representing the result of running _todo_tasklet without batching. """ fut = tasklets.Future('%s.add(%s, %s)' % (self, arg, options)) todo = self._queues.get(options) if todo is None: utils.logging_debug('AutoBatcher(%s): creating new queue for %r', self._todo_tasklet.__name__, options) if not self._queues: eventloop.add_idle(self._on_idle) todo = self._queues[options] = [] todo.append((fut, arg)) if len(todo) >= self._limit: del self._queues[options] self.run_queue(options, todo) return fut
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Adds an arg and gets back a future. Args: arg: one argument for _todo_tasklet. options: rpc options. Return: An instance of future, representing the result of running _todo_tasklet without batching.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/autobatcher.py#L90-L113
train
GoogleCloudPlatform/datastore-ndb-python
ndb/autobatcher.py
AutoBatcher._finished_callback
def _finished_callback(self, batch_fut, todo): """Passes exception along. Args: batch_fut: the batch future returned by running todo_tasklet. todo: (fut, option) pair. fut is the future return by each add() call. If the batch fut was successful, it has already called fut.set_result() on other individual futs. This method only handles when the batch fut encountered an exception. """ self._running.remove(batch_fut) err = batch_fut.get_exception() if err is not None: tb = batch_fut.get_traceback() for (fut, _) in todo: if not fut.done(): fut.set_exception(err, tb)
python
def _finished_callback(self, batch_fut, todo): """Passes exception along. Args: batch_fut: the batch future returned by running todo_tasklet. todo: (fut, option) pair. fut is the future return by each add() call. If the batch fut was successful, it has already called fut.set_result() on other individual futs. This method only handles when the batch fut encountered an exception. """ self._running.remove(batch_fut) err = batch_fut.get_exception() if err is not None: tb = batch_fut.get_traceback() for (fut, _) in todo: if not fut.done(): fut.set_exception(err, tb)
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Passes exception along. Args: batch_fut: the batch future returned by running todo_tasklet. todo: (fut, option) pair. fut is the future return by each add() call. If the batch fut was successful, it has already called fut.set_result() on other individual futs. This method only handles when the batch fut encountered an exception.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/autobatcher.py#L132-L149
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
get_namespaces
def get_namespaces(start=None, end=None): """Return all namespaces in the specified range. Args: start: only return namespaces >= start if start is not None. end: only return namespaces < end if end is not None. Returns: A list of namespace names between the (optional) start and end values. """ q = Namespace.query() if start is not None: q = q.filter(Namespace.key >= Namespace.key_for_namespace(start)) if end is not None: q = q.filter(Namespace.key < Namespace.key_for_namespace(end)) return [x.namespace_name for x in q]
python
def get_namespaces(start=None, end=None): """Return all namespaces in the specified range. Args: start: only return namespaces >= start if start is not None. end: only return namespaces < end if end is not None. Returns: A list of namespace names between the (optional) start and end values. """ q = Namespace.query() if start is not None: q = q.filter(Namespace.key >= Namespace.key_for_namespace(start)) if end is not None: q = q.filter(Namespace.key < Namespace.key_for_namespace(end)) return [x.namespace_name for x in q]
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Return all namespaces in the specified range. Args: start: only return namespaces >= start if start is not None. end: only return namespaces < end if end is not None. Returns: A list of namespace names between the (optional) start and end values.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L239-L254
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
get_kinds
def get_kinds(start=None, end=None): """Return all kinds in the specified range, for the current namespace. Args: start: only return kinds >= start if start is not None. end: only return kinds < end if end is not None. Returns: A list of kind names between the (optional) start and end values. """ q = Kind.query() if start is not None and start != '': q = q.filter(Kind.key >= Kind.key_for_kind(start)) if end is not None: if end == '': return [] q = q.filter(Kind.key < Kind.key_for_kind(end)) return [x.kind_name for x in q]
python
def get_kinds(start=None, end=None): """Return all kinds in the specified range, for the current namespace. Args: start: only return kinds >= start if start is not None. end: only return kinds < end if end is not None. Returns: A list of kind names between the (optional) start and end values. """ q = Kind.query() if start is not None and start != '': q = q.filter(Kind.key >= Kind.key_for_kind(start)) if end is not None: if end == '': return [] q = q.filter(Kind.key < Kind.key_for_kind(end)) return [x.kind_name for x in q]
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Return all kinds in the specified range, for the current namespace. Args: start: only return kinds >= start if start is not None. end: only return kinds < end if end is not None. Returns: A list of kind names between the (optional) start and end values.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L257-L275
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
get_properties_of_kind
def get_properties_of_kind(kind, start=None, end=None): """Return all properties of kind in the specified range. NOTE: This function does not return unindexed properties. Args: kind: name of kind whose properties you want. start: only return properties >= start if start is not None. end: only return properties < end if end is not None. Returns: A list of property names of kind between the (optional) start and end values. """ q = Property.query(ancestor=Property.key_for_kind(kind)) if start is not None and start != '': q = q.filter(Property.key >= Property.key_for_property(kind, start)) if end is not None: if end == '': return [] q = q.filter(Property.key < Property.key_for_property(kind, end)) return [Property.key_to_property(k) for k in q.iter(keys_only=True)]
python
def get_properties_of_kind(kind, start=None, end=None): """Return all properties of kind in the specified range. NOTE: This function does not return unindexed properties. Args: kind: name of kind whose properties you want. start: only return properties >= start if start is not None. end: only return properties < end if end is not None. Returns: A list of property names of kind between the (optional) start and end values. """ q = Property.query(ancestor=Property.key_for_kind(kind)) if start is not None and start != '': q = q.filter(Property.key >= Property.key_for_property(kind, start)) if end is not None: if end == '': return [] q = q.filter(Property.key < Property.key_for_property(kind, end)) return [Property.key_to_property(k) for k in q.iter(keys_only=True)]
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L278-L300
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
get_representations_of_kind
def get_representations_of_kind(kind, start=None, end=None): """Return all representations of properties of kind in the specified range. NOTE: This function does not return unindexed properties. Args: kind: name of kind whose properties you want. start: only return properties >= start if start is not None. end: only return properties < end if end is not None. Returns: A dictionary mapping property names to its list of representations. """ q = Property.query(ancestor=Property.key_for_kind(kind)) if start is not None and start != '': q = q.filter(Property.key >= Property.key_for_property(kind, start)) if end is not None: if end == '': return {} q = q.filter(Property.key < Property.key_for_property(kind, end)) result = {} for property in q: result[property.property_name] = property.property_representation return result
python
def get_representations_of_kind(kind, start=None, end=None): """Return all representations of properties of kind in the specified range. NOTE: This function does not return unindexed properties. Args: kind: name of kind whose properties you want. start: only return properties >= start if start is not None. end: only return properties < end if end is not None. Returns: A dictionary mapping property names to its list of representations. """ q = Property.query(ancestor=Property.key_for_kind(kind)) if start is not None and start != '': q = q.filter(Property.key >= Property.key_for_property(kind, start)) if end is not None: if end == '': return {} q = q.filter(Property.key < Property.key_for_property(kind, end)) result = {} for property in q: result[property.property_name] = property.property_representation return result
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L303-L328
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
get_entity_group_version
def get_entity_group_version(key): """Return the version of the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The version of the entity group containing key. This version is guaranteed to increase on every change to the entity group. The version may increase even in the absence of user-visible changes to the entity group. May return None if the entity group was never written to. On non-HR datatores, this function returns None. """ eg = EntityGroup.key_for_entity_group(key).get() if eg: return eg.version else: return None
python
def get_entity_group_version(key): """Return the version of the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The version of the entity group containing key. This version is guaranteed to increase on every change to the entity group. The version may increase even in the absence of user-visible changes to the entity group. May return None if the entity group was never written to. On non-HR datatores, this function returns None. """ eg = EntityGroup.key_for_entity_group(key).get() if eg: return eg.version else: return None
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Return the version of the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The version of the entity group containing key. This version is guaranteed to increase on every change to the entity group. The version may increase even in the absence of user-visible changes to the entity group. May return None if the entity group was never written to. On non-HR datatores, this function returns None.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L331-L350
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
Namespace.key_for_namespace
def key_for_namespace(cls, namespace): """Return the Key for a namespace. Args: namespace: A string giving the namespace whose key is requested. Returns: The Key for the namespace. """ if namespace: return model.Key(cls.KIND_NAME, namespace) else: return model.Key(cls.KIND_NAME, cls.EMPTY_NAMESPACE_ID)
python
def key_for_namespace(cls, namespace): """Return the Key for a namespace. Args: namespace: A string giving the namespace whose key is requested. Returns: The Key for the namespace. """ if namespace: return model.Key(cls.KIND_NAME, namespace) else: return model.Key(cls.KIND_NAME, cls.EMPTY_NAMESPACE_ID)
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Return the Key for a namespace. Args: namespace: A string giving the namespace whose key is requested. Returns: The Key for the namespace.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L76-L88
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
Property.key_for_property
def key_for_property(cls, kind, property): """Return the __property__ key for property of kind. Args: kind: kind whose key is requested. property: property whose key is requested. Returns: The key for property of kind. """ return model.Key(Kind.KIND_NAME, kind, Property.KIND_NAME, property)
python
def key_for_property(cls, kind, property): """Return the __property__ key for property of kind. Args: kind: kind whose key is requested. property: property whose key is requested. Returns: The key for property of kind. """ return model.Key(Kind.KIND_NAME, kind, Property.KIND_NAME, property)
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Return the __property__ key for property of kind. Args: kind: kind whose key is requested. property: property whose key is requested. Returns: The key for property of kind.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L168-L178
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
Property.key_to_kind
def key_to_kind(cls, key): """Return the kind specified by a given __property__ key. Args: key: key whose kind name is requested. Returns: The kind specified by key. """ if key.kind() == Kind.KIND_NAME: return key.id() else: return key.parent().id()
python
def key_to_kind(cls, key): """Return the kind specified by a given __property__ key. Args: key: key whose kind name is requested. Returns: The kind specified by key. """ if key.kind() == Kind.KIND_NAME: return key.id() else: return key.parent().id()
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L181-L193
train
GoogleCloudPlatform/datastore-ndb-python
ndb/metadata.py
EntityGroup.key_for_entity_group
def key_for_entity_group(cls, key): """Return the key for the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The __entity_group__ key for the entity group containing key. """ return model.Key(cls.KIND_NAME, cls.ID, parent=key.root())
python
def key_for_entity_group(cls, key): """Return the key for the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The __entity_group__ key for the entity group containing key. """ return model.Key(cls.KIND_NAME, cls.ID, parent=key.root())
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Return the key for the entity group containing key. Args: key: a key for an entity group whose __entity_group__ key you want. Returns: The __entity_group__ key for the entity group containing key.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/metadata.py#L227-L236
train
GoogleCloudPlatform/datastore-ndb-python
ndb/django_middleware.py
NdbDjangoMiddleware.process_request
def process_request(self, unused_request): """Called by Django before deciding which view to execute.""" # Compare to the first half of toplevel() in context.py. tasklets._state.clear_all_pending() # Create and install a new context. ctx = tasklets.make_default_context() tasklets.set_context(ctx)
python
def process_request(self, unused_request): """Called by Django before deciding which view to execute.""" # Compare to the first half of toplevel() in context.py. tasklets._state.clear_all_pending() # Create and install a new context. ctx = tasklets.make_default_context() tasklets.set_context(ctx)
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Called by Django before deciding which view to execute.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/django_middleware.py#L42-L48
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
_make_ctx_options
def _make_ctx_options(ctx_options, config_cls=ContextOptions): """Helper to construct a ContextOptions object from keyword arguments. Args: ctx_options: A dict of keyword arguments. config_cls: Optional Configuration class to use, default ContextOptions. Note that either 'options' or 'config' can be used to pass another Configuration object, but not both. If another Configuration object is given it provides default values. Returns: A Configuration object, or None if ctx_options is empty. """ if not ctx_options: return None for key in list(ctx_options): translation = _OPTION_TRANSLATIONS.get(key) if translation: if translation in ctx_options: raise ValueError('Cannot specify %s and %s at the same time' % (key, translation)) ctx_options[translation] = ctx_options.pop(key) return config_cls(**ctx_options)
python
def _make_ctx_options(ctx_options, config_cls=ContextOptions): """Helper to construct a ContextOptions object from keyword arguments. Args: ctx_options: A dict of keyword arguments. config_cls: Optional Configuration class to use, default ContextOptions. Note that either 'options' or 'config' can be used to pass another Configuration object, but not both. If another Configuration object is given it provides default values. Returns: A Configuration object, or None if ctx_options is empty. """ if not ctx_options: return None for key in list(ctx_options): translation = _OPTION_TRANSLATIONS.get(key) if translation: if translation in ctx_options: raise ValueError('Cannot specify %s and %s at the same time' % (key, translation)) ctx_options[translation] = ctx_options.pop(key) return config_cls(**ctx_options)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L103-L126
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.set_cache_policy
def set_cache_policy(self, func): """Set the context cache policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should be cached. May be None. """ if func is None: func = self.default_cache_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._cache_policy = func
python
def set_cache_policy(self, func): """Set the context cache policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should be cached. May be None. """ if func is None: func = self.default_cache_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._cache_policy = func
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L281-L292
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context._use_cache
def _use_cache(self, key, options=None): """Return whether to use the context cache for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the key should be cached, False otherwise. """ flag = ContextOptions.use_cache(options) if flag is None: flag = self._cache_policy(key) if flag is None: flag = ContextOptions.use_cache(self._conn.config) if flag is None: flag = True return flag
python
def _use_cache(self, key, options=None): """Return whether to use the context cache for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the key should be cached, False otherwise. """ flag = ContextOptions.use_cache(options) if flag is None: flag = self._cache_policy(key) if flag is None: flag = ContextOptions.use_cache(self._conn.config) if flag is None: flag = True return flag
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L294-L311
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.set_memcache_policy
def set_memcache_policy(self, func): """Set the memcache policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should be cached. May be None. """ if func is None: func = self.default_memcache_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._memcache_policy = func
python
def set_memcache_policy(self, func): """Set the memcache policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should be cached. May be None. """ if func is None: func = self.default_memcache_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._memcache_policy = func
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L348-L359
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context._use_memcache
def _use_memcache(self, key, options=None): """Return whether to use memcache for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the key should be cached in memcache, False otherwise. """ flag = ContextOptions.use_memcache(options) if flag is None: flag = self._memcache_policy(key) if flag is None: flag = ContextOptions.use_memcache(self._conn.config) if flag is None: flag = True return flag
python
def _use_memcache(self, key, options=None): """Return whether to use memcache for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the key should be cached in memcache, False otherwise. """ flag = ContextOptions.use_memcache(options) if flag is None: flag = self._memcache_policy(key) if flag is None: flag = ContextOptions.use_memcache(self._conn.config) if flag is None: flag = True return flag
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L361-L378
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.default_datastore_policy
def default_datastore_policy(key): """Default datastore policy. This defers to _use_datastore on the Model class. Args: key: Key instance. Returns: A bool or None. """ flag = None if key is not None: modelclass = model.Model._kind_map.get(key.kind()) if modelclass is not None: policy = getattr(modelclass, '_use_datastore', None) if policy is not None: if isinstance(policy, bool): flag = policy else: flag = policy(key) return flag
python
def default_datastore_policy(key): """Default datastore policy. This defers to _use_datastore on the Model class. Args: key: Key instance. Returns: A bool or None. """ flag = None if key is not None: modelclass = model.Model._kind_map.get(key.kind()) if modelclass is not None: policy = getattr(modelclass, '_use_datastore', None) if policy is not None: if isinstance(policy, bool): flag = policy else: flag = policy(key) return flag
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Default datastore policy. This defers to _use_datastore on the Model class. Args: key: Key instance. Returns: A bool or None.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L381-L402
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.set_datastore_policy
def set_datastore_policy(self, func): """Set the context datastore policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should use the datastore. May be None. """ if func is None: func = self.default_datastore_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._datastore_policy = func
python
def set_datastore_policy(self, func): """Set the context datastore policy function. Args: func: A function that accepts a Key instance as argument and returns a bool indicating if it should use the datastore. May be None. """ if func is None: func = self.default_datastore_policy elif isinstance(func, bool): func = lambda unused_key, flag=func: flag self._datastore_policy = func
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L415-L426
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context._use_datastore
def _use_datastore(self, key, options=None): """Return whether to use the datastore for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the datastore should be used, False otherwise. """ flag = ContextOptions.use_datastore(options) if flag is None: flag = self._datastore_policy(key) if flag is None: flag = ContextOptions.use_datastore(self._conn.config) if flag is None: flag = True return flag
python
def _use_datastore(self, key, options=None): """Return whether to use the datastore for this key. Args: key: Key instance. options: ContextOptions instance, or None. Returns: True if the datastore should be used, False otherwise. """ flag = ContextOptions.use_datastore(options) if flag is None: flag = self._datastore_policy(key) if flag is None: flag = ContextOptions.use_datastore(self._conn.config) if flag is None: flag = True return flag
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L428-L445
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.default_memcache_timeout_policy
def default_memcache_timeout_policy(key): """Default memcache timeout policy. This defers to _memcache_timeout on the Model class. Args: key: Key instance. Returns: Memcache timeout to use (integer), or None. """ timeout = None if key is not None and isinstance(key, model.Key): modelclass = model.Model._kind_map.get(key.kind()) if modelclass is not None: policy = getattr(modelclass, '_memcache_timeout', None) if policy is not None: if isinstance(policy, (int, long)): timeout = policy else: timeout = policy(key) return timeout
python
def default_memcache_timeout_policy(key): """Default memcache timeout policy. This defers to _memcache_timeout on the Model class. Args: key: Key instance. Returns: Memcache timeout to use (integer), or None. """ timeout = None if key is not None and isinstance(key, model.Key): modelclass = model.Model._kind_map.get(key.kind()) if modelclass is not None: policy = getattr(modelclass, '_memcache_timeout', None) if policy is not None: if isinstance(policy, (int, long)): timeout = policy else: timeout = policy(key) return timeout
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L448-L469
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.set_memcache_timeout_policy
def set_memcache_timeout_policy(self, func): """Set the policy function for memcache timeout (expiration). Args: func: A function that accepts a key instance as argument and returns an integer indicating the desired memcache timeout. May be None. If the function returns 0 it implies the default timeout. """ if func is None: func = self.default_memcache_timeout_policy elif isinstance(func, (int, long)): func = lambda unused_key, flag=func: flag self._memcache_timeout_policy = func
python
def set_memcache_timeout_policy(self, func): """Set the policy function for memcache timeout (expiration). Args: func: A function that accepts a key instance as argument and returns an integer indicating the desired memcache timeout. May be None. If the function returns 0 it implies the default timeout. """ if func is None: func = self.default_memcache_timeout_policy elif isinstance(func, (int, long)): func = lambda unused_key, flag=func: flag self._memcache_timeout_policy = func
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L473-L486
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context._get_memcache_timeout
def _get_memcache_timeout(self, key, options=None): """Return the memcache timeout (expiration) for this key.""" timeout = ContextOptions.memcache_timeout(options) if timeout is None: timeout = self._memcache_timeout_policy(key) if timeout is None: timeout = ContextOptions.memcache_timeout(self._conn.config) if timeout is None: timeout = 0 return timeout
python
def _get_memcache_timeout(self, key, options=None): """Return the memcache timeout (expiration) for this key.""" timeout = ContextOptions.memcache_timeout(options) if timeout is None: timeout = self._memcache_timeout_policy(key) if timeout is None: timeout = ContextOptions.memcache_timeout(self._conn.config) if timeout is None: timeout = 0 return timeout
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L492-L501
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context._load_from_cache_if_available
def _load_from_cache_if_available(self, key): """Returns a cached Model instance given the entity key if available. Args: key: Key instance. Returns: A Model instance if the key exists in the cache. """ if key in self._cache: entity = self._cache[key] # May be None, meaning "doesn't exist". if entity is None or entity._key == key: # If entity's key didn't change later, it is ok. # See issue 13. http://goo.gl/jxjOP raise tasklets.Return(entity)
python
def _load_from_cache_if_available(self, key): """Returns a cached Model instance given the entity key if available. Args: key: Key instance. Returns: A Model instance if the key exists in the cache. """ if key in self._cache: entity = self._cache[key] # May be None, meaning "doesn't exist". if entity is None or entity._key == key: # If entity's key didn't change later, it is ok. # See issue 13. http://goo.gl/jxjOP raise tasklets.Return(entity)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L518-L532
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.get
def get(self, key, **ctx_options): """Return a Model instance given the entity key. It will use the context cache if the cache policy for the given key is enabled. Args: key: Key instance. **ctx_options: Context options. Returns: A Model instance if the key exists in the datastore; None otherwise. """ options = _make_ctx_options(ctx_options) use_cache = self._use_cache(key, options) if use_cache: self._load_from_cache_if_available(key) use_datastore = self._use_datastore(key, options) if (use_datastore and isinstance(self._conn, datastore_rpc.TransactionalConnection)): use_memcache = False else: use_memcache = self._use_memcache(key, options) ns = key.namespace() memcache_deadline = None # Avoid worries about uninitialized variable. if use_memcache: mkey = self._memcache_prefix + key.urlsafe() memcache_deadline = self._get_memcache_deadline(options) mvalue = yield self.memcache_get(mkey, for_cas=use_datastore, namespace=ns, use_cache=True, deadline=memcache_deadline) # A value may have appeared while yielding. if use_cache: self._load_from_cache_if_available(key) if mvalue not in (_LOCKED, None): cls = model.Model._lookup_model(key.kind(), self._conn.adapter.default_model) pb = entity_pb.EntityProto() try: pb.MergePartialFromString(mvalue) except ProtocolBuffer.ProtocolBufferDecodeError: logging.warning('Corrupt memcache entry found ' 'with key %s and namespace %s' % (mkey, ns)) mvalue = None else: entity = cls._from_pb(pb) # Store the key on the entity since it wasn't written to memcache. entity._key = key if use_cache: # Update in-memory cache. self._cache[key] = entity raise tasklets.Return(entity) if mvalue is None and use_datastore: yield self.memcache_set(mkey, _LOCKED, time=_LOCK_TIME, namespace=ns, use_cache=True, deadline=memcache_deadline) yield self.memcache_gets(mkey, namespace=ns, use_cache=True, deadline=memcache_deadline) if not use_datastore: # NOTE: Do not cache this miss. In some scenarios this would # prevent an app from working properly. raise tasklets.Return(None) if use_cache: entity = yield self._get_batcher.add_once(key, options) else: entity = yield self._get_batcher.add(key, options) if entity is not None: if use_memcache and mvalue != _LOCKED: # Don't serialize the key since it's already the memcache key. pbs = entity._to_pb(set_key=False).SerializePartialToString() # Don't attempt to write to memcache if too big. Note that we # use LBYL ("look before you leap") because a multi-value # memcache operation would fail for all entities rather than # for just the one that's too big. (Also, the AutoBatcher # class doesn't pass back exceptions very well.) if len(pbs) <= memcache.MAX_VALUE_SIZE: timeout = self._get_memcache_timeout(key, options) # Don't use fire-and-forget -- for users who forget # @ndb.toplevel, it's too painful to diagnose why their simple # code using a single synchronous call doesn't seem to use # memcache. See issue 105. http://goo.gl/JQZxp yield self.memcache_cas(mkey, pbs, time=timeout, namespace=ns, deadline=memcache_deadline) if use_cache: # Cache hit or miss. NOTE: In this case it is okay to cache a # miss; the datastore is the ultimate authority. self._cache[key] = entity raise tasklets.Return(entity)
python
def get(self, key, **ctx_options): """Return a Model instance given the entity key. It will use the context cache if the cache policy for the given key is enabled. Args: key: Key instance. **ctx_options: Context options. Returns: A Model instance if the key exists in the datastore; None otherwise. """ options = _make_ctx_options(ctx_options) use_cache = self._use_cache(key, options) if use_cache: self._load_from_cache_if_available(key) use_datastore = self._use_datastore(key, options) if (use_datastore and isinstance(self._conn, datastore_rpc.TransactionalConnection)): use_memcache = False else: use_memcache = self._use_memcache(key, options) ns = key.namespace() memcache_deadline = None # Avoid worries about uninitialized variable. if use_memcache: mkey = self._memcache_prefix + key.urlsafe() memcache_deadline = self._get_memcache_deadline(options) mvalue = yield self.memcache_get(mkey, for_cas=use_datastore, namespace=ns, use_cache=True, deadline=memcache_deadline) # A value may have appeared while yielding. if use_cache: self._load_from_cache_if_available(key) if mvalue not in (_LOCKED, None): cls = model.Model._lookup_model(key.kind(), self._conn.adapter.default_model) pb = entity_pb.EntityProto() try: pb.MergePartialFromString(mvalue) except ProtocolBuffer.ProtocolBufferDecodeError: logging.warning('Corrupt memcache entry found ' 'with key %s and namespace %s' % (mkey, ns)) mvalue = None else: entity = cls._from_pb(pb) # Store the key on the entity since it wasn't written to memcache. entity._key = key if use_cache: # Update in-memory cache. self._cache[key] = entity raise tasklets.Return(entity) if mvalue is None and use_datastore: yield self.memcache_set(mkey, _LOCKED, time=_LOCK_TIME, namespace=ns, use_cache=True, deadline=memcache_deadline) yield self.memcache_gets(mkey, namespace=ns, use_cache=True, deadline=memcache_deadline) if not use_datastore: # NOTE: Do not cache this miss. In some scenarios this would # prevent an app from working properly. raise tasklets.Return(None) if use_cache: entity = yield self._get_batcher.add_once(key, options) else: entity = yield self._get_batcher.add(key, options) if entity is not None: if use_memcache and mvalue != _LOCKED: # Don't serialize the key since it's already the memcache key. pbs = entity._to_pb(set_key=False).SerializePartialToString() # Don't attempt to write to memcache if too big. Note that we # use LBYL ("look before you leap") because a multi-value # memcache operation would fail for all entities rather than # for just the one that's too big. (Also, the AutoBatcher # class doesn't pass back exceptions very well.) if len(pbs) <= memcache.MAX_VALUE_SIZE: timeout = self._get_memcache_timeout(key, options) # Don't use fire-and-forget -- for users who forget # @ndb.toplevel, it's too painful to diagnose why their simple # code using a single synchronous call doesn't seem to use # memcache. See issue 105. http://goo.gl/JQZxp yield self.memcache_cas(mkey, pbs, time=timeout, namespace=ns, deadline=memcache_deadline) if use_cache: # Cache hit or miss. NOTE: In this case it is okay to cache a # miss; the datastore is the ultimate authority. self._cache[key] = entity raise tasklets.Return(entity)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L542-L637
train
GoogleCloudPlatform/datastore-ndb-python
ndb/context.py
Context.call_on_commit
def call_on_commit(self, callback): """Call a callback upon successful commit of a transaction. If not in a transaction, the callback is called immediately. In a transaction, multiple callbacks may be registered and will be called once the transaction commits, in the order in which they were registered. If the transaction fails, the callbacks will not be called. If the callback raises an exception, it bubbles up normally. This means: If the callback is called immediately, any exception it raises will bubble up immediately. If the call is postponed until commit, remaining callbacks will be skipped and the exception will bubble up through the transaction() call. (However, the transaction is already committed at that point.) """ if not self.in_transaction(): callback() else: self._on_commit_queue.append(callback)
python
def call_on_commit(self, callback): """Call a callback upon successful commit of a transaction. If not in a transaction, the callback is called immediately. In a transaction, multiple callbacks may be registered and will be called once the transaction commits, in the order in which they were registered. If the transaction fails, the callbacks will not be called. If the callback raises an exception, it bubbles up normally. This means: If the callback is called immediately, any exception it raises will bubble up immediately. If the call is postponed until commit, remaining callbacks will be skipped and the exception will bubble up through the transaction() call. (However, the transaction is already committed at that point.) """ if not self.in_transaction(): callback() else: self._on_commit_queue.append(callback)
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Call a callback upon successful commit of a transaction. If not in a transaction, the callback is called immediately. In a transaction, multiple callbacks may be registered and will be called once the transaction commits, in the order in which they were registered. If the transaction fails, the callbacks will not be called. If the callback raises an exception, it bubbles up normally. This means: If the callback is called immediately, any exception it raises will bubble up immediately. If the call is postponed until commit, remaining callbacks will be skipped and the exception will bubble up through the transaction() call. (However, the transaction is already committed at that point.)
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/context.py#L908-L928
train
GoogleCloudPlatform/datastore-ndb-python
demo/app/main.py
get_nickname
def get_nickname(userid): """Return a Future for a nickname from an account.""" account = yield get_account(userid) if not account: nickname = 'Unregistered' else: nickname = account.nickname or account.email raise ndb.Return(nickname)
python
def get_nickname(userid): """Return a Future for a nickname from an account.""" account = yield get_account(userid) if not account: nickname = 'Unregistered' else: nickname = account.nickname or account.email raise ndb.Return(nickname)
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Return a Future for a nickname from an account.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/app/main.py#L111-L118
train
GoogleCloudPlatform/datastore-ndb-python
demo/task_list.py
mark_done
def mark_done(task_id): """Marks a task as done. Args: task_id: The integer id of the task to update. Raises: ValueError: if the requested task doesn't exist. """ task = Task.get_by_id(task_id) if task is None: raise ValueError('Task with id %d does not exist' % task_id) task.done = True task.put()
python
def mark_done(task_id): """Marks a task as done. Args: task_id: The integer id of the task to update. Raises: ValueError: if the requested task doesn't exist. """ task = Task.get_by_id(task_id) if task is None: raise ValueError('Task with id %d does not exist' % task_id) task.done = True task.put()
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Marks a task as done. Args: task_id: The integer id of the task to update. Raises: ValueError: if the requested task doesn't exist.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/task_list.py#L61-L74
train
GoogleCloudPlatform/datastore-ndb-python
demo/task_list.py
format_tasks
def format_tasks(tasks): """Converts a list of tasks to a list of string representations. Args: tasks: A list of the tasks to convert. Returns: A list of string formatted tasks. """ return ['%d : %s (%s)' % (task.key.id(), task.description, ('done' if task.done else 'created %s' % task.created)) for task in tasks]
python
def format_tasks(tasks): """Converts a list of tasks to a list of string representations. Args: tasks: A list of the tasks to convert. Returns: A list of string formatted tasks. """ return ['%d : %s (%s)' % (task.key.id(), task.description, ('done' if task.done else 'created %s' % task.created)) for task in tasks]
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Converts a list of tasks to a list of string representations. Args: tasks: A list of the tasks to convert. Returns: A list of string formatted tasks.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/task_list.py#L102-L114
train
GoogleCloudPlatform/datastore-ndb-python
demo/task_list.py
handle_command
def handle_command(command): """Accepts a string command and performs an action. Args: command: the command to run as a string. """ try: cmds = command.split(None, 1) cmd = cmds[0] if cmd == 'new': add_task(get_arg(cmds)) elif cmd == 'done': mark_done(int(get_arg(cmds))) elif cmd == 'list': for task in format_tasks(list_tasks()): print task elif cmd == 'delete': delete_task(int(get_arg(cmds))) else: print_usage() except Exception, e: # pylint: disable=broad-except print e print_usage()
python
def handle_command(command): """Accepts a string command and performs an action. Args: command: the command to run as a string. """ try: cmds = command.split(None, 1) cmd = cmds[0] if cmd == 'new': add_task(get_arg(cmds)) elif cmd == 'done': mark_done(int(get_arg(cmds))) elif cmd == 'list': for task in format_tasks(list_tasks()): print task elif cmd == 'delete': delete_task(int(get_arg(cmds))) else: print_usage() except Exception, e: # pylint: disable=broad-except print e print_usage()
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/demo/task_list.py#L135-L157
train
GoogleCloudPlatform/datastore-ndb-python
ndb/blobstore.py
delete_async
def delete_async(blob_key, **options): """Async version of delete().""" if not isinstance(blob_key, (basestring, BlobKey)): raise TypeError('Expected blob key, got %r' % (blob_key,)) rpc = blobstore.create_rpc(**options) yield blobstore.delete_async(blob_key, rpc=rpc)
python
def delete_async(blob_key, **options): """Async version of delete().""" if not isinstance(blob_key, (basestring, BlobKey)): raise TypeError('Expected blob key, got %r' % (blob_key,)) rpc = blobstore.create_rpc(**options) yield blobstore.delete_async(blob_key, rpc=rpc)
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Async version of delete().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/blobstore.py#L273-L278
train
GoogleCloudPlatform/datastore-ndb-python
ndb/blobstore.py
delete_multi_async
def delete_multi_async(blob_keys, **options): """Async version of delete_multi().""" if isinstance(blob_keys, (basestring, BlobKey)): raise TypeError('Expected a list, got %r' % (blob_key,)) rpc = blobstore.create_rpc(**options) yield blobstore.delete_async(blob_keys, rpc=rpc)
python
def delete_multi_async(blob_keys, **options): """Async version of delete_multi().""" if isinstance(blob_keys, (basestring, BlobKey)): raise TypeError('Expected a list, got %r' % (blob_key,)) rpc = blobstore.create_rpc(**options) yield blobstore.delete_async(blob_keys, rpc=rpc)
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Async version of delete_multi().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/blobstore.py#L293-L298
train
GoogleCloudPlatform/datastore-ndb-python
ndb/blobstore.py
create_upload_url
def create_upload_url(success_path, max_bytes_per_blob=None, max_bytes_total=None, **options): """Create upload URL for POST form. Args: success_path: Path within application to call when POST is successful and upload is complete. max_bytes_per_blob: The maximum size in bytes that any one blob in the upload can be or None for no maximum size. max_bytes_total: The maximum size in bytes that the aggregate sizes of all of the blobs in the upload can be or None for no maximum size. **options: Options for create_rpc(). Returns: The upload URL. Raises: TypeError: If max_bytes_per_blob or max_bytes_total are not integral types. ValueError: If max_bytes_per_blob or max_bytes_total are not positive values. """ fut = create_upload_url_async(success_path, max_bytes_per_blob=max_bytes_per_blob, max_bytes_total=max_bytes_total, **options) return fut.get_result()
python
def create_upload_url(success_path, max_bytes_per_blob=None, max_bytes_total=None, **options): """Create upload URL for POST form. Args: success_path: Path within application to call when POST is successful and upload is complete. max_bytes_per_blob: The maximum size in bytes that any one blob in the upload can be or None for no maximum size. max_bytes_total: The maximum size in bytes that the aggregate sizes of all of the blobs in the upload can be or None for no maximum size. **options: Options for create_rpc(). Returns: The upload URL. Raises: TypeError: If max_bytes_per_blob or max_bytes_total are not integral types. ValueError: If max_bytes_per_blob or max_bytes_total are not positive values. """ fut = create_upload_url_async(success_path, max_bytes_per_blob=max_bytes_per_blob, max_bytes_total=max_bytes_total, **options) return fut.get_result()
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Create upload URL for POST form. Args: success_path: Path within application to call when POST is successful and upload is complete. max_bytes_per_blob: The maximum size in bytes that any one blob in the upload can be or None for no maximum size. max_bytes_total: The maximum size in bytes that the aggregate sizes of all of the blobs in the upload can be or None for no maximum size. **options: Options for create_rpc(). Returns: The upload URL. Raises: TypeError: If max_bytes_per_blob or max_bytes_total are not integral types. ValueError: If max_bytes_per_blob or max_bytes_total are not positive values.
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/blobstore.py#L301-L328
train
GoogleCloudPlatform/datastore-ndb-python
ndb/blobstore.py
create_upload_url_async
def create_upload_url_async(success_path, max_bytes_per_blob=None, max_bytes_total=None, **options): """Async version of create_upload_url().""" rpc = blobstore.create_rpc(**options) rpc = blobstore.create_upload_url_async(success_path, max_bytes_per_blob=max_bytes_per_blob, max_bytes_total=max_bytes_total, rpc=rpc) result = yield rpc raise tasklets.Return(result)
python
def create_upload_url_async(success_path, max_bytes_per_blob=None, max_bytes_total=None, **options): """Async version of create_upload_url().""" rpc = blobstore.create_rpc(**options) rpc = blobstore.create_upload_url_async(success_path, max_bytes_per_blob=max_bytes_per_blob, max_bytes_total=max_bytes_total, rpc=rpc) result = yield rpc raise tasklets.Return(result)
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Async version of create_upload_url().
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cf4cab3f1f69cd04e1a9229871be466b53729f3f
https://github.com/GoogleCloudPlatform/datastore-ndb-python/blob/cf4cab3f1f69cd04e1a9229871be466b53729f3f/ndb/blobstore.py#L332-L343
train