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get the filednames for the idfobject based on endswith
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)] |
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
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 |
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
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 |
initialze values for all the inlet outlet nodes for the object.
if force == False, it willl init only if field = ''
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 |
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
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 |
make an airloop
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 |
make plant loop with pip components
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 |
make condenser loop with pipe components
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 |
force it to be a list of tuples
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] |
force 3 items in the tuple
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] |
get idfobject or make it if it does not exist
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 |
do I even use this ? .... yup! I do
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 |
It will replace the components in the branch with components in
listofcomponents
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 |
the main routine
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") |
Area of a polygon poly
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) |
unit normal vector of plane defined by points pt_a, pt_b, and pt_c
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 |
Width of a polygon poly
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])) |
Height of a polygon poly
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])) |
angle between two vectors
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)) |
Azimuth of a polygon poly
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) |
Tilt of a polygon poly
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) |
get all the fields that have the key 'field'
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 |
get the names of the repeating fields
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 |
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
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 |
This is an object list where thre is no first field name
to give a hint of what the first field name should be
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,),] |
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.
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 |
Remove all pending events without running any.
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') |
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.
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) |
Schedule a function call at a specific time in the future.
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)) |
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?
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) |
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.
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)) |
Run one of the idle callbacks.
Returns:
True if one was called, False if no idle callback was called.
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 |
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.
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 |
Run one item (a callback or an RPC wait_any) or sleep.
Returns:
True if something happened; False if all queues are empty.
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 |
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.
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({}, {}) |
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.
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 |
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.
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 |
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.
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 |
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.
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) |
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.
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) |
Run this query, putting entities into the given 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 |
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.
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))) |
Return a new Query with additional filter(s) applied.
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) |
Return a new Query with additional sort order(s) applied.
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) |
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.)
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() |
Map a callback function or tasklet over the query results.
This is the asynchronous version of Query.map().
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) |
Fetch a list of query results, up to a limit.
This is the asynchronous version of Query.fetch().
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) |
Internal version of 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]) |
Count the number of query results, up to a limit.
This is the asynchronous version of Query.count().
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) |
Internal version of 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) |
Fetch a page of results.
This is the asynchronous version of Query.fetch_page().
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) |
Internal version of 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()) |
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.
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 |
Return a list giving the parameters required by a query.
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) |
Bind parameter values. Returns a new Query object.
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) |
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.
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 |
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.
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 |
Return a Future whose result will say whether a next item is available.
See the module docstring for the usage pattern.
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) |
Iterator protocol: get next item or raise StopIteration.
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 |
Run this query, putting entities into the given 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() |
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.
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) |
A decorator to declare that only the first N arguments may be positional.
Note that for methods, n includes 'self'.
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 |
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 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 |
A recursive Fibonacci to exercise task switching.
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) |
A memoizing recursive Fibonacci to exercise RPCs.
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) |
Actually run the _todo_tasklet.
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) |
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.
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 |
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.
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) |
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.
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] |
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.
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] |
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.
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)] |
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.
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 |
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.
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 |
Return the Key for a namespace.
Args:
namespace: A string giving the namespace whose key is requested.
Returns:
The Key for the 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) |
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.
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) |
Return the kind specified by a given __property__ key.
Args:
key: key whose kind name is requested.
Returns:
The kind specified by key.
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() |
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.
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()) |
Called by Django before deciding which view to execute.
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) |
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.
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) |
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.
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 |
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.
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 |
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.
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 |
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.
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 |
Default datastore policy.
This defers to _use_datastore on the Model class.
Args:
key: Key instance.
Returns:
A bool or None.
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 |
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.
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 |
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.
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 |
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.
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 |
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.
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 |
Return the memcache timeout (expiration) for this key.
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 |
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.
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) |
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.
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) |
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.)
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) |
Return a Future for a nickname from an account.
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) |
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.
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() |
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.
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] |
Accepts a string command and performs an action.
Args:
command: the command to run as a string.
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() |
Async version of delete().
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) |
Async version of delete_multi().
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) |
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.
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() |
Async version of create_upload_url().
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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