supporting user defined view collection through either inputting list…

… of views or Spec-based creation of views

lux/action/Correlation.py

@@ -36,7 +36,7 @@ def correlation(ldf:LuxDataFrame,ignoreTranspose:bool=False):
 						   "description":"Show relationships between two quantitative variables."}
 	vc = ldf.viewCollection
 	# if (ignoreIdentity): vc = filter(lambda x: x.specLst[0].attribute!=x.specLst[1].attribute,ldf.viewCollection)
-	vc = Compiler.compile(ldf, vc, enumerateCollection=False)
+	vc = Compiler.compile(ldf,ldf.context, vc, enumerateCollection=False)
 
 	ldf.executor.execute(vc,ldf)
 	# Then use the data populated in the view collection to compute score

lux/action/Enhance.py

@@ -44,7 +44,7 @@ def enhance(ldf):
 			view = lux.view.View.View(cxtNew)
 			output.append(view)
 	vc = lux.view.ViewCollection.ViewCollection(output)
-	vc = Compiler.compile(ldf,vc,enumerateCollection=False)
+	vc = Compiler.compile(ldf,ldf.context,vc,enumerateCollection=False)
 
 	ldf.executor.execute(vc,ldf)
 

lux/action/Filter.py

@@ -66,7 +66,7 @@ def filter(ldf):
 				tempView = View(newSpec)
 				output.append(tempView)
 	vc = lux.view.ViewCollection.ViewCollection(output)
-	vc = Compiler.compile(ldf,vc,enumerateCollection=False)
+	vc = Compiler.compile(ldf,ldf.context,vc,enumerateCollection=False)
 
 	ldf.executor.execute(vc,ldf)
 	for view in vc:

lux/action/Generalize.py

@@ -61,7 +61,7 @@ def generalize(ldf):
 		output.append(tempView)
 
 	vc = lux.view.ViewCollection.ViewCollection(output)
-	vc = Compiler.compile(ldf,vc,enumerateCollection=False)
+	vc = Compiler.compile(ldf,ldf.context,vc,enumerateCollection=False)
 	ldf.executor.execute(vc,ldf)
 	recommendation["collection"] = vc
 	for view in vc:

lux/compiler/Compiler.py

@@ -19,7 +19,7 @@ def __repr__(self):
 		return f"<Compiler>"
 
 	@staticmethod
-	def compile(ldf: LuxDataFrame, viewCollection: ViewCollection, enumerateCollection=True) -> ViewCollection:
+	def compile(ldf: LuxDataFrame,specLst:List[Spec], viewCollection: ViewCollection, enumerateCollection=True) -> ViewCollection:
 		"""
 		Compiles input specifications in the context of the ldf into a collection of lux.View objects for visualization.
 		1) Enumerate a collection of views interested by the user to generate a view collection
@@ -41,15 +41,15 @@ def compile(ldf: LuxDataFrame, viewCollection: ViewCollection, enumerateCollecti
 			view collection with compiled lux.View objects.
 		"""
 		if (enumerateCollection):
-			viewCollection = Compiler.enumerateCollection(ldf)
+			viewCollection = Compiler.enumerateCollection(specLst,ldf)
 		viewCollection = Compiler.expandUnderspecified(ldf, viewCollection)  # autofill data type/model information
 		viewCollection = Compiler.removeAllInvalid(viewCollection) # remove invalid views from collection
 		for view in viewCollection:
 			Compiler.determineEncoding(ldf, view)  # autofill viz related information
 		return viewCollection
 
 	@staticmethod
-	def enumerateCollection(ldf: LuxDataFrame) -> ViewCollection:
+	def enumerateCollection(specLst:List[Spec],ldf: LuxDataFrame) -> ViewCollection:
 		"""
 		Given specifications that have been expanded thorught populateOptions,
 		recursively iterate over the resulting list combinations to generate a View collection.
@@ -65,7 +65,7 @@ def enumerateCollection(ldf: LuxDataFrame) -> ViewCollection:
 			view collection with compiled lux.View objects.
 		"""
 		import copy
-		specs = Compiler.populateWildcardOptions(ldf)
+		specs = Compiler.populateWildcardOptions(specLst,ldf)
 		attributes = specs['attributes']
 		filters = specs['filters']
 		if len(attributes) == 0 and len(filters) > 0:
@@ -91,7 +91,6 @@ def combine(colAttrs, accum):
 						collection.append(view)
 				else:
 					combine(colAttrs[1:], columnList)
-
 		combine(attributes, [])
 		return ViewCollection(collection)
 
@@ -348,8 +347,8 @@ def enforceSpecifiedChannel(view: View, autoChannel: Dict[str,str]):
 		return view
 
 	@staticmethod
-	def populateWildcardOptions(ldf: LuxDataFrame) -> dict:
-	# def populateWildcardOptions(specLst:List[Spec],ldf: LuxDataFrame) -> dict:
+	# def populateWildcardOptions(ldf: LuxDataFrame) -> dict:
+	def populateWildcardOptions(specLst:List[Spec],ldf: LuxDataFrame) -> dict:
 		"""
 		Given wildcards and constraints in the LuxDataFrame's context,
 		return the list of available values that satisfies the dataType or dataModel constraints.
@@ -368,7 +367,7 @@ def populateWildcardOptions(ldf: LuxDataFrame) -> dict:
 		from lux.utils.utils import convert2List
 
 		specs = {"attributes": [], "filters": []}
-		for spec in ldf.context:
+		for spec in specLst:
 			specOptions = []
 			if spec.value == "":  # attribute
 				if spec.attribute == "?":
@@ -392,8 +391,8 @@ def populateWildcardOptions(ldf: LuxDataFrame) -> dict:
 					options = []
 					if spec.value == "?":
 						options = ldf.uniqueValues[attr]
-						specInd = ldf.context.index(spec)
-						ldf.context[specInd] = Spec(attribute=spec.attribute, filterOp="=", value=list(options))
+						specInd = specLst.index(spec)
+						specLst[specInd] = Spec(attribute=spec.attribute, filterOp="=", value=list(options))
 					else:
 						options.extend(convert2List(spec.value))
 					for optStr in options:

lux/luxDataFrame/LuxDataframe.py

@@ -68,7 +68,7 @@ def _refreshContext(self):
             self.computeDatasetMetadata()
         self.context = Parser.parse(self.getContext())
         Validator.validateSpec(self.context,self)
-        viewCollection = Compiler.compile(self,self.viewCollection)
+        viewCollection = Compiler.compile(self,self.context,self.viewCollection)
         self.setViewCollection(viewCollection)
 
     def setContext(self,context:typing.List[typing.Union[str,Spec]]):

lux/view/View.py

@@ -151,6 +151,6 @@ def load(self, ldf) -> View:
 		#TODO: handle case when user input vanilla Pandas dataframe
 		self.specLst = Parser.parse(self.specLst)
 		Validator.validateSpec(self.specLst,ldf)
-		vc = Compiler.compile(ldf,[self],enumerateCollection=False)
+		vc = Compiler.compile(ldf,ldf.context,[self],enumerateCollection=False)
 		PandasExecutor.execute(vc,ldf)
 		return vc[0]

lux/view/ViewCollection.py

@@ -1,12 +1,22 @@
+from __future__ import annotations
 from lux.vizLib.altair.AltairRenderer import AltairRenderer
 from lux.utils.utils import checkImportLuxWidget
+from typing import List, Union
+from lux.view.View import View
+from lux.context.Spec import Spec
 class ViewCollection():
 	'''
 	ViewCollection is a list of View objects. 
 	'''
-	def __init__(self,collection):
-		self.collection=collection
-
+	def __init__(self,inputLst:Union[List[View],List[Spec]]):
+		# Overloaded Constructor
+		if (type(inputLst[0])==View):
+			self.collection = inputLst
+			self.inputType = "View"
+		elif (type(inputLst[0])==Spec):
+			self.specLst = inputLst
+			self.collection = []
+			self.inputType = "Spec"
 	def __getitem__(self, key):
 		return self.collection[key]
 	def __setitem__(self, key, value):
@@ -135,4 +145,38 @@ def _repr_html_(self):
 				recommendations=recJSON,
 				context={}
 			)
-		display(widget)	
+		display(widget)	
+
+	def load(self, ldf) -> ViewCollection:
+		"""
+		Loading the data into the views in the ViewCollection by instantiating the specification and populating the view based on the data, effectively "materializing" the view.
+
+		Parameters
+		----------
+		ldf : LuxDataframe
+			Input Dataframe to be attached to the ViewCollection
+
+		Returns
+		-------
+		ViewCollection
+			Complete ViewCollection with fully-specified fields
+		
+		See Also
+		--------
+		lux.view.View.load
+		"""		
+		from lux.compiler.Parser import Parser
+		from lux.compiler.Validator import Validator
+		from lux.compiler.Compiler import Compiler
+		from lux.executor.PandasExecutor import PandasExecutor #TODO: temporary (generalize to executor)
+		if (self.inputType=="View"):
+			for view in self.collection:
+				view.specLst = Parser.parse(view.specLst)
+				Validator.validateSpec(view.specLst,ldf)
+			vc = Compiler.compile(ldf,ldf.context,self.collection,enumerateCollection=False)
+		elif (self.inputType=="Spec"):
+			self.specLst = Parser.parse(self.specLst)
+			Validator.validateSpec(self.specLst,ldf)
+			vc = Compiler.compile(ldf,self.specLst,self)
+		PandasExecutor.execute(vc,ldf)
+		return vc