diff --git a/conda/js/src/components/experiment/ExperimentEditor.jsx b/conda/js/src/components/experiment/ExperimentEditor.jsx
index 0d9221b3968e34db72804f635818df0c0a28275f..78f3e502588f2e4468686f34b64f534d82757b90 100644
--- a/conda/js/src/components/experiment/ExperimentEditor.jsx
+++ b/conda/js/src/components/experiment/ExperimentEditor.jsx
@@ -588,7 +588,7 @@ export class ExperimentEditor extends React.Component<Props, State> {
//Object.keys(this.props.data.contents.datasets).map(dsName
const newDs = JSON.parse(str);
- console.log(newDs);
+ //console.log(newDs);
this.setContents({...this.props.data.contents, datasets: newDs});
for(const dataset in this.props.data.contents.datasets){
this.setLockMap(dataset, true);
@@ -719,6 +719,21 @@ export class ExperimentEditor extends React.Component<Props, State> {
</FormGroup>
);
+ // update a block's data (and the globals object, if it changed)
+ updateBlock = (blockName: string, isAnalyzer: boolean, newBlock: any, globals: any = this.props.data.contents.globals) => {
+ if(isAnalyzer)
+ delete newBlock.outputs;
+
+ const bKey = isAnalyzer ? 'analyzers' : 'blocks';
+ const newBlocks = copyObj(this.props.data.contents[bKey]);
+ newBlocks[blockName] = newBlock;
+ this.setContents({
+ ...this.props.data.contents,
+ [bKey]: newBlocks,
+ globals,
+ });
+ }
+
// renders the currently selected block to edit, if any
renderBlock = (blockName: string, block: any, isAnalyzer: boolean, key: number) => {
// gets the block info from the tc
@@ -742,19 +757,8 @@ export class ExperimentEditor extends React.Component<Props, State> {
});
// func to update the exp's block info
- const updateBlock = (newBlock, globals = this.props.data.contents.globals, newName = blockName) => {
- if(isAnalyzer)
- delete newBlock.outputs;
-
- const bKey = isAnalyzer ? 'analyzers' : 'blocks';
- const newBlocks = changeObjFieldName(this.props.data.contents[bKey], blockName, newName);
- newBlocks[newName] = newBlock;
- this.setContents({
- ...this.props.data.contents,
- [bKey]: newBlocks,
- globals,
- });
- };
+ const updateBlock = (newBlock: any, globals: any = this.props.data.contents.globals) =>
+ this.updateBlock(blockName, isAnalyzer, newBlock, globals);
// get info that could be from the block or from the global settings
const envInfo = block.environment || this.props.data.contents.globals.environment;
@@ -834,69 +838,112 @@ export class ExperimentEditor extends React.Component<Props, State> {
<Label>
Algorithm
</Label>
- <Input
- type='select'
- className='custom-select'
- valid={block.algorithm !== ''}
- value={block.algorithm}
- onChange={e => {
- // user selected a different algorithm
- const str = e.target.value;
- // find the alg or use a dummy algorithm instead
- const alg = algorithms.find(nb => nb.name === str) || {name: '', contents: {groups: []}};
- const [ inputs, outputs ] = algIOs(alg);
- // try to map the IOs from the algorithm to the block via names
- const findCloseIOMatch = (s: string, arr: string[]): string => {
- if(arr.length === 1)
- return arr[0];
- const findRes = arr.find(str => str.includes(s));
- if(findRes !== undefined)
- return findRes;
- return '';
- };
-
- // create the new block obj with the new alg
- const thisBlock = {
- ...block,
- algorithm: str,
- };
- // assign new inputs
- if(tcBlock.inputs)
- thisBlock.inputs = levMapStrings(Object.keys(inputs), tcBlock.inputs);
- // assign new outputs
- if(tcBlock.outputs)
- thisBlock.outputs = levMapStrings(Object.keys(outputs), tcBlock.outputs);
-
- // setup the parameters (erase & create stuff)
- const globals = {
- ...this.props.data.contents.globals,
- };
- if(alg.contents.parameters && Object.keys(alg.contents.parameters).length > 0)
- globals[alg.name] = {
- ...Object.entries(alg.contents.parameters || {})
- .map(([pName, param]) => ({[pName]: getDefaultParameterValue(param)}))
- .reduce((o, p) => ({...o, ...p}), {}),
- ...this.props.data.contents.globals[alg.name],
- };
+ <FormGroup row>
+ <Col>
+ <Input
+ type='select'
+ className='custom-select'
+ valid={block.algorithm !== ''}
+ value={block.algorithm}
+ onChange={e => {
+ // user selected a different algorithm
+ const str = e.target.value;
+ // find the alg or use a dummy algorithm instead
+ const alg = algorithms.find(nb => nb.name === str) || {name: '', contents: {groups: []}};
+ const [ inputs, outputs ] = algIOs(alg);
+ // try to map the IOs from the algorithm to the block via names
+ const findCloseIOMatch = (s: string, arr: string[]): string => {
+ if(arr.length === 1)
+ return arr[0];
+ const findRes = arr.find(str => str.includes(s));
+ if(findRes !== undefined)
+ return findRes;
+ return '';
+ };
+
+ // create the new block obj with the new alg
+ const thisBlock = {
+ ...block,
+ algorithm: str,
+ };
+ // assign new inputs
+ if(tcBlock.inputs)
+ thisBlock.inputs = levMapStrings(Object.keys(inputs), tcBlock.inputs);
+ // assign new outputs
+ if(tcBlock.outputs)
+ thisBlock.outputs = levMapStrings(Object.keys(outputs), tcBlock.outputs);
+
+ // setup the parameters (erase & create stuff)
+ const globals = copyObj(this.props.data.contents.globals);
+ // if the new alg has parameters, gen the global defaults for the params
+ if(alg.contents.parameters && Object.keys(alg.contents.parameters).length > 0)
+ globals[alg.name] = {
+ ...Object.entries(alg.contents.parameters || {})
+ .map(([pName, param]) => ({[pName]: getDefaultParameterValue(param)}))
+ .reduce((o, p) => ({...o, ...p}), {}),
+ ...this.props.data.contents.globals[alg.name],
+ };
- if(globals.hasOwnProperty(block.algorithm) &&
- [...this.props.toolchain.contents.analyzers, ...this.props.toolchain.contents.blocks]
- .filter(b => b.algorithm === block.algorithm).length <= 1)
- delete globals[block.algorithm];
+ // if the old algorithm had parameters and no other block is using that alg,
+ // delete the global param defaults for the old alg
+ if(globals.hasOwnProperty(block.algorithm) &&
+ [...this.props.toolchain.contents.analyzers, ...this.props.toolchain.contents.blocks]
+ .filter(b => b.algorithm === block.algorithm).length <= 1)
+ delete globals[block.algorithm];
- updateBlock(thisBlock, globals);
- this.setLockMap(blockName, true);
- }}
- >
- <option value=''>Algorithm...</option>
- {
- possibleAlgorithms
- .map(nb => nb.name)
- .map((str, i) =>
- <option key={i} value={str}>{ str }</option>
- )
+ updateBlock(thisBlock, globals);
+ this.setLockMap(blockName, true);
+ }}
+ >
+ <option value=''>Algorithm...</option>
+ {
+ possibleAlgorithms
+ .map(nb => nb.name)
+ .map((str, i) =>
+ <option key={i} value={str}>{ str }</option>
+ )
+ }
+ </Input>
+ </Col>
+ { !isAnalyzer &&
+ <Col sm='auto'>
+ <Button
+ color='primary'
+ disabled={block.algorithm === ''}
+ title={`Copies the algorithm & IO mappings to all unconfigured blocks with the same inputs & outputs`}
+ onClick={() => {
+ // copy the algorithm and input/output mappings to blocks
+ // that are the same except for the block name and dont have an algorithm assigned already
+ const targetBlocks = this.props.toolchain.contents.blocks
+ // not the block being copied from
+ .filter(b => b.name !== blockName)
+ // same IO
+ .filter(b => JSON.stringify(b.inputs) === JSON.stringify(tcBlock.inputs))
+ .filter(b => JSON.stringify(b.outputs) === JSON.stringify(tcBlock.outputs))
+ .map(b => b.name)
+ ;
+ //console.log(targetBlocks);
+
+ // because the target blocks have the exact same inputs/outputs as the current block
+ // we dont need to re-compute the block object that we will copy!
+ // instead, just reuse the current block object and assign it to all
+ // the target blocks.
+ // also, because all these blocks are the same algorithm as the current block
+ // we dont need to mess with the globals.
+
+ const newBlocks = copyObj(this.props.data.contents.blocks);
+ targetBlocks.forEach(bName => { newBlocks[bName] = copyObj(block); });
+ this.setContents({
+ ...this.props.data.contents,
+ blocks: newBlocks,
+ });
+ }}
+ >
+ Copy to similar blocks
+ </Button>
+ </Col>
}
- </Input>
+ </FormGroup>
</FormGroup>
{ Object.entries(alg.contents.parameters || {}).length > 0 && <h5>Parameters</h5> }
{
diff --git a/conda/js/src/components/experiment/ExperimentEditor.spec.jsx b/conda/js/src/components/experiment/ExperimentEditor.spec.jsx
index c86c65546b6a74ca69a2f8133e8380b0bd91436a..07713901d239510ff54e180c7918c4a21550262b 100644
--- a/conda/js/src/components/experiment/ExperimentEditor.spec.jsx
+++ b/conda/js/src/components/experiment/ExperimentEditor.spec.jsx
@@ -80,7 +80,7 @@ describe('<ExperimentEditor />', () => {
const tc = testTcs.find(tc => expName.includes(tc.name));
wrapper = mount(
<C
- data={getValidObj({name: expName, contents: {}}, tc, [normalBlocks, ...analyzerBlocks])}
+ data={getValidObj({name: expName, contents: {}}, tc, [...normalBlocks, ...analyzerBlocks])}
experiments={[]}
normalBlocks={normalBlocks}
analyzerBlocks={analyzerBlocks}
@@ -207,5 +207,306 @@ describe('<ExperimentEditor />', () => {
}
});
});
+
+ // tests:
+ // - assigning protocol for datasets
+ // - copy block algs
+ it('test/test/iris_advanced/1/iris', () => {
+ const expName ='test/test/iris_advanced/1/iris';
+ const saveFunc = sinon.spy();
+ const _updateFunc = (obj) => {
+ wrapper.setProps && wrapper.setProps({ data: obj });
+ };
+ const updateFunc = sinon.spy(_updateFunc);
+ const tc = testTcs.find(tc => expName.includes(tc.name));
+ wrapper = mount(
+ <C
+ data={getValidObj({name: expName, contents: {}}, tc, [...normalBlocks, ...analyzerBlocks])}
+ experiments={[]}
+ normalBlocks={normalBlocks}
+ analyzerBlocks={analyzerBlocks}
+ datasets={datasets}
+ toolchain={tc}
+ saveFunc={saveFunc}
+ environments={envs}
+ updateFunc={updateFunc}
+ />
+ );
+
+ expect(wrapper.props().data).to.have.property('name', expName);
+
+ console.log('doing dataset');
+
+ wrapper.find('div.datasets select').at(0).simulate('change', { target: { value: '{"testing_data":{"database":"iris/1","protocol":"Main","set":"training"},"training_data":{"database":"iris/1","protocol":"Main","set":"testing"}}'}});
+ expect(updateFunc.callCount).to.equal(1);
+ expect(wrapper.props().data.contents).to.have.deep.property('datasets', {
+ 'testing_data': {
+ 'database': 'iris/1',
+ 'protocol': 'Main',
+ 'set': 'training'
+ },
+ 'training_data': {
+ 'database': 'iris/1',
+ 'protocol': 'Main',
+ 'set': 'testing'
+ }
+ });
+
+ //console.log('finished dataset, doing preprocessor blocks');
+
+ wrapper.find('svg #block_pre_training').simulate('click');
+ expect(wrapper.find({ name: 'pre_training', set: 'blocks'}).find('.tcBlockBackground').prop('className')).to.include('highlighted');
+ wrapper.find('div.block0 div.algorithm select').at(0).simulate('change', { target: { value: 'test/iris_preprocessor/1'}});
+ //console.log('finished preprocessor_training, copying alg to other preprocessors');
+ expect(updateFunc.callCount).to.equal(2);
+ wrapper.find('div.block0 div.algorithm button').at(0).simulate('click');
+ expect(updateFunc.callCount).to.equal(3);
+
+ expect(wrapper.props().data.contents).to.have.deep.property('blocks', {
+ 'pre_testing': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'pre_training': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'testing_alg': {
+ 'algorithm': '',
+ 'inputs': {
+ 'lda_machine': '',
+ 'measurements': ''
+ },
+ 'outputs': {
+ 'scores': ''
+ },
+ 'parameters': {}
+ },
+ 'training_alg': {
+ 'algorithm': '',
+ 'inputs': {
+ 'measurements': '',
+ 'species': ''
+ },
+ 'outputs': {
+ 'lda_machine': ''
+ },
+ 'parameters': {}
+ }
+ });
+
+ //console.log('finished preprocessors, doing training');
+
+ wrapper.find('svg #block_training_alg').simulate('click');
+ expect(wrapper.find({ name: 'training_alg', set: 'blocks'}).find('.tcBlockBackground').prop('className')).to.include('highlighted');
+ wrapper.find('div.block0 div.algorithm select').at(0).simulate('change', { target: { value: 'test/iris_training/1'}});
+ expect(updateFunc.callCount).to.equal(4);
+ expect(wrapper.props().data.contents).to.have.deep.property('blocks', {
+ 'pre_testing': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'pre_training': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'testing_alg': {
+ 'algorithm': '',
+ 'inputs': {
+ 'lda_machine': '',
+ 'measurements': ''
+ },
+ 'outputs': {
+ 'scores': ''
+ },
+ 'parameters': {}
+ },
+ 'training_alg': {
+ 'algorithm': 'test/iris_training/1',
+ 'inputs': {
+ 'measurements': 'measurements',
+ 'species': 'species'
+ },
+ 'outputs': {
+ 'lda_machine': 'lda_machine'
+ },
+ 'parameters': {}
+ }
+ });
+
+ //console.log('finished training, doing testing');
+
+ wrapper.find('svg #block_testing_alg').simulate('click');
+ expect(wrapper.find({ name: 'testing_alg', set: 'blocks'}).find('.tcBlockBackground').prop('className')).to.include('highlighted');
+ wrapper.find('div.block0 div.algorithm select').at(0).simulate('change', { target: { value: 'test/iris_testing/1'}});
+ expect(updateFunc.callCount).to.equal(5);
+ expect(wrapper.props().data.contents).to.have.deep.property('blocks', {
+ 'pre_testing': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'pre_training': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'testing_alg': {
+ 'algorithm': 'test/iris_testing/1',
+ 'inputs': {
+ 'lda_machine': 'lda_machine',
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'scores': 'scores'
+ },
+ 'parameters': {}
+ },
+ 'training_alg': {
+ 'algorithm': 'test/iris_training/1',
+ 'inputs': {
+ 'measurements': 'measurements',
+ 'species': 'species'
+ },
+ 'outputs': {
+ 'lda_machine': 'lda_machine'
+ },
+ 'parameters': {}
+ }
+ });
+
+ //console.log('finished blocks, doing analyzer');
+
+ wrapper.find('svg #block_analyzer').simulate('click');
+ expect(wrapper.find({ name: 'analyzer', set: 'analyzers'}).find('.tcBlockBackground').prop('className')).to.include('highlighted');
+ wrapper.find('div.block0 div.algorithm select').at(0).simulate('change', { target: { value: 'test/iris_analyzer/1'}});
+ expect(updateFunc.callCount).to.equal(6);
+ expect(wrapper.props().data.contents).to.have.deep.property('analyzers', {
+ 'analyzer': {
+ 'algorithm': 'test/iris_analyzer/1',
+ 'inputs': {
+ 'scores': 'scores',
+ 'species': 'species'
+ },
+ 'parameters': {}
+ }
+ });
+
+ //console.log('finished analyzer, doing cache check');
+
+ expect(wrapper.props().data).to.be.deep.equal({
+ 'name': 'test/test/iris_advanced/1/iris',
+ 'contents': {
+ 'description': '',
+ 'analyzers': {
+ 'analyzer': {
+ 'algorithm': 'test/iris_analyzer/1',
+ 'inputs': {
+ 'scores': 'scores',
+ 'species': 'species'
+ },
+ 'parameters': {}
+ }
+ },
+ 'blocks': {
+ 'pre_testing': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'pre_training': {
+ 'algorithm': 'test/iris_preprocessor/1',
+ 'inputs': {
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'measurements': 'measurements'
+ },
+ 'parameters': {}
+ },
+ 'testing_alg': {
+ 'algorithm': 'test/iris_testing/1',
+ 'inputs': {
+ 'lda_machine': 'lda_machine',
+ 'measurements': 'measurements'
+ },
+ 'outputs': {
+ 'scores': 'scores'
+ },
+ 'parameters': {}
+ },
+ 'training_alg': {
+ 'algorithm': 'test/iris_training/1',
+ 'inputs': {
+ 'measurements': 'measurements',
+ 'species': 'species'
+ },
+ 'outputs': {
+ 'lda_machine': 'lda_machine'
+ },
+ 'parameters': {}
+ }
+ },
+ 'datasets': {
+ 'testing_data': {
+ 'database': 'iris/1',
+ 'protocol': 'Main',
+ 'set': 'training'
+ },
+ 'training_data': {
+ 'database': 'iris/1',
+ 'protocol': 'Main',
+ 'set': 'testing'
+ }
+ },
+ 'globals': {
+ 'queue': 'Default',
+ 'environment': {
+ 'name': 'Scientific Python 2.7',
+ 'version': '0.0.4'
+ }
+ }
+ }
+ });
+ });
});
});
diff --git a/conda/js/test/test_algs.json b/conda/js/test/test_algs.json
index 9d0bcd708fad5da8f4c7529b1301b6f3e0c841cf..3a41206a4fbb3e3b4a6a0e15a5af6c9493f3e135 100644
--- a/conda/js/test/test_algs.json
+++ b/conda/js/test/test_algs.json
@@ -1,4 +1,619 @@
[
+ {
+ "name": "test/iris_preprocessor/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ }
+ },
+ "name": "group",
+ "outputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ }
+ }
+ }
+ ],
+ "description": "Rounds each measurement in an Iris dataset sample to the nearest integer",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/iris_testing/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ }
+ },
+ "name": "group",
+ "outputs": {
+ "scores": {
+ "type": "system/float/1"
+ }
+ }
+ },
+ {
+ "inputs": {
+ "lda_machine": {
+ "type": "tutorial/linear_machine/1"
+ }
+ },
+ "name": "group0"
+ }
+ ],
+ "description": "",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/iris_training/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ },
+ "species": {
+ "type": "system/text/1"
+ }
+ },
+ "name": "group",
+ "outputs": {
+ "lda_machine": {
+ "type": "tutorial/linear_machine/1"
+ }
+ }
+ }
+ ],
+ "description": "",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/linear_machine_projection/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "id": {
+ "type": "system/uint64/1"
+ },
+ "image": {
+ "type": "system/array_2d_uint8/1"
+ }
+ },
+ "name": "main",
+ "outputs": {
+ "projections": {
+ "type": "system/array_2d_floats/1"
+ }
+ }
+ },
+ {
+ "inputs": {
+ "subspace": {
+ "type": "tutorial/linear_machine/1"
+ }
+ }
+ }
+ ],
+ "description": "This algorithm linearizes and accumulates images into a buffer",
+ "language": "python",
+ "splittable": true
+ }
+ },
+ {
+ "name": "test/linear_machines_scoring/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "comparison_ids": {
+ "type": "system/array_1d_uint64/1"
+ },
+ "probe_client_id": {
+ "type": "system/uint64/1"
+ },
+ "probe_id": {
+ "type": "system/uint64/1"
+ },
+ "probe_projections": {
+ "type": "system/array_2d_floats/1"
+ }
+ },
+ "name": "probes",
+ "outputs": {
+ "scores": {
+ "type": "tutorial/probe_scores/1"
+ }
+ }
+ },
+ {
+ "inputs": {
+ "template_client_id": {
+ "type": "system/uint64/1"
+ },
+ "template_id": {
+ "type": "system/uint64/1"
+ },
+ "template_projections": {
+ "type": "system/array_2d_floats/1"
+ }
+ },
+ "name": "templates"
+ }
+ ],
+ "description": "This algorithm generates comparison scores",
+ "language": "python",
+ "splittable": true
+ }
+ },
+ {
+ "name": "test/means_testing/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ }
+ },
+ "name": "group",
+ "outputs": {
+ "scores": {
+ "type": "system/float/1"
+ }
+ }
+ },
+ {
+ "inputs": {
+ "means": {
+ "type": "system/array_1d_floats/1"
+ }
+ },
+ "name": "group0"
+ }
+ ],
+ "description": "",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/means_training/1",
+ "contents": {
+ "parameters": {},
+ "uses": {},
+ "groups": [
+ {
+ "inputs": {
+ "measurements": {
+ "type": "system/array_1d_floats/1"
+ },
+ "species": {
+ "type": "system/text/1"
+ }
+ },
+ "name": "group",
+ "outputs": {
+ "means": {
+ "type": "system/array_1d_floats/1"
+ }
+ }
+ }
+ ],
+ "description": "",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/pca/1",
+ "contents": {
+ "parameters": {
+ "number-of-components": {
+ "default": 5,
+ "type": "uint32"
+ }
+ },
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+ }
+ },
+ "outputs": {
+ "subspace": {
+ "type": "tutorial/linear_machine/1"
+ }
+ }
+ }
+ ],
+ "description": "Principal Component Analysis (PCA)",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/pcatest/1",
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+ "type": "uint32"
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+ {
+ "inputs": {},
+ "name": "group"
+ }
+ ],
+ "description": "Principal Component Analysis (PCA)",
+ "language": "python",
+ "splittable": false
+ }
+ },
+ {
+ "name": "test/testing/1",
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+ "groups": [
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+ "splittable": true
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+ },
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+ "name": "test/iris_analyzer/1",
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+ },
+ "scores_distribution": {
+ "display": false,
+ "type": "plot/bar/1"
+ }
+ }
+ }
+ },
+ {
+ "name": "test/iris_analyzer_error/1",
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diff --git a/conda/js/test/test_dbs.json b/conda/js/test/test_dbs.json
index f9f6e2249181cb7167d09314d9fdfd893acb8c12..eb5d1a78d81fa681116aa0dd44e2ded8f6a8c25d 100644
--- a/conda/js/test/test_dbs.json
+++ b/conda/js/test/test_dbs.json
@@ -1,4 +1,39 @@
[
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+ "root_folder": "/not/needed",
+ "protocols": [
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+ "sets": [
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+ "measurements": "system/array_1d_floats/1",
+ "species": "system/text/1"
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+ "template": "iris",
+ "view": "Training"
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+ {
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+ "view": "Testing"
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+ "template": "iris"
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{
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diff --git a/conda/js/test/test_exps.json b/conda/js/test/test_exps.json
index aedfb7feeb8344368f9fd9e6e6645e1f95a6e0c0..3a9cf6fa96cc3e76ad13091788d1b0442d9156e4 100644
--- a/conda/js/test/test_exps.json
+++ b/conda/js/test/test_exps.json
@@ -1,4 +1,83 @@
[
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+ "contents": {
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+ "inputs": {
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+ "parameters": {}
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+ "inputs": {
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+ "measurements": "measurements"
+ },
+ "outputs": {
+ "scores": "scores"
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+ "parameters": {}
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+ "training_alg": {
+ "algorithm": "test/iris_training/1",
+ "inputs": {
+ "measurements": "measurements",
+ "species": "species"
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+ "outputs": {
+ "lda_machine": "lda_machine"
+ },
+ "parameters": {}
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+ },
+ "datasets": {
+ "testing_data": {
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+ "protocol": "Main",
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+ "protocol": "Main",
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+ "environment": {
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+ }
+ }
+ },
{
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diff --git a/conda/js/test/test_tcs.json b/conda/js/test/test_tcs.json
index 063aa9c0b556dc1a1aee0b984705bfd6d850b039..4ccebb7c004a23035c569c97fa17822ba5ea45f6 100644
--- a/conda/js/test/test_tcs.json
+++ b/conda/js/test/test_tcs.json
@@ -1,4 +1,182 @@
[
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