Use datawidget union

Author: vidartf

js/scripts/generate-wrappers.js

@@ -423,14 +423,13 @@ _.extend(JavascriptWrapper.prototype, {
 
         }, this);
 
-        this.serializedProps = _.reduce(this.config.properties, function(result, prop, propName) {
-
-            if (prop.serialize) {
-                result.push(propName);
-            }
-            return result;
-
-        }, []);
+        this.serializedProps = _.mapObject(_.pick(this.config.properties,
+            function(prop, propName) {
+                return !!prop.serializer;
+            }),
+            function(prop, propName) {
+                return prop.serializer;
+            }, {});
 
         this.enum_properties = _.reduce(this.config.properties, function(result, prop, propName) {
             if (prop.enumTypeName) {

js/scripts/prop-types.js

@@ -1,5 +1,7 @@
 var _ = require('underscore');
 
+var WIDGET_SERIALIZER = '{ deserialize: widgets.unpack_models }';
+
 function BaseType() {}
 _.extend(BaseType.prototype, {
     getJSPropertyValue: function() {
@@ -30,7 +32,7 @@ _.extend(BaseType.prototype, {
 function ThreeType(typeName, options={}) {
     this.typeName = typeName;
     this.defaultValue = null;
-    this.serialize = true;
+    this.serializer = WIDGET_SERIALIZER;
     this.nullable = options.nullable !== false;
     this.args = options.args;
     this.kwargs = options.kwargs;
@@ -114,7 +116,7 @@ _.extend(InitializedThreeType.prototype, ThreeType.prototype, {
 function ThreeTypeArray(typeName) {
     this.typeName = typeName;
     this.defaultValue = [];
-    this.serialize = true;
+    this.serializer = WIDGET_SERIALIZER;
 }
 _.extend(ThreeTypeArray.prototype, BaseType.prototype, {
     getTraitlet: function() {
@@ -136,7 +138,7 @@ _.extend(ThreeTypeArray.prototype, BaseType.prototype, {
 function ThreeTypeDict(typeName) {
     this.typeName = typeName;
     this.defaultValue = {};
-    this.serialize = true;
+    this.serializer = WIDGET_SERIALIZER;
 }
 _.extend(ThreeTypeDict.prototype, BaseType.prototype, {
     getTraitlet: function() {
@@ -260,22 +262,24 @@ _.extend(ArrayType.prototype, BaseType.prototype, {
     },
 });
 
-// TODO: support more than Float32Array
-function ArrayBufferType(arrayType, shape_constraint) {
+
+function ArrayBufferType(arrayType, shapeConstraint) {
     this.arrayType = arrayType;
-    this.shape_constraint = shape_constraint;
+    this.shapeConstraint = shapeConstraint;
     this.defaultValue = null;
+    this.serializer = 'datawidgets.data_union_serialization';
 }
 _.extend(ArrayBufferType.prototype, BaseType.prototype, {
     getTraitlet: function() {
-        var r = `NDArray(dtype=${this.arrayType || 'None'}).tag(sync=True, **array_serialization)`;
-        if (this.shape_constraint) {
-            var constraint = this.shape_constraint.reduce(function(wip, element) {
-                return wip + `, ${element === null ? 'None' : element}`;
-            }, this);
-            r += `.valid(shape_constraint([${this.shape_constraint}]))`
+        var args = [];
+        if (this.arrayType) {
+            args.push(`dtype=${this.arrayType}`);
         }
-        return r;
+        if (this.shapeConstraint) {
+            args.push(`shape_constraint=${this.shapeConstraint}`);
+        }
+
+        return `DataUnion(${args.join(', ')}).tag(sync=True)`;
     },
     getPropertyConverterFn: function() {
         return 'convertArrayBuffer';

js/scripts/templates/js_wrapper.mustache

@@ -72,12 +72,12 @@ var {{ modelName }} = {{ superClass.modelName }}.extend({
 
     model_name: '{{ modelName }}',
 
-    {{#if serialized_props}}
+    {{#if serialized_props }}
     serializers: _.extend({
-        {{#each serialized_props as |propName|}}
-        {{ propName }}: { deserialize: widgets.unpack_models },
+        {{#each serialized_props as |serializer propName|}}
+        {{ propName }}: {{ serializer }},
         {{/each}}
-    }, {{ superClass.modelName }}.serializers),
+    }{{#if superClass }},  {{ superClass.modelName }}.serializers){{/if}},
     {{/if}}
 });
 

js/scripts/templates/py_wrapper.mustache

@@ -6,7 +6,7 @@ from traitlets import (
     Tuple, List, Dict, Float, CFloat, Bool, Union, Any,
     )
 
-from ipydatawidgets import NDArray, array_serialization, NDArrayWidget
+from ipydatawidgets import DataUnion
 
 from {{ py_base_relative_path }}enums import *
 from {{ py_base_relative_path }}traits import *

js/src/core/BufferAttribute.js

@@ -15,16 +15,13 @@ var BufferAttributeModel = BufferAttributeAutogen.extend({
 
     decodeData() {
         var rawData = this.get('array');
-        if (rawData.dimension > 2) {
-            throw Error('Array has too many dimensions:', array)
-        }
-        var itemSize = rawData.dimension === 1 ? 1 : rawData.shape[1];
+        var itemSize = rawData.dimension === 1 ? 1 : rawData.shape[rawData.dimension - 1];
 
         var data = this.convertArrayBufferModelToThree(rawData, 'array');
         return {
             array: data,
             itemSize: itemSize,
-        }
+        };
     },
 
     constructThreeObject: function() {
@@ -56,10 +53,6 @@ var BufferAttributeModel = BufferAttributeAutogen.extend({
         }
     },
 
-}, {
-    serializers: _.extend({
-        array: datawidgets.array_serialization,
-    }, BufferAttributeAutogen.serializers),
 });
 
 module.exports = {

pythreejs/core/BufferAttribute.py

@@ -1,7 +1,7 @@
 import numpy as np
 from ipywidgets import register
 from traitlets import validate, TraitError, Undefined
-from ipydatawidgets import NDArray, array_serialization, NDArrayWidget
+from ipydatawidgets import NDArrayWidget
 
 from .BufferAttribute_autogen import BufferAttribute as BaseBufferAttribute
 
@@ -20,11 +20,14 @@ def __init__(self, array=None, normalized=True, **kwargs):
 
     @validate('array')
     def _valid_array(self, proposal):
-        # Validate shape
-        if np.ndim(proposal) > 2:
-            raise TraitError('Array needs to have at most two dimensions. Given shape was: %r'
-                % np.shape(proposal))
         value = proposal['value']
+        if isinstance(value, NDArrayWidget):
+            value = value.array
+
+        if isinstance(proposal['value'], NDArrayWidget):
+            if value is not Undefined and value.dtype == np.float64:
+                raise TraitError('Cannot use a float64 data widget as a BufferAttribute source.')
+            return proposal['value']
         if value is not Undefined and value.dtype == np.float64:
             # 64-bit not supported, coerce to 32-bit
             value = value.astype(np.float32)

pythreejs/textures/DataTexture.py

@@ -1,6 +1,7 @@
 import numpy as np
 from ipywidgets import register
-from traitlets import validate, TraitError
+from traitlets import validate, TraitError, Undefined
+from ipydatawidgets import NDArrayWidget
 
 from .DataTexture_autogen import DataTexture as BaseDataTexture
 
@@ -10,15 +11,26 @@ class DataTexture(BaseDataTexture):
 
     @validate('data')
     def _valid_data(self, proposal):
+        value = proposal['value']
+        if isinstance(value, NDArrayWidget):
+            value = value.array
+
         # Validate shape
-        if np.ndim(proposal) > 2:
+        if np.ndim(value) < 2 or np.ndim(value) > 3:
             raise TraitError('Data needs to have two or three dimensions. Given shape was: %r'
-                % np.shape(proposal))
+                % (np.shape(value),))
+
         old = self._trait_values.get(proposal['trait'].name, None)
-        value = proposal['value']
-        if old is not None and np.shape(old) != np.shape(value):
-            raise TraitError('Cannot change shape of previously initialized DataTexture.')
-        if value.dtype == np.float64:
+        if old is not None and np.ndim(old) > 0 and np.shape(old) != np.shape(value):
+            raise TraitError('Cannot change shape of previously initialized DataTexture. %r vs %r'
+                % (np.shape(old), np.shape(value)))
+
+        if isinstance(proposal['value'], NDArrayWidget):
+            if value is not Undefined and value.dtype == np.float64:
+                raise TraitError('Cannot use a float64 data widget as a BufferAttribute source.')
+            return proposal['value']
+        if value is not Undefined and value.dtype == np.float64:
             # 64-bit not supported, coerce to 32-bit
             value = value.astype(np.float32)
         return value
+