bump version (#518)

* create occupancy component

* occupancy working

* centering, width, flex spacing

* add is-number

* extend sort to cover string + int

* proof of concept sorted occupancy

* handle undefined occupancy entry, cleanup

* only render occupancy when colorby is cat

* cleanup unused vars

* cleanup

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.3
+current_version = 0.4.0
 
 [bumpversion:file:setup.py]
 search = version="{current_version}"

.dockerignore

@@ -0,0 +1,5 @@
+bin
+client
+dist
+docs
+server

.travis.yml

@@ -11,10 +11,11 @@ install:
   - ./bin/build-client
   - pip install -e .
   - pip install -r server/requirements-dev.txt
+  - docker build .
 script:
   - set -eo pipefail
-  - flake8 server/app/
-  - flake8 server/cli/
+  - flake8 server
+  - black --check
   - npm run --prefix client/ build
   - npm run --prefix client/ test
   - pytest -s server/test

Dockerfile

@@ -0,0 +1,10 @@
+FROM ubuntu:bionic
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+RUN apt-get update && \
+    apt-get install -y build-essential libxml2-dev python3-dev python3-pip zlib1g-dev && \
+    pip3 install cellxgene
+
+ENTRYPOINT ["cellxgene"]

README.md

@@ -8,7 +8,9 @@
 
 ## getting started
 
-You'll need **python 3.6** and **Google Chrome**. The web UI is tested on OSX and Windows using Chrome, and the python CLI is tested on OSX and Ubuntu (via WSL/Windows). It should work on other platforms, but if you run into trouble let us know (see [help](#help-and-contact) below).
+You'll need **python 3.6** and **Google Chrome**. (*Warning*: Python 3.7 is **not** supported at this time)
+The web UI is tested on OSX and Windows using Chrome, and the python CLI is tested on OSX and Ubuntu (via WSL/Windows). It should work on other platforms, but if you run into trouble let us know (see [help](#help-and-contact) below).
+
 
 To install run
 
@@ -27,6 +29,7 @@ If you want an example dataset download [this file](https://github.com/chanzucke
 ```
 cellxgene launch pbmc3k.h5ad --open
 ```
+
 You should see your web browser open with the following
 
 <img width="450" src="https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/docs/cellxgene-opening-screenshot.png" pad="50px">
@@ -35,7 +38,7 @@ You should see your web browser open with the following
 
 There are several options available, such as:
 
-- `--layout` to specify the layout as  `tsne` or `umap`
+- `--layout` to specify the layout as `tsne` or `umap`
 - `--title` to show a title on the explorer
 - `--open` to automatically open the web browser after launching (OS X only)
 
@@ -56,11 +59,11 @@ The `launch` command assumes that the data is stored in the `.h5ad` format from
 - an `obs` field has a unique identifier for every cell (you can specify which field to use with the `--obs-names` option, by default it will use the value of `data.obs_names`)
 - a `var` field has a unique identifier for every gene (you can specify which field to use with the `--var-names` option, by default it will use the value of `data.var_names`)
 - an `obsm` field contains the two-dimensional coordinates for the layout that you want to render (e.g. `X_tsne` for the `tsne` layout or `X_umap` for the `umap` layout)
-- any additional  `obs` fields will be rendered as per-cell continuous or categorical metadata by the app (e.g. `louvain` cluster assignments) 
+- any additional `obs` fields will be rendered as per-cell continuous or categorical metadata by the app (e.g. `louvain` cluster assignments)
 
 ### prepare
 
-The `prepare` command is included to help you format your data. It uses `scanpy` under the hood. This is especially useful if you are starting with raw unanalyzed data and are unfamiliar with `scanpy`. 
+The `prepare` command is included to help you format your data. It uses `scanpy` under the hood. This is especially useful if you are starting with raw unanalyzed data and are unfamiliar with `scanpy`.
 
 To prepare from an existing `.h5ad` file use
 
@@ -68,7 +71,7 @@ To prepare from an existing `.h5ad` file use
 cellxgene prepare dataset.h5ad --output=dataset-processed.h5ad
 ```
 
-This will load the input data, perform PCA and nearest neighbor calculations, compute `umap` and `tsne` layouts and `louvain` cluster assignments, and save the results in a new file called `dataset-processed.h5ad` that can be loaded using `cellxgene launch`. Data can be loaded from several formats, including `.h5ad` `.loom` and a `10-Genomics-formatted` `mtx` directory. Several options are available, including running one of the preprocessing `recipes` included with `scanpy`, which include steps like cell filtering and gene selection. 
+This will load the input data, perform PCA and nearest neighbor calculations, compute `umap` and `tsne` layouts and `louvain` cluster assignments, and save the results in a new file called `dataset-processed.h5ad` that can be loaded using `cellxgene launch`. Data can be loaded from several formats, including `.h5ad` `.loom` and a `10-Genomics-formatted` `mtx` directory. Several options are available, including running one of the preprocessing `recipes` included with `scanpy`, which include steps like cell filtering and gene selection. To learn more about the `recipes` please see the `scanpy` [documentation](https://github.com/theislab/scanpy/blob/master/scanpy/preprocessing/recipes.py).
 
 Depending on the options chosen, `prepare` can take a long time to run (a few minutes for datasets with 10-100k cells, up to an hour or more for datasets with >100k cells). If you want `prepare` to run faster we recommend using the `sparse` option and only computing the layout for `umap`, using a call like this
 
@@ -88,6 +91,12 @@ cellxgene prepare --help
 pip install cellxgene[louvain]
 ```
 
+If the aforementioned optional package installation fails, you can also install these packages directly:
+
+```
+pip install python-igraph louvain>=0.6
+```
+
 ## conda and virtual environments
 
 If you use conda and want to create a conda environment for `cellxgene` you can use the following commands
@@ -102,13 +111,28 @@ Or you can create a virtual environment by using
 
 ```
 ENV_NAME=cellxgene
-python3 -m venv ${ENV_NAME}
+python3.6 -m venv ${ENV_NAME}
 source ${ENV_NAME}/bin/activate
 pip install cellxgene
 ```
 
+## docker
+
+We have included a dockerfile to conveniently run cellxgene from docker. 
+
+1. Build the  image `docker build . -t cellxgene`
+2. Run the container and mount data `docker run -v "$PWD/example-dataset/:/data/" -p 5005:5005 cellxgene launch --host 0.0.0.0 data/pbmc3k.h5ad`
+    * You will need to use --host 0.0.0.0 to have the container listen to incoming requests from the browser
+
+
 ## FAQ
 
+<details>
+
+<summary> questions about data formatting </summary>
+  
+<hr>
+  
 > Someone sent me a directory of `10X-Genomics` data with a `mtx` file and I've never used `scanpy`, can I use `cellxgene`?
 
 Yep! This should only take a couple steps. We'll assume your data is in a folder called `data/` and you've successfully installed `cellxgene` with the `louvain` packages as described above. Just run
@@ -125,14 +149,18 @@ cellxgene launch data-processed.h5ad --layout=umap --open
 
 And your web browser should open with an interactive view of your data.
 
+<hr>
+
 > In my `prepare` command I received the following error `Warning: louvain module is not installed, no clusters will be calculated. To fix this please install cellxgene with the optional feature louvain enabled`
 
-Louvain clustering requires additional dependencies that are somewhat complex, so we don't include them by default. For now, you need to specify that you want these packages by using 
+Louvain clustering requires additional dependencies that are somewhat complex, so we don't include them by default. For now, you need to specify that you want these packages by using
 
 ```
 pip install cellxgene[louvain]
 ```
 
+<hr>
+
 > I ran `prepare` and I'm getting results that look unexpected
 
 You might want to try running one of the preprocessing recipes included with `scanpy` (read more about them [here](https://scanpy.readthedocs.io/en/latest/api/index.html#recipes)). You can specify this with the `--recipe` option, such as
@@ -143,22 +171,60 @@ cellxgene prepare data/ --output=data-processed.h5ad --recipe=zheng17
 
 It should be easy to run `prepare` then call `cellxgene launch` a few times with different settings to explore different behaviors. We may explore adding other preprocessing options in the future.
 
+<hr>
+
 > I have extra metadata that I want to add to my dataset
 
 Currently this is not supported directly, but you should be able to do this manually using `scanpy`. For example, this [notebook](https://github.com/falexwolf/fun-analyses/blob/master/tabula_muris/tabula_muris.ipynb) shows adding the contents of a `csv` file with metadata to an `anndata` object. For now, you could do this manually on your data in the same way and then save out the result before loading into `cellxgene`.
 
+<hr>
+
+> What part of the anndata objects does cellxgene pull in for visualization?
+
+- `.obs` and `.var` annotations are use to extract metadata for filtering
+- `.X` is used to display expression (histograms, scatterplot & colorscale) and to compute differential expression
+- `.obsm` is used for layout
+
+<hr>
+
+> When I start cellxgene, I get an error `Unexpected HTTP response 500, INTERNAL SERVER ERROR -- Out of range float values are not JSON compliant` in the web UI, or `Warning: JSON encoding failure - suggest trying --nan-to-num command line option` in the CLI. What can I do?
+
+At the moment, cellxgene is unable to transmit floating point NaN or Inifinty values to the web UI (due to a limitation on data serialization method in use). We expect to resolve this in a future release, but in the meantime, you can work around this issue by starting cellxgene with the `--nan-to-num` command line option, ie, `cellxgene launch data.h5ad --nan-to-num`.
+
+This option will convert all NaNs to zero, and all positive/negative infinities to the min/max of the data element within which the value was found (eg, +Infinity within an `obs` annotation will be converted to the maximum finite value in that annotation). This option will increase startup time, so we recommend only using it when the dataset contains NaN/Infinities.
+
+</details>
+
+<details>
+  
+<summary> questions about installing and building </summary>
+
+<hr>
+
 > I tried to `pip install cellxgene` and got a weird error I don't understand
 
 This may happen, especially as we work out bugs in our installation process! Please create a new [Github issue](https://github.com/chanzuckerberg/cellxgene/issues), explain what you did, and include all the error messages you saw. It'd also be super helpful if you call `pip freeze` and include the full output alongside your issue.
 
-> How are you computing and sorting differential expression results?
-
-Currently we use a [Welch's *t*-test](https://en.wikipedia.org/wiki/Welch%27s_t-test) implementation including the same variance overestimation correction as used in `scanpy`. We sort the `tscore` to identify the top N genes, and then filter to remove any that fall below a cutoff log fold change value, which can help remove spurious test results. The default threshold is `0.01` and can be changed using the option `--diffexp-lfc-cutoff`. We can explore adding support for other test types in the future.
+<hr>
 
 > I'm following the developer instructions and get an error about "missing files and directories” when trying to build the client
 
 This is likely because you do not have node and npm installed, we recommend using [nvm](https://github.com/creationix/nvm) if you're new to using these tools.
 
+</details>
+
+<details>
+
+<summary> questions about algorithms </summary>
+
+<hr>
+
+> How are you computing and sorting differential expression results?
+
+Currently we use a [Welch's _t_-test](https://en.wikipedia.org/wiki/Welch%27s_t-test) implementation including the same variance overestimation correction as used in `scanpy`. We sort the `tscore` to identify the top N genes, and then filter to remove any that fall below a cutoff log fold change value, which can help remove spurious test results. The default threshold is `0.01` and can be changed using the option `--diffexp-lfc-cutoff`. We can explore adding support for other test types in the future.
+
+</details>
+
 ## developer guide
 
 This project has made a few key design choices
@@ -194,15 +260,15 @@ pip install -e .
 
 You can start the app while developing either by calling `cellxgene` or by calling `python -m server`. We recommend using the `--debug` flag to see more output, which you can include when reporting bugs.
 
-If you have any questions about developing or contributing, come hang out with us by joining the [CZI Science Slack](https://cziscience.slack.com/messages/CCTA8DF1T) and posting in the `#cellxgene-dev` channel.
+If you have any questions about developing or contributing, come hang out with us by joining the [CZI Science Slack](https://join-cellxgene-users.herokuapp.com/) and posting in the `#cellxgene-dev` channel.
 
 ## development roadmap
 
-`cellxgene` is still very much in development, and we've love to include the community as we plan new features to work on. We are thinking about working on the following features over the next 3-12 months. If you are interested in updates, want to give feedback, want to contribute, or have ideas about other features we should work on, please [contact us](#help-and-contact) 
+`cellxgene` is still very much in development, and we've love to include the community as we plan new features to work on. We are thinking about working on the following features over the next 3-12 months. If you are interested in updates, want to give feedback, want to contribute, or have ideas about other features we should work on, please [contact us](#help-and-contact)
 
 - **Visualizaling spatial metadata** Image-based transcriptomics methods also generate large cell by gene matrices, alongside rich metadata about spatial location; we would like to render this information in `cellxgene`
-- **Visualizing trajectories** Trajectory analyses infer progression along some ordering or pseudotime; we would like `cellxgene ` to render the results of these analyses when they have been performed
-- **Deploy to web**  Many projects release public data browser websites  alongside their publicatons; we would like to make it easy for anyone to deploy `cellxgene` to a custom URL with their own dataset that they own and operate
+- **Visualizing trajectories** Trajectory analyses infer progression along some ordering or pseudotime; we would like `cellxgene` to render the results of these analyses when they have been performed
+- **Deploy to web** Many projects release public data browser websites alongside their publicatons; we would like to make it easy for anyone to deploy `cellxgene` to a custom URL with their own dataset that they own and operate
 - **HCA Integration** The [Human Cell Atlas](https://humancellatlas.org) is generating a large corpus of single-cell expression data and will make it available through the Data Coordination Platform; we would like `cellxgene` to be one of several different portals for browsing these data
 
 ## contributing
@@ -215,13 +281,13 @@ We've been heavily inspired by several other related single-cell visualization p
 
 We were inspired by Mike Bostock and the [crossfilter](https://github.com/crossfilter) team for the design of our filtering implementation.
 
-We have been working closely with the [`scanpy`](https://github.com/theislab/scanpy) team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset. 
+We have been working closely with the [`scanpy`](https://github.com/theislab/scanpy) team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset.
 
 We are eager to explore integrations with other computational backends such as [`Seurat`](https://github.com/satijalab/seurat) or [`Bioconductor`](https://github.com/Bioconductor)
 
 ## help and contact
 
-Have questions, suggestions, or comments? You can come hang out with us by joining the [CZI Science Slack](https://cziscience.slack.com/messages/CCTA8DF1T) and posting in the `#cellxgene-users` channel. As mentioned above, please submit any feature requests or bugs as [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). We'd love to hear from you!
+Have questions, suggestions, or comments? You can come hang out with us by joining the [CZI Science Slack](https://join-cellxgene-users.herokuapp.com/) and posting in the `#cellxgene-users` channel. As mentioned above, please submit any feature requests or bugs as [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). We'd love to hear from you!
 
 ## reuse
 

client/__tests__/util/stateManager/summarizeAnnotations.test.js

@@ -0,0 +1,192 @@
+import summarizeAnnotations from "../../../src/util/stateManager/summarizeAnnotations";
+
+describe("summarizeAnnotations", () => {
+  const schema = {
+    annotations: {
+      obs: [
+        { name: "name", type: "string" },
+        { name: "nameString", type: "string" },
+        { name: "nameBoolean", type: "boolean" },
+        { name: "nameFloat32", type: "float32" },
+        { name: "nameInt32", type: "int32" },
+        {
+          name: "nameCategorical",
+          type: "categorical",
+          categories: [true, false, 1, 0, 0.00001, 4383.4833, "test", "", "0"]
+        }
+      ],
+      var: [{ name: "name", type: "string" }]
+    }
+  };
+
+  test("empty test", () => {
+    const summary = summarizeAnnotations(schema, [], []);
+    expect(summary).toEqual(
+      expect.objectContaining({
+        obs: {
+          nameString: {
+            categorical: true,
+            categories: [],
+            categoryCounts: new Map(),
+            numCategories: 0
+          },
+          nameBoolean: {
+            categorical: true,
+            categories: [],
+            categoryCounts: new Map(),
+            numCategories: 0
+          },
+          nameFloat32: {
+            categorical: false,
+            range: {
+              max: Number.NEGATIVE_INFINITY,
+              min: Number.POSITIVE_INFINITY
+            }
+          },
+          nameInt32: {
+            categorical: false,
+            range: {
+              max: Number.NEGATIVE_INFINITY,
+              min: Number.POSITIVE_INFINITY
+            }
+          },
+          nameCategorical: {
+            categorical: true,
+            categories: [],
+            categoryCounts: new Map(),
+            numCategories: 0
+          }
+        },
+        var: {}
+      })
+    );
+  });
+
+  test("simple test", () => {
+    const obsAnnotations = [
+      {
+        __index__: 0,
+        name: "n1",
+        nameString: "hi",
+        nameBoolean: true,
+        nameFloat32: 39.3,
+        nameInt32: 99,
+        nameCategorical: 1
+      }
+    ];
+    const varAnnotations = [];
+
+    const summary = summarizeAnnotations(
+      schema,
+      obsAnnotations,
+      varAnnotations
+    );
+
+    expect(summary).toEqual(
+      expect.objectContaining({
+        obs: {
+          nameString: {
+            categorical: true,
+            categories: ["hi"],
+            categoryCounts: new Map([["hi", 1]]),
+            numCategories: 1
+          },
+          nameBoolean: {
+            categorical: true,
+            categories: [true],
+            categoryCounts: new Map([[true, 1]]),
+            numCategories: 1
+          },
+          nameFloat32: {
+            categorical: false,
+            range: { min: 39.3, max: 39.3 }
+          },
+          nameInt32: {
+            categorical: false,
+            range: { min: 99, max: 99 }
+          },
+          nameCategorical: {
+            categorical: true,
+            categories: [1],
+            categoryCounts: new Map([[1, 1]]),
+            numCategories: 1
+          }
+        },
+        var: {}
+      })
+    );
+  });
+
+  test("multi test", () => {
+    const obsAnnotations = [
+      {
+        __index__: 0,
+        name: "n0",
+        nameString: "hi",
+        nameBoolean: false,
+        nameFloat32: 39.3,
+        nameInt32: 99,
+        nameCategorical: 1
+      },
+      {
+        __index__: 1,
+        name: "n1",
+        nameString: "hi",
+        nameBoolean: true,
+        nameFloat32: 39.3,
+        nameInt32: 99,
+        nameCategorical: false
+      },
+      {
+        __index__: 2,
+        name: "n2",
+        nameString: "bye",
+        nameBoolean: true,
+        nameFloat32: 0,
+        nameInt32: 99,
+        nameCategorical: "0"
+      }
+    ];
+    const varAnnotations = [];
+
+    const summary = summarizeAnnotations(
+      schema,
+      obsAnnotations,
+      varAnnotations
+    );
+
+    expect(summary).toMatchObject(
+      expect.objectContaining({
+        obs: {
+          nameString: {
+            categorical: true,
+            categories: expect.arrayContaining(["hi", "bye"]),
+            categoryCounts: new Map([["hi", 2], ["bye", 1]]),
+            numCategories: 2
+          },
+          nameBoolean: {
+            categorical: true,
+            categories: expect.arrayContaining([true, false]),
+            categoryCounts: new Map([[true, 2], [false, 1]]),
+            numCategories: 2
+          },
+          nameFloat32: {
+            categorical: false,
+            range: { min: 0, max: 39.3 }
+          },
+          nameInt32: {
+            categorical: false,
+            range: { min: 99, max: 99 }
+          },
+          nameCategorical: {
+            categorical: true,
+            categories: expect.arrayContaining([1, false, "0"]),
+            categoryCounts: new Map([[1, 1], [false, 1], ["0", 1]]),
+            numCategories: 3
+          }
+        },
+        var: {}
+      })
+    );
+  });
+});

client/__tests__/util/stateManager/worldUtil.test.js

@@ -0,0 +1,45 @@
+import {
+  countCategoryValues2D,
+  clearCaches
+} from "../../../src/util/stateManager/worldUtil";
+
+describe("WorldUtil cache management", () => {
+  test("empty", () => {
+    const count = countCategoryValues2D("a", "b", []);
+    expect(count).toMatchObject(new Map());
+  });
+
+  test("simple couts", () => {
+    const rows = [{ a: 0, b: false }, { a: 0, b: true }, { a: 1, b: false }];
+    const count = countCategoryValues2D("a", "b", rows);
+    expect(count).toMatchObject(
+      new Map([
+        [0, new Map([[true, 1], [false, 1]])],
+        [1, new Map([[false, 1]])]
+      ])
+    );
+  });
+
+  test("memo cache clear", () => {
+    clearCaches();
+    const row1 = [];
+    const row2 = [{ a: 0, b: false }, { a: 0, b: true }, { a: 1, b: false }];
+    const count1 = countCategoryValues2D("a", "b", row1);
+    const count2 = countCategoryValues2D("a", "b", row1);
+    const count3 = countCategoryValues2D("a", "b", []);
+    const count4 = countCategoryValues2D("a", "b", row2);
+
+    clearCaches();
+    const count10 = countCategoryValues2D("a", "b", row1);
+    const count11 = countCategoryValues2D("a", "b", row2);
+
+    expect(count1).toEqual(count2);
+    expect(count1).toEqual(count3);
+    expect(count1).toEqual(count10);
+    expect(count1).not.toBe(count3);
+    expect(count1).not.toBe(count10);
+
+    expect(count4).toEqual(count11);
+    expect(count4).not.toBe(count11);
+  });
+});

client/package-lock.json

@@ -1,6 +1,6 @@
 {
   "name": "cellxgene",
-  "version": "0.2.3",
+  "version": "0.4.0",
   "lockfileVersion": 1,
   "requires": true,
   "dependencies": {
@@ -2194,7 +2194,7 @@
     },
     "babel-plugin-syntax-object-rest-spread": {
       "version": "6.13.0",
-      "resolved": "http://registry.npmjs.org/babel-plugin-syntax-object-rest-spread/-/babel-plugin-syntax-object-rest-spread-6.13.0.tgz",
+      "resolved": "https://registry.npmjs.org/babel-plugin-syntax-object-rest-spread/-/babel-plugin-syntax-object-rest-spread-6.13.0.tgz",
       "integrity": "sha1-/WU28rzhODb/o6VFjEkDpZe7O/U=",
       "dev": true
     },
@@ -2627,7 +2627,7 @@
     },
     "browserify-aes": {
       "version": "1.2.0",
-      "resolved": "http://registry.npmjs.org/browserify-aes/-/browserify-aes-1.2.0.tgz",
+      "resolved": "https://registry.npmjs.org/browserify-aes/-/browserify-aes-1.2.0.tgz",
       "integrity": "sha512-+7CHXqGuspUn/Sl5aO7Ea0xWGAtETPXNSAjHo48JfLdPWcMng33Xe4znFvQweqc/uzk5zSOI3H52CYnjCfb5hA==",
       "dev": true,
       "requires": {
@@ -2664,7 +2664,7 @@
     },
     "browserify-rsa": {
       "version": "4.0.1",
-      "resolved": "http://registry.npmjs.org/browserify-rsa/-/browserify-rsa-4.0.1.tgz",
+      "resolved": "https://registry.npmjs.org/browserify-rsa/-/browserify-rsa-4.0.1.tgz",
       "integrity": "sha1-IeCr+vbyApzy+vsTNWenAdQTVSQ=",
       "dev": true,
       "requires": {
@@ -2718,7 +2718,7 @@
     },
     "buffer": {
       "version": "4.9.1",
-      "resolved": "http://registry.npmjs.org/buffer/-/buffer-4.9.1.tgz",
+      "resolved": "https://registry.npmjs.org/buffer/-/buffer-4.9.1.tgz",
       "integrity": "sha1-bRu2AbB6TvztlwlBMgkwJ8lbwpg=",
       "dev": true,
       "requires": {
@@ -3394,7 +3394,7 @@
     },
     "create-hash": {
       "version": "1.2.0",
-      "resolved": "http://registry.npmjs.org/create-hash/-/create-hash-1.2.0.tgz",
+      "resolved": "https://registry.npmjs.org/create-hash/-/create-hash-1.2.0.tgz",
       "integrity": "sha512-z00bCGNHDG8mHAkP7CtT1qVu+bFQUPjYq/4Iv3C3kWjTFV10zIjfSoeqXo9Asws8gwSHDGj/hl2u4OGIjapeCg==",
       "dev": true,
       "requires": {
@@ -3407,7 +3407,7 @@
     },
     "create-hmac": {
       "version": "1.1.7",
-      "resolved": "http://registry.npmjs.org/create-hmac/-/create-hmac-1.1.7.tgz",
+      "resolved": "https://registry.npmjs.org/create-hmac/-/create-hmac-1.1.7.tgz",
       "integrity": "sha512-MJG9liiZ+ogc4TzUwuvbER1JRdgvUFSB5+VR/g5h82fGaIRWMWddtKBHi7/sVhfjQZ6SehlyhvQYrcYkaUIpLg==",
       "dev": true,
       "requires": {
@@ -4090,7 +4090,7 @@
     },
     "diffie-hellman": {
       "version": "5.0.3",
-      "resolved": "http://registry.npmjs.org/diffie-hellman/-/diffie-hellman-5.0.3.tgz",
+      "resolved": "https://registry.npmjs.org/diffie-hellman/-/diffie-hellman-5.0.3.tgz",
       "integrity": "sha512-kqag/Nl+f3GwyK25fhUMYj81BUOrZ9IuJsjIcDE5icNM9FJHAVm3VcUDxdLPoQtTuUylWm6ZIknYJwwaPxsUzg==",
       "dev": true,
       "requires": {
@@ -4705,7 +4705,7 @@
         },
         "load-json-file": {
           "version": "2.0.0",
-          "resolved": "http://registry.npmjs.org/load-json-file/-/load-json-file-2.0.0.tgz",
+          "resolved": "https://registry.npmjs.org/load-json-file/-/load-json-file-2.0.0.tgz",
           "integrity": "sha1-eUfkIUmvgNaWy/eXvKq8/h/inKg=",
           "dev": true,
           "requires": {
@@ -4891,7 +4891,7 @@
     },
     "events": {
       "version": "1.1.1",
-      "resolved": "http://registry.npmjs.org/events/-/events-1.1.1.tgz",
+      "resolved": "https://registry.npmjs.org/events/-/events-1.1.1.tgz",
       "integrity": "sha1-nr23Y1rQmccNzEwqH1AEKI6L2SQ="
     },
     "evp_bytestokey": {
@@ -5202,11 +5202,22 @@
         "randomatic": "^3.0.0",
         "repeat-element": "^1.1.2",
         "repeat-string": "^1.5.2"
+      },
+      "dependencies": {
+        "is-number": {
+          "version": "2.1.0",
+          "resolved": "https://registry.npmjs.org/is-number/-/is-number-2.1.0.tgz",
+          "integrity": "sha1-Afy7s5NGOlSPL0ZszhbezknbkI8=",
+          "dev": true,
+          "requires": {
+            "kind-of": "^3.0.2"
+          }
+        }
       }
     },
     "finalhandler": {
       "version": "1.1.1",
-      "resolved": "http://registry.npmjs.org/finalhandler/-/finalhandler-1.1.1.tgz",
+      "resolved": "https://registry.npmjs.org/finalhandler/-/finalhandler-1.1.1.tgz",
       "integrity": "sha512-Y1GUDo39ez4aHAw7MysnUD5JzYX+WaIj8I57kO3aEPT1fFRL4sr7mjei97FgnwhAyyzRYmQZaTHb2+9uZ1dPtg==",
       "dev": true,
       "requires": {
@@ -5922,6 +5933,11 @@
       "integrity": "sha1-GwqzvVU7Kg1jmdKcDj6gslIHgyc=",
       "dev": true
     },
+    "fuzzysort": {
+      "version": "1.1.4",
+      "resolved": "https://registry.npmjs.org/fuzzysort/-/fuzzysort-1.1.4.tgz",
+      "integrity": "sha512-JzK/lHjVZ6joAg3OnCjylwYXYVjRiwTY6Yb25LvfpJHK8bjisfnZJ5bY8aVWwTwCXgxPNgLAtmHL+Hs5q1ddLQ=="
+    },
     "get-caller-file": {
       "version": "1.0.3",
       "resolved": "https://registry.npmjs.org/get-caller-file/-/get-caller-file-1.0.3.tgz",
@@ -5936,7 +5952,7 @@
     },
     "get-stream": {
       "version": "3.0.0",
-      "resolved": "http://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz",
+      "resolved": "https://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz",
       "integrity": "sha1-jpQ9E1jcN1VQVOy+LtsFqhdO3hQ=",
       "dev": true
     },
@@ -6323,7 +6339,7 @@
     },
     "html-webpack-plugin": {
       "version": "3.2.0",
-      "resolved": "http://registry.npmjs.org/html-webpack-plugin/-/html-webpack-plugin-3.2.0.tgz",
+      "resolved": "https://registry.npmjs.org/html-webpack-plugin/-/html-webpack-plugin-3.2.0.tgz",
       "integrity": "sha1-sBq71yOsqqeze2r0SS69oD2d03s=",
       "dev": true,
       "requires": {
@@ -6399,7 +6415,7 @@
     },
     "http-errors": {
       "version": "1.6.3",
-      "resolved": "http://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz",
+      "resolved": "https://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz",
       "integrity": "sha1-i1VoC7S+KDoLW/TqLjhYC+HZMg0=",
       "dev": true,
       "requires": {
@@ -6622,7 +6638,7 @@
     },
     "is-builtin-module": {
       "version": "1.0.0",
-      "resolved": "http://registry.npmjs.org/is-builtin-module/-/is-builtin-module-1.0.0.tgz",
+      "resolved": "https://registry.npmjs.org/is-builtin-module/-/is-builtin-module-1.0.0.tgz",
       "integrity": "sha1-VAVy0096wxGfj3bDDLwbHgN6/74=",
       "dev": true,
       "requires": {
@@ -6752,13 +6768,9 @@
       "dev": true
     },
     "is-number": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/is-number/-/is-number-2.1.0.tgz",
-      "integrity": "sha1-Afy7s5NGOlSPL0ZszhbezknbkI8=",
-      "dev": true,
-      "requires": {
-        "kind-of": "^3.0.2"
-      }
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz",
+      "integrity": "sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng=="
     },
     "is-obj": {
       "version": "1.0.1",
@@ -7629,7 +7641,7 @@
     },
     "json5": {
       "version": "0.5.1",
-      "resolved": "http://registry.npmjs.org/json5/-/json5-0.5.1.tgz",
+      "resolved": "https://registry.npmjs.org/json5/-/json5-0.5.1.tgz",
       "integrity": "sha1-Hq3nrMASA0rYTiOWdn6tn6VJWCE=",
       "dev": true
     },
@@ -7719,7 +7731,7 @@
     },
     "load-json-file": {
       "version": "1.1.0",
-      "resolved": "http://registry.npmjs.org/load-json-file/-/load-json-file-1.1.0.tgz",
+      "resolved": "https://registry.npmjs.org/load-json-file/-/load-json-file-1.1.0.tgz",
       "integrity": "sha1-lWkFcI1YtLq0wiYbBPWfMcmTdMA=",
       "dev": true,
       "requires": {
@@ -8043,7 +8055,7 @@
       "dependencies": {
         "minimist": {
           "version": "1.2.0",
-          "resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+          "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
           "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
           "dev": true
         }
@@ -8168,7 +8180,7 @@
     },
     "minimist": {
       "version": "0.0.8",
-      "resolved": "http://registry.npmjs.org/minimist/-/minimist-0.0.8.tgz",
+      "resolved": "https://registry.npmjs.org/minimist/-/minimist-0.0.8.tgz",
       "integrity": "sha1-hX/Kv8M5fSYluCKCYuhqp6ARsF0=",
       "dev": true
     },
@@ -8213,7 +8225,7 @@
     },
     "mkdirp": {
       "version": "0.5.1",
-      "resolved": "http://registry.npmjs.org/mkdirp/-/mkdirp-0.5.1.tgz",
+      "resolved": "https://registry.npmjs.org/mkdirp/-/mkdirp-0.5.1.tgz",
       "integrity": "sha1-MAV0OOrGz3+MR2fzhkjWaX11yQM=",
       "dev": true,
       "requires": {
@@ -9939,7 +9951,7 @@
     },
     "parse-asn1": {
       "version": "5.1.1",
-      "resolved": "http://registry.npmjs.org/parse-asn1/-/parse-asn1-5.1.1.tgz",
+      "resolved": "https://registry.npmjs.org/parse-asn1/-/parse-asn1-5.1.1.tgz",
       "integrity": "sha512-KPx7flKXg775zZpnp9SxJlz00gTd4BmJ2yJufSc44gMCRrRQ7NSzAcSJQfifuOLgW6bEi+ftrALtsgALeB2Adw==",
       "dev": true,
       "requires": {
@@ -10647,7 +10659,7 @@
     },
     "readable-stream": {
       "version": "2.3.6",
-      "resolved": "http://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz",
+      "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz",
       "integrity": "sha512-tQtKA9WIAhBF3+VLAseyMqZeBjW0AHJoxOtYqSUZNJxauErmLbVm2FW1y+J/YA9dUrAC39ITejlZWhVIwawkKw==",
       "dev": true,
       "requires": {
@@ -11669,7 +11681,7 @@
         },
         "minimist": {
           "version": "1.2.0",
-          "resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+          "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
           "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
           "dev": true
         },
@@ -11856,7 +11868,7 @@
     },
     "sha.js": {
       "version": "2.4.11",
-      "resolved": "http://registry.npmjs.org/sha.js/-/sha.js-2.4.11.tgz",
+      "resolved": "https://registry.npmjs.org/sha.js/-/sha.js-2.4.11.tgz",
       "integrity": "sha512-QMEp5B7cftE7APOjk5Y6xgrbWu+WkLVQwk8JNjZ8nKRciZaByEW6MubieAiToS7+dwvrjGhH8jRXz3MVd0AYqQ==",
       "dev": true,
       "requires": {
@@ -12310,7 +12322,7 @@
     },
     "strip-ansi": {
       "version": "3.0.1",
-      "resolved": "http://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
       "integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=",
       "dev": true,
       "requires": {
@@ -12546,7 +12558,7 @@
     },
     "through": {
       "version": "2.3.8",
-      "resolved": "http://registry.npmjs.org/through/-/through-2.3.8.tgz",
+      "resolved": "https://registry.npmjs.org/through/-/through-2.3.8.tgz",
       "integrity": "sha1-DdTJ/6q8NXlgsbckEV1+Doai4fU=",
       "dev": true
     },
@@ -13202,7 +13214,7 @@
       "dependencies": {
         "minimist": {
           "version": "1.2.0",
-          "resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+          "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
           "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
           "dev": true
         }
@@ -13865,7 +13877,7 @@
     },
     "wrap-ansi": {
       "version": "2.1.0",
-      "resolved": "http://registry.npmjs.org/wrap-ansi/-/wrap-ansi-2.1.0.tgz",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-2.1.0.tgz",
       "integrity": "sha1-2Pw9KE3QV5T+hJc8rs3Rz4JP3YU=",
       "dev": true,
       "requires": {
@@ -13973,7 +13985,7 @@
     },
     "yargs": {
       "version": "11.1.0",
-      "resolved": "http://registry.npmjs.org/yargs/-/yargs-11.1.0.tgz",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-11.1.0.tgz",
       "integrity": "sha512-NwW69J42EsCSanF8kyn5upxvjp5ds+t3+udGBeTbFnERA+lF541DDpMawzo4z6W/QrzNM18D+BPMiOBibnFV5A==",
       "dev": true,
       "requires": {

client/package.json

@@ -1,6 +1,6 @@
 {
   "name": "cellxgene",
-  "version": "0.2.3",
+  "version": "0.4.0",
   "license": "MIT",
   "description": "cellxgene is a web application for the interactive exploration of single cell sequence data.",
   "repository": "https://github.com/chanzuckerberg/cellxgene",
@@ -34,8 +34,10 @@
     "d3": "^4.10.0",
     "d3-scale-chromatic": "^1.3.0",
     "font-color-contrast": "^1.0.3",
+    "fuzzysort": "^1.1.4",
     "gl-mat4": "^1.1.4",
     "gl-matrix": "^2.7.1",
+    "is-number": "^7.0.0",
     "key-pressed": "0.0.1",
     "lodash": "^4.17.4",
     "memoize-one": "^4.0.0",

client/src/actions/index.js

@@ -24,7 +24,7 @@ const doInitialDataLoad = () =>
         "config",
         "schema",
         "annotations/obs",
-        "annotations/var",
+        "annotations/var?annotation-name=name",
         "layout/obs"
       ])
         .map(r => `${globals.API.prefix}${globals.API.version}${r}`)

client/src/components/categorical/category.js

@@ -6,7 +6,7 @@ import { Button, Tooltip } from "@blueprintjs/core";
 
 import * as globals from "../../globals";
 import Value from "./value";
-import alphabeticallySortedValues from "./util";
+import sortedCategoryValues from "./util";
 
 @connect(state => ({
   colorAccessor: state.controls.colorAccessor,
@@ -25,24 +25,22 @@ class Category extends React.Component {
     const { categoricalSelectionState, metadataField } = this.props;
     const cat = categoricalSelectionState[metadataField];
     const categoryCount = {
-      // total number of options in this category
-      totalOptionCount: cat.numOptions,
+      // total number of categories in this dimension
+      totalCatCount: cat.numCategories,
       // number of selected options in this category
-      selectedOptionCount: _.reduce(
-        cat.optionSelected,
+      selectedCatCount: _.reduce(
+        cat.categorySelected,
         (res, cond) => (cond ? res + 1 : res),
         0
       )
     };
-    if (categoryCount.selectedOptionCount === categoryCount.totalOptionCount) {
+    if (categoryCount.selectedCatCount === categoryCount.totalCatCount) {
       /* everything is on, so not indeterminate */
       this.checkbox.indeterminate = false;
-    } else if (categoryCount.selectedOptionCount === 0) {
+    } else if (categoryCount.selectedCatCount === 0) {
       /* nothing is on, so no */
       this.checkbox.indeterminate = false;
-    } else if (
-      categoryCount.selectedOptionCount < categoryCount.totalOptionCount
-    ) {
+    } else if (categoryCount.selectedCatCount < categoryCount.totalCatCount) {
       /* to be explicit... */
       this.checkbox.indeterminate = true;
     }
@@ -88,12 +86,13 @@ class Category extends React.Component {
     const { categoricalSelectionState, metadataField } = this.props;
 
     const cat = categoricalSelectionState[metadataField];
-    const optTuples = alphabeticallySortedValues([...cat.optionIndex]);
+    const optTuples = sortedCategoryValues([...cat.categoryIndices]);
     return _.map(optTuples, (tuple, i) => (
       <Value
+        optTuples={optTuples}
         key={tuple[1]}
         metadataField={metadataField}
-        optionIndex={tuple[1]}
+        categoryIndex={tuple[1]}
         i={i}
       />
     ));

client/src/components/categorical/occupancy.js

@@ -0,0 +1,73 @@
+// jshint esversion: 6
+import React from "react";
+import _ from "lodash";
+import { connect } from "react-redux";
+import * as d3 from "d3";
+
+@connect()
+class Occupancy extends React.Component {
+  render() {
+    const {
+      occupancy,
+      colorScale,
+      categoricalSelectionState,
+      colorAccessor,
+      schema
+    } = this.props;
+    const width = 100;
+    const height = 11;
+
+    const categories = _.filter(schema.annotations.obs, {
+      name: colorAccessor
+    })[0].categories;
+
+    const x = d3
+      .scaleLinear()
+      /* get all the keys d[1] as an array, then find the sum */
+      .domain([0, d3.sum(Array.from(occupancy, d => d[1]))])
+      .range([0, width]);
+
+    let currentOffset = 0;
+
+    const stacks = categoricalSelectionState[colorAccessor].categoryValues.map(
+      d => {
+        const o = occupancy.get(d);
+
+        const scaledValue = x(o);
+
+        const stackItem = {
+          key: d,
+          value: o || 0,
+          rectWidth: o ? scaledValue : 0,
+          offset: currentOffset,
+          fill: o ? colorScale(categories.indexOf(d)) : "rgb(255,255,255)"
+        };
+        currentOffset += o ? scaledValue : 0;
+        return stackItem;
+      }
+    );
+
+    return (
+      <svg
+        style={{
+          marginRight: 5,
+          width,
+          height
+        }}
+      >
+        {stacks.map(d => (
+          <rect
+            key={d.key}
+            width={d.rectWidth}
+            height={height}
+            x={d.offset}
+            title={d.metadataField}
+            fill={d.fill}
+          />
+        ))}
+      </svg>
+    );
+  }
+}
+
+export default Occupancy;

client/src/components/categorical/util.js

@@ -3,9 +3,33 @@
 // values is [ [optVal, optIdx], ...]
 // index is range array
 // return sorted index
-export default values =>
-  values.sort((a, b) => {
+
+import isNumber from "is-number";
+import _ from "lodash";
+
+const sortedCategoryValues = values => {
+  /* this sort could be memoized for perf */
+
+  const strings = [];
+  const ints = [];
+
+  _.forEach(values, v => {
+    if (isNumber(v[0])) {
+      ints.push(v);
+    } else {
+      strings.push(v);
+    }
+  });
+
+  strings.sort((a, b) => {
     const textA = String(a[0]).toUpperCase();
     const textB = String(b[0]).toUpperCase();
     return textA < textB ? -1 : textA > textB ? 1 : 0;
   });
+
+  ints.sort((a, b) => +a[0] - +b[0]);
+
+  return ints.concat(strings);
+};
+
+export default sortedCategoryValues;

client/src/components/categorical/value.js

@@ -2,29 +2,33 @@
 import { connect } from "react-redux";
 import React from "react";
 import _ from "lodash";
+import Occupancy from "./occupancy";
+import { countCategoryValues2D } from "../../util/stateManager/worldUtil";
+import * as globals from "../../globals";
 
 @connect(state => ({
   categoricalSelectionState: state.controls.categoricalSelectionState,
   colorScale: state.controls.colorScale,
   colorAccessor: state.controls.colorAccessor,
-  schema: _.get(state.controls.world, "schema", null)
+  schema: _.get(state.controls.world, "schema", null),
+  world: state.controls.world
 }))
 class CategoryValue extends React.Component {
   toggleOff() {
-    const { dispatch, metadataField, optionIndex } = this.props;
+    const { dispatch, metadataField, categoryIndex } = this.props;
     dispatch({
       type: "categorical metadata filter deselect",
       metadataField,
-      optionIndex
+      categoryIndex
     });
   }
 
   toggleOn() {
-    const { dispatch, metadataField, optionIndex } = this.props;
+    const { dispatch, metadataField, categoryIndex } = this.props;
     dispatch({
       type: "categorical metadata filter select",
       metadataField,
-      optionIndex
+      categoryIndex
     });
   }
 
@@ -32,31 +36,43 @@ class CategoryValue extends React.Component {
     const {
       categoricalSelectionState,
       metadataField,
-      optionIndex,
+      categoryIndex,
       colorAccessor,
       colorScale,
       i,
-      schema
+      schema,
+      world
     } = this.props;
 
     if (!categoricalSelectionState) return null;
 
     const category = categoricalSelectionState[metadataField];
-    const selected = category.optionSelected[optionIndex];
-    const count = category.optionCount[optionIndex];
-    const value = category.optionValue[optionIndex];
-    const displayString = String(category.optionValue[optionIndex]).valueOf();
+    const selected = category.categorySelected[categoryIndex];
+    const count = category.categoryCounts[categoryIndex];
+    const value = category.categoryValues[categoryIndex];
+    const displayString = String(
+      category.categoryValues[categoryIndex]
+    ).valueOf();
 
     /* this is the color scale, so add swatches below */
-    const c = metadataField === colorAccessor;
+    const isColorBy = metadataField === colorAccessor;
     let categories = null;
+    let occupancy = null;
 
-    if (c && schema) {
+    if (isColorBy && schema) {
       categories = _.filter(schema.annotations.obs, {
         name: colorAccessor
       })[0].categories;
     }
 
+    if (colorAccessor && !isColorBy) {
+      occupancy = countCategoryValues2D(
+        metadataField,
+        colorAccessor,
+        world.obsAnnotations
+      );
+    }
+
     return (
       <div
         key={i}
@@ -70,7 +86,10 @@ class CategoryValue extends React.Component {
           style={{
             margin: 0,
             padding: 0,
-            userSelect: "none"
+            userSelect: "none",
+            width: globals.leftSidebarWidth - 130,
+            display: "flex",
+            justifyContent: "space-between"
           }}
         >
           <label className="bp3-control bp3-checkbox">
@@ -84,6 +103,18 @@ class CategoryValue extends React.Component {
             <span className="bp3-control-indicator" />
             {displayString}
           </label>
+          <span style={{ flexShrink: 0 }}>
+            {colorAccessor &&
+            !isColorBy &&
+            categoricalSelectionState[colorAccessor] ? (
+              <Occupancy
+                occupancy={occupancy.get(
+                  category.categoryValues[categoryIndex]
+                )}
+                {...this.props}
+              />
+            ) : null}
+          </span>
         </div>
         <span>
           <span>{count}</span>
@@ -93,7 +124,7 @@ class CategoryValue extends React.Component {
               width: 11,
               height: 11,
               backgroundColor:
-                c && categories
+                isColorBy && categories
                   ? colorScale(categories.indexOf(value))
                   : "inherit"
             }}

client/src/components/geneExpression/index.js

@@ -4,14 +4,51 @@
 import React from "react";
 import _ from "lodash";
 import * as d3 from "d3";
+import fuzzysort from "fuzzysort";
+
 import { connect } from "react-redux";
-import { Button, Tooltip } from "@blueprintjs/core";
+import { MenuItem } from "@blueprintjs/core";
+import { Suggest } from "@blueprintjs/select";
 import HistogramBrush from "../brushableHistogram";
 import * as globals from "../../globals";
 import actions from "../../actions";
 import { postUserErrorToast } from "../framework/toasters";
 import ExpressionButtons from "./expressionButtons";
 
+const renderGene = (fuzzySortResult, { handleClick, modifiers, query }) => {
+  if (!modifiers.matchesPredicate) {
+    return null;
+  }
+  /* the fuzzysort wraps the object with other properties, like a score */
+  const gene = fuzzySortResult.obj;
+  const text = gene.name;
+
+  return (
+    <MenuItem
+      active={modifiers.active}
+      disabled={modifiers.disabled}
+      // Use of annotations in this way is incorrect and dataset specific.
+      // See https://github.com/chanzuckerberg/cellxgene/issues/483
+      // label={gene.n_counts}
+      key={gene.name}
+      onClick={g => {
+        /* this fires when user clicks a menu item */
+        handleClick(g);
+      }}
+      text={text}
+    />
+  );
+};
+
+const filterGenes = (query, genes) => {
+  /* fires on load, once, and then for each character typed into the input */
+  return fuzzysort.go(query, genes, {
+    key: "name",
+    limit: 5,
+    threshold: -10000 // don't return bad results
+  });
+};
+
 @connect(state => {
   const metadata = _.get(state.controls.world, "obsAnnotations", null);
   const ranges = _.get(state.controls.world, "summary.obs", null);
@@ -29,23 +66,9 @@ import ExpressionButtons from "./expressionButtons";
   };
 })
 class GeneExpression extends React.Component {
-  constructor(props) {
-    super(props);
-    this.state = {
-      gene: ""
-    };
-  }
-
-  keyPress(e) {
-    if (e.keyCode === 13) {
-      this.handleClick();
-    }
-  }
-
-  handleClick() {
+  handleClick(g) {
     const { world, dispatch, userDefinedGenes } = this.props;
-    const { gene } = this.state;
-
+    const gene = g.target;
     if (userDefinedGenes.indexOf(gene) !== -1) {
       postUserErrorToast("That gene already exists");
     } else if (userDefinedGenes.length > 15) {
@@ -60,13 +83,11 @@ class GeneExpression extends React.Component {
         type: "user defined gene",
         data: gene
       });
-      this.setState({ gene: "" });
     }
   }
 
   render() {
     const { world, userDefinedGenes, differential } = this.props;
-    const { gene } = this.state;
 
     return (
       <div>
@@ -87,27 +108,27 @@ class GeneExpression extends React.Component {
             style={{ padding: globals.leftSidebarSectionPadding }}
             className="bp3-control-group"
           >
-            <div className="bp3-input-group bp3-fill">
-              <input
-                onKeyDown={this.keyPress.bind(this)}
-                onChange={e => {
-                  this.setState({ gene: e.target.value });
-                }}
-                value={gene}
-                type="text"
-                className="bp3-input"
-                placeholder="Enter a gene name"
-                style={{ paddingRight: 94 }}
-              />
-            </div>
-            <Tooltip
-              content="Add a gene to see its expression levels"
-              position="bottom"
-            >
-              <Button intent="primary" onClick={this.handleClick.bind(this)}>
-                Add
-              </Button>
-            </Tooltip>
+            <Suggest
+              closeOnSelect
+              openOnKeyDown
+              resetOnSelect
+              noResults={<MenuItem disabled text="No matching genes." />}
+              onItemSelect={g => {
+                /* this happens on 'enter' */
+                this.handleClick(g);
+              }}
+              inputValueRenderer={g => {
+                return "";
+              }}
+              itemListPredicate={filterGenes}
+              itemRenderer={renderGene.bind(this)}
+              items={
+                world && world.varAnnotations
+                  ? world.varAnnotations
+                  : [{ name: "No genes", n_counts: "" }]
+              }
+              popoverProps={{ minimal: true }}
+            />
           </div>
           {world && userDefinedGenes.length > 0
             ? _.map(userDefinedGenes, (geneName, index) => {

client/src/reducers/controls.js

@@ -1,7 +1,7 @@
 // jshint esversion: 6
 
 import _ from "lodash";
-import { World, kvCache } from "../util/stateManager";
+import { World, kvCache, WorldUtil } from "../util/stateManager";
 import parseRGB from "../util/parseRGB";
 import Crossfilter from "../util/typedCrossfilter";
 import * as globals from "../globals";
@@ -24,25 +24,27 @@ Remember that option values can be ANY js type, except undefined/null.
   {
     _category_name_1: {
       // map of option value to index
-      optionIndex: Map([
-        optval1: index,
+      categoryIndices: Map([
+        catval1: index,
         ...
       ])
 
       // index->selection true/false state
-      optionSelected: [ true/false, true/false, ... ]
+      categorySelected: [ true/false, true/false, ... ]
 
       // number of options
-      numOptions: number,
+      numCategories: number,
 
       // isTruncated - true if the options for selection has
       // been truncated (ie, was too large to implement)
     }
   }
 */
-function topNoptions(summary) {
-  const counts = _.map(summary.categories, cat => summary.options[cat]);
-  const sortIndex = fillRange(new Array(summary.numOptions)).sort(
+function topNCategories(summary) {
+  const counts = _.map(summary.categories, cat =>
+    summary.categoryCounts.get(cat)
+  );
+  const sortIndex = fillRange(new Array(summary.numCategories)).sort(
     (a, b) => counts[b] - counts[a]
   );
   const sortedCategories = _.map(sortIndex, i => summary.categories[i]);
@@ -65,20 +67,18 @@ function createCategoricalSelectionState(state, world) {
         key !== "name" &&
         value.categories.length < state.maxCategoryItems;
       if (isSelectableCategory) {
-        const [optionValue, optionCount] = topNoptions(value);
-        // const optionCount = Object.values(value.options);
-
-        const optionIndex = new Map(optionValue.map((v, i) => [v, i]));
-        const numOptions = optionIndex.size;
-        const optionSelected = new Array(numOptions).fill(true);
-        const isTruncated = optionValue.length < value.numOptions;
+        const [categoryValues, categoryCounts] = topNCategories(value);
+        const categoryIndices = new Map(categoryValues.map((v, i) => [v, i]));
+        const numCategories = categoryIndices.size;
+        const categorySelected = new Array(numCategories).fill(true);
+        const isTruncated = categoryValues.length < value.numCategories;
         res[key] = {
-          optionValue, // array: of natively typed option values
-          optionIndex, // map: option value (native type) -> option index
-          optionSelected, // array: t/f selection state
-          numOptions, // number: of options
+          categoryValues, // array: of natively typed category values
+          categoryIndices, // map: category value (native type) -> category index
+          categorySelected, // array: t/f selection state
+          numCategories, // number: of categories
           isTruncated, // bool: true if list was truncated
-          optionCount // array: cardinality of each option
+          categoryCounts // array: cardinality of each category
         };
       }
     }
@@ -87,12 +87,12 @@ function createCategoricalSelectionState(state, world) {
 }
 
 /*
-given a categoricalSelectionState, return the list of all option values
+given a categoricalSelectionState, return the list of all category values
 where selection state is true (ie, they are selected).
 */
 function selectedValuesForCategory(categorySelectionState) {
-  const selectedValues = _([...categorySelectionState.optionIndex])
-    .filter(tuple => categorySelectionState.optionSelected[tuple[1]])
+  const selectedValues = _([...categorySelectionState.categoryIndices])
+    .filter(tuple => categorySelectionState.categorySelected[tuple[1]])
     .map(tuple => tuple[0])
     .value();
   return selectedValues;
@@ -175,6 +175,7 @@ const Controls = (
       );
       const crossfilter = Crossfilter(world.obsAnnotations);
       const dimensionMap = World.createObsDimensionMap(crossfilter, world);
+      WorldUtil.clearCaches();
 
       const worldVarDataCache = world.varDataCache;
 
@@ -247,6 +248,7 @@ const Controls = (
       );
       const crossfilter = Crossfilter(world.obsAnnotations);
       const dimensionMap = World.createObsDimensionMap(crossfilter, world);
+      WorldUtil.clearCaches();
 
       const worldVarDataCache = world.varDataCache;
       /* var dimensions */
@@ -514,15 +516,15 @@ const Controls = (
           Categorical metadata
     *******************************/
     case "categorical metadata filter select": {
-      const newOptionSelected = Array.from(
-        state.categoricalSelectionState[action.metadataField].optionSelected
+      const newCategorySelected = Array.from(
+        state.categoricalSelectionState[action.metadataField].categorySelected
       );
-      newOptionSelected[action.optionIndex] = true;
+      newCategorySelected[action.categoryIndex] = true;
       const newCategoricalSelectionState = {
         ...state.categoricalSelectionState,
         [action.metadataField]: {
           ...state.categoricalSelectionState[action.metadataField],
-          optionSelected: newOptionSelected
+          categorySelected: newCategorySelected
         }
       };
 
@@ -538,15 +540,15 @@ const Controls = (
       };
     }
     case "categorical metadata filter deselect": {
-      const newOptionSelected = Array.from(
-        state.categoricalSelectionState[action.metadataField].optionSelected
+      const newCategorySelected = Array.from(
+        state.categoricalSelectionState[action.metadataField].categorySelected
       );
-      newOptionSelected[action.optionIndex] = false;
+      newCategorySelected[action.categoryIndex] = false;
       const newCategoricalSelectionState = {
         ...state.categoricalSelectionState,
         [action.metadataField]: {
           ...state.categoricalSelectionState[action.metadataField],
-          optionSelected: newOptionSelected
+          categorySelected: newCategorySelected
         }
       };
 
@@ -566,8 +568,9 @@ const Controls = (
         ...state.categoricalSelectionState,
         [action.metadataField]: {
           ...state.categoricalSelectionState[action.metadataField],
-          optionSelected: Array.from(
-            state.categoricalSelectionState[action.metadataField].optionSelected
+          categorySelected: Array.from(
+            state.categoricalSelectionState[action.metadataField]
+              .categorySelected
           ).fill(false)
         }
       };
@@ -584,8 +587,9 @@ const Controls = (
         ...state.categoricalSelectionState,
         [action.metadataField]: {
           ...state.categoricalSelectionState[action.metadataField],
-          optionSelected: Array.from(
-            state.categoricalSelectionState[action.metadataField].optionSelected
+          categorySelected: Array.from(
+            state.categoricalSelectionState[action.metadataField]
+              .categorySelected
           ).fill(true)
         }
       };

client/src/util/actionHelpers.js

@@ -38,7 +38,11 @@ export const doJsonRequest = async url => {
     return res.json();
   }
   // else an error
-  const msg = `Unexpected HTTP response ${res.status}, ${res.statusText}`;
+  let msg = `Unexpected HTTP response ${res.status}, ${res.statusText}`;
+  const body = await res.text();
+  if (body && body.length > 0) {
+    msg = `${msg} -- ${body}`;
+  }
   dispatchNetworkErrorMessageToUser(msg);
   throw new Error(msg);
 };

client/src/util/stateManager/index.js

@@ -17,3 +17,4 @@ exists to support those concepts.
 export * as Universe from "./universe";
 export * as World from "./world";
 export * as kvCache from "./keyvalcache";
+export * as WorldUtil from "./worldUtil";

client/src/util/stateManager/summarizeAnnotations.js

@@ -8,6 +8,7 @@ Value will be an object, containing summary information.
 
 For continuous annotations (int, float, etc):
   <annotation_name>: {
+    categorical: false,
     range {
       min: <number>,
       max: <number>
@@ -15,12 +16,14 @@ For continuous annotations (int, float, etc):
   }
 
 For categorical annotations (boolean, string, category):
-  <annotatoin_name>: {
-    options: {
-      <option1>: <number>,
+  <annotation_name>: {
+    categorical: true,
+    categories: [ <category1>, <category2>, ... ]
+    categoryCounts: Map {
+      <category1>: <number>,
       ...
     },
-    numOptions: <number>
+    numCategories: <number>
   }
 
 Summarize will be returned for BOTH obs and var annotations.
@@ -28,19 +31,19 @@ Summarize will be returned for BOTH obs and var annotations.
 Example:
   {
     "Splice_sites_Annotated": {
-      "range": {
+      categorical: false,
+      range: {
         "min": 26,
         "max": 1075869
       }
     },
     "Selection": {
-      numOptions, 6,
-      "options": {
+      categorical: true,
+      numCategories, 3,
+      categories: [ "Astrocytes(HEPACAM)", "Endothelial(BSC)", "Unpanned" ],
+      categoryCounts: Map {
         "Astrocytes(HEPACAM)": 714,
         "Endothelial(BSC)": 123,
-        "Oligodendrocytes(GC)": 294,
-        "Neurons(Thy1)": 685,
-        "Microglia(CD45)": 1108,
         "Unpanned": 665
       }
     }
@@ -48,46 +51,46 @@ Example:
 
 NOTE: will not summarize the required 'name' annotation, as that is
 specified as unique per element.
-
-TODO: XXX - this data structure coerces all metadata categories into a string
-(ie, stores values as an Object property in the `options` field).   This looses
-information (eg, type) for category types which are not strings.  Consider an
-alterative data structure that does not use the object property for non-string
-data types (and does not use _.countBy to summarize).
 */
-function summarizeDimension(schema, annotations) {
-  return _(schema)
+function _summarizeAnnotations(_schema, annotations) {
+  const summary = _(_schema) // lodash wrapping: https://lodash.com/docs/4.17.11#lodash
     .filter(v => v.name !== "name")
     .keyBy("name")
     .mapValues(anno => {
       const { name, type } = anno;
       const continuous = type === "int32" || type === "float32";
 
-      if (!continuous) {
-        const categories = _.uniq(_.flatMap(annotations, name));
-        const options = _.countBy(annotations, name);
-        const numOptions = _.size(options);
-        return {
-          numOptions,
-          options,
-          categories
-        };
-      }
-
       if (continuous) {
         let min = Number.POSITIVE_INFINITY;
         let max = Number.NEGATIVE_INFINITY;
-        _.forEach(annotations, obs => {
-          const val = Number(obs[name]);
+        for (let r = 0; r < annotations.length; r += 1) {
+          const val = Number(annotations[r][name]);
           min = val < min ? val : min;
           max = val > max ? val : max;
-        });
-        return { range: { min, max } };
+        }
+        return {
+          categorical: false,
+          range: { min, max }
+        };
       }
 
-      throw new Error("incomprehensible schema");
+      /* else categorical */
+      const categoryCounts = new Map();
+      for (let r = 0; r < annotations.length; r += 1) {
+        const val = annotations[r][name];
+        let curCount = categoryCounts.get(val);
+        if (curCount === undefined) curCount = 0;
+        categoryCounts.set(val, curCount + 1);
+      }
+      return {
+        categorical: true,
+        categories: [...categoryCounts.keys()],
+        categoryCounts,
+        numCategories: categoryCounts.size
+      };
     })
     .value();
+  return summary;
 }
 
 export default function summarizeAnnotations(
@@ -96,7 +99,7 @@ export default function summarizeAnnotations(
   varAnnotations
 ) {
   return {
-    obs: summarizeDimension(schema.annotations.obs, obsAnnotations),
-    var: summarizeDimension(schema.annotations.var, varAnnotations)
+    obs: _summarizeAnnotations(schema.annotations.obs, obsAnnotations),
+    var: _summarizeAnnotations(schema.annotations.var, varAnnotations)
   };
 }

client/src/util/stateManager/worldUtil.js

@@ -0,0 +1,65 @@
+/* eslint-disable import/prefer-default-export */
+import _ from "lodash";
+
+/*
+Various utility functions operating on World/Universe
+*/
+
+/*
+Count unique category values, binning first by dim1 then by dim2
+Return:
+
+Map {
+   dim1_val1: Map {
+      dim2_val1: number,
+      dim2_val2: number,
+      ...
+   },
+   ...
+}
+
+*/
+function _countCategoryValues2D(dim1, dim2, rows) {
+  const dimMap = new Map();
+  for (let r = 0; r < rows.length; r += 1) {
+    const row = rows[r];
+    const val1 = row[dim1];
+    const val2 = row[dim2];
+    let d2Map = dimMap.get(val1);
+    if (d2Map === undefined) {
+      d2Map = new Map();
+      dimMap.set(val1, d2Map);
+    }
+    let curCount = d2Map.get(val2);
+    if (curCount === undefined) {
+      curCount = 0;
+    }
+    d2Map.set(val2, curCount + 1);
+  }
+  return dimMap;
+}
+
+let __worldUtilMemoId__ = 0;
+function _memoizedId(x) {
+  if (!x.__worldUtilMemoId__) {
+    __worldUtilMemoId__ += 1;
+    x.__worldUtilMemoId__ = __worldUtilMemoId__;
+  }
+  return x.__worldUtilMemoId__;
+}
+function _countCategoryValues2DResolver(...args) {
+  const id = args[0] + args[1] + _memoizedId(args[2]);
+  return id;
+}
+
+export const countCategoryValues2D = _.memoize(
+  _countCategoryValues2D,
+  _countCategoryValues2DResolver
+);
+
+/*
+Clear any cached data within WorldUtil caches, eg, memoized functions
+*/
+export function clearCaches() {
+  countCategoryValues2D.cache.clear();
+}

docs/_config.yml

@@ -0,0 +1 @@
+theme: jekyll-theme-architect

docs/index.md

@@ -0,0 +1,37 @@
+# cellxgene
+
+cellxgene is an interactive data explorer for single-cell transcriptomics data designed to handle large datasets (1 million cells or more) and integrate with your favorite analysis tools
+
+## getting started
+
+install the package
+> `> pip install cellxgene`
+
+preprocess the data for use with cellxgene (optional)
+> `> cellxgene --prepare dataset.h5ad -o processed.h5ad`
+
+launch the web app
+> `> cellxgene --launch processed.h5ad`
+
+## features
+
+
+
+### inspiration and collaboration
+
+We've been heavily inspired by several other related single-cell visualization projects:
+* [UCSC Cell Browswer](http://cells.ucsc.edu/)
+* [Cytoscape](http://www.cytoscape.org/)
+* [Xena](https://xena.ucsc.edu/)
+* [ASAP](https://asap.epfl.ch/)
+* [Gene Pattern](http://genepattern-notebook.org/)
+
+We were inspired by Mike Bostock and the [crossfilter](https://github.com/crossfilter) team for the design of our filtering implementation.
+
+We have been working closely with the [`scanpy`](https://github.com/theislab/scanpy) team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset.
+
+We are eager to explore integrations with other computational backends such as [`Seurat`](https://github.com/satijalab/seurat) or [`Bioconductor`](https://github.com/Bioconductor)
+
+### help and contact
+
+Have questions, suggestions, or comments? You can come hang out with us by joining the [CZI Science Slack](https://join-cziscience-slack.herokuapp.com/) and posting in the `#cellxgene-users` channel. As mentioned above, please submit any feature requests or bugs as [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). We'd love to hear from you!

docs/pull_request_guidelines.md

@@ -0,0 +1,35 @@
+## Creating PR
+1. Name [username]/branchname 
+    1. Branch name should be all lowercase
+    2. Words separated by “-”
+2. Code should address only one issue ideally, make a separate PR for each task
+3. Description 
+    1. Clear explanation of issues solved
+    2. Describe why and how, when appropriate
+    3. Call out specific areas you want extra attention in review (optional)
+    4. If your PR requires more than one reviewer tag those people in the description or comments and let them know that you specifically require them
+4. Ensure that the PR updates tests and documentation and adds tests where appropriate
+5. Use github’s issue keywords when PR is addressing an issue https://help.github.com/articles/closing-issues-using-keywords/
+6. Tags (add at beginning of title)
+    1. [EASY] - small non-controversial change, easy to review
+    2. [DO NOT MERGE] - PR is in progress, do not merge changes
+
+## Review
+1. Assign at least one reviewer to submitted PRs.   Reviewers should be selected based on expertise in areas affected by the PR (eg, web UI: Colin), and should include Comp Bio and PM as needed.
+2. Reviewers should approve or request changes (not just comment) and put general and line level comments where appropriate
+3. As a PR submitter respond to all comments (eg, comment, commit a change, etc)
+4. External PRs
+    1. For external PRs or PRs not from our core team, core team should assign a reviewer and make initial contact within 1 business day
+    2. Build code on local environment and run smoke tests 
+
+## Required to Merge
+1. Travis CI Build passing
+2. At least one reviewer approved
+    1. Exceptions: 
+        1. Release PRs where version is just bumped should not need review
+        2. Complex PRs which touch multiple parts of the codebase should have reviews from all relevant parties
+3. License and Security checks (SNYK) passing. If their server is down and you didn’t add any new external npm or python packages, merge is OK
+
+## Merging
+1. Use "squash and merge" option when merging
+2. If you resolved conflicts, wait until the build passes to merge

docs/release_process.md

@@ -14,35 +14,40 @@ The release process should result in the following side-effects:
 - Tagged github release
 - Publication to PyPi
 
-## Process
+## Recipe
 
 Follow these steps to create a release.
 
 1.  Preparation:
-    - Define the release version number, using [semantic versioning](https://semver.org/)
-    - Write the release title and release notes and add to 
-    [release notes document](https://docs.google.com/document/d/1KnHwkYfhyWO5H8BDcMu7y3ogjvq5Yi4OwpmZ8DB6w0Y/edit)
+    - python3.6 environment, and a cellxgene clone
+    - install required tools: `pip install -r requirements-dev.txt`
+    - Define the release version number, using [semantic versioning](https://semver.org/),
+      and specifying all three digits (eg, 0.3.0)
+    - Write the release title and release notes and add to
+      [release notes document](https://docs.google.com/document/d/1KnHwkYfhyWO5H8BDcMu7y3ogjvq5Yi4OwpmZ8DB6w0Y/edit)
 2.  Create a release branch, eg, `release-version`
 3.  In the release branch:
-    - Run `bumpversion --config-file .bumpversion.cfg [major | minor | patch]`
+    - Run `bumpversion --config-file .bumpversion.cfg [major | minor | patch]`,
+      where you choose major/minor/patch depending on which part of the version
+      is being bumped (eg, 0.2.9->0.3 is minor).
     - Clean up existing environment using `bin/clean`
     - Build the JS asserts using `bin/build-client`
 4.  Commit and push the new branch
 5.  Create a PR for the release.
     - [optional] As needed, conduct PR review.
 6.  Merge to master
-7.  Create Github release using the version number and release notes 
-([instructions](https://help.github.com/articles/creating-releases/)).
+7.  Create Github release using the version number and release notes
+    ([instructions](https://help.github.com/articles/creating-releases/)).
     - Draft new release
     - Type version name matching release version number from (1)
     - Select `master` as release branch (ensure you merged the release PR)
     - Type title `Release {version num}`
     - [optional] Check pre-release if this release is not ready for production
     - Publish Release
-8.  Publish to pypi by performing the following steps (assumes you have `setuptools` and `twine` installed and that you 
-have registered for pypi and have write access to the cellxgene pypi package)
-    - Build the distribution by calling
-    `python setup.py sdist`
+8.  Publish to pypi by performing the following steps (assumes you have `setuptools`
+    and `twine` installed, that you have registered for pypi, and that you have
+    write access to the cellxgene pypi package):
+    - Build the distribution by calling `python setup.py sdist`
       inside the top-level directory
     - [optional] Upload the package to test pypi
       `twine upload --repository-url https://test.pypi.org/legacy/ dist/*`

server/__main__.py

@@ -2,11 +2,14 @@
 if __package__ is None:
     import sys
     from pathlib import Path
+
     PKG_PATH = Path(__file__).parent
     sys.path.insert(0, str(PKG_PATH.parent))
-    import server
+    import server # noqa F401
+
     __package__ = PKG_PATH.name
 
 # Main thing
-from .cli.cli import cli
+from .cli.cli import cli # noqa F402
+
 cli()

server/app/app.py

@@ -14,16 +14,14 @@ REACTIVE_LIMIT = 1_000_000
 
 app = Flask(__name__, static_folder="web/static")
 app.json_encoder = Float32JSONEncoder
-cache = Cache(app, config={"CACHE_TYPE": "simple", "CACHE_DEFAULT_TIMEOUT": 860000})
+cache = Cache(app, config={"CACHE_TYPE": "simple", "CACHE_DEFAULT_TIMEOUT": 860_000})
 Compress(app)
 CORS(app)
 
 # Config
 SECRET_KEY = os.environ.get("CXG_SECRET_KEY", default="SparkleAndShine")
 
-app.config.update(
-    SECRET_KEY=SECRET_KEY,
-)
+app.config.update(SECRET_KEY=SECRET_KEY)
 
 # Application Data
 data = None
@@ -36,7 +34,13 @@ docs.append(resources.get_swagger_doc())
 app.register_blueprint(webapp.bp)
 app.register_blueprint(resources.blueprint)
 app.register_blueprint(
-    get_swagger_blueprint(docs, "/api/swagger", produces=["application/json"], title="cellxgene rest api",
-                          description="An API connecting ExpressionMatrix2 clustering algorithm to cellxgene"))
+    get_swagger_blueprint(
+        docs,
+        "/api/swagger",
+        produces=["application/json"],
+        title="cellxgene rest api",
+        description="An API connecting ExpressionMatrix2 clustering algorithm to cellxgene",
+    )
+)
 
 app.add_url_rule("/", endpoint="index")

server/app/driver/driver.py

@@ -11,7 +11,6 @@ Sort order for methods
 
 
 class CXGDriver(metaclass=ABCMeta):
-
     def __init__(self, data, args):
         self.data = self._load_data(data)
         self.layout_method = args["layout"]
@@ -24,11 +23,8 @@ class CXGDriver(metaclass=ABCMeta):
     def features(self):
         features = {
             "cluster": {"available": False},
-            "layout": {
-                "obs": {"available": False},
-                "var": {"available": False},
-            },
-            "diffexp": {"available": False}
+            "layout": {"obs": {"available": False}, "var": {"available": False}},
+            "diffexp": {"available": False},
         }
         # TODO - Interactive limit should be generated from the actual available methods see GH issue #94
         if self.layout_method:

server/app/rest_api/rest.py

@@ -1,13 +1,12 @@
 from http import HTTPStatus
 import pkg_resources
+import warnings
 
-from flask import (
-    Blueprint, current_app, jsonify, make_response, request
-)
+from flask import Blueprint, current_app, jsonify, make_response, request
 from flask_restful_swagger_2 import Api, swagger, Resource
 from werkzeug.datastructures import ImmutableMultiDict
 
-from server.app.util.constants import Axis, DiffExpMode
+from server.app.util.constants import Axis, DiffExpMode, JSON_NaN_to_num_warning_msg
 from server.app.util.filter import parse_filter, QueryStringError
 from server.app.util.models import FilterModel
 from server.app.util.utils import get_mime_type
@@ -22,83 +21,78 @@ Sort order for routes
 
 
 class SchemaAPI(Resource):
-    @swagger.doc({
-        "summary": "get schema for dataframe and annotations",
-        "tags": ["initialize"],
-        "parameters": [],
-        "responses": {
-            "200": {
-                "description": "schema",
-                "examples": {
-                    "application/json": {
-                        "schema": {
-                            "dataframe": {
-                                "nObs": 383,
-                                "nVar": 19944,
-                                "type": "float32"
-                            },
-                            "annotations": {
-                                "obs": [
-                                    {"name": "name", "type": "string"},
-                                    {"name": "tissue_type", "type": "string"},
-                                    {"name": "num_reads", "type": "int32"},
-                                    {"name": "sample_name", "type": "string"},
-                                    {
-                                        "name": "clusters",
-                                        "type": "categorical",
-                                        "categories": [99, 1, "unknown cluster"]
-                                    },
-                                    {"name": "QScore", "type": "float32"}
-                                ],
-                                "var": [
-                                    {"name": "name", "type": "string"},
-                                    {"name": "gene", "type": "string"}
-                                ]
+    @swagger.doc(
+        {
+            "summary": "get schema for dataframe and annotations",
+            "tags": ["initialize"],
+            "parameters": [],
+            "responses": {
+                "200": {
+                    "description": "schema",
+                    "examples": {
+                        "application/json": {
+                            "schema": {
+                                "dataframe": {"nObs": 383, "nVar": 19944, "type": "float32"},
+                                "annotations": {
+                                    "obs": [
+                                        {"name": "name", "type": "string"},
+                                        {"name": "tissue_type", "type": "string"},
+                                        {"name": "num_reads", "type": "int32"},
+                                        {"name": "sample_name", "type": "string"},
+                                        {
+                                            "name": "clusters",
+                                            "type": "categorical",
+                                            "categories": [99, 1, "unknown cluster"],
+                                        },
+                                        {"name": "QScore", "type": "float32"},
+                                    ],
+                                    "var": [{"name": "name", "type": "string"}, {"name": "gene", "type": "string"}],
+                                },
                             }
                         }
-                    }
+                    },
                 }
-            }
+            },
         }
-
-    })
+    )
     def get(self):
         return make_response(jsonify({"schema": current_app.data.schema}), HTTPStatus.OK)
 
 
 class ConfigAPI(Resource):
-    @swagger.doc({
-        "summary": "Configuration information to assist in front-end adaptation"
-                   " to underlying engine, available functionality, interactive time limits, etc",
-        "tags": ["initialize"],
-        "parameters": [],
-        "responses": {
-            "200": {
-                "description": "schema",
-                "examples": {
-                    "application/json": {
-                        "config": {
-                            "features": [
-                                {"method": "POST", "path": "/cluster/", "available": False},
-                                {
-                                    "method": "POST",
-                                    "path": "/layout/obs",
-                                    "available": True,
-                                    "interactiveLimit": 10000
+    @swagger.doc(
+        {
+            "summary": "Configuration information to assist in front-end adaptation"
+            " to underlying engine, available functionality, interactive time limits, etc",
+            "tags": ["initialize"],
+            "parameters": [],
+            "responses": {
+                "200": {
+                    "description": "schema",
+                    "examples": {
+                        "application/json": {
+                            "config": {
+                                "features": [
+                                    {"method": "POST", "path": "/cluster/", "available": False},
+                                    {
+                                        "method": "POST",
+                                        "path": "/layout/obs",
+                                        "available": True,
+                                        "interactiveLimit": 10000,
+                                    },
+                                    {"method": "POST", "path": "/layout/var", "available": False},
+                                ],
+                                "displayNames": {
+                                    "engine": "ScanPy version 1.33",
+                                    "dataset": "/home/joe/mouse/blorth.csv",
                                 },
-                                {"method": "POST", "path": "/layout/var", "available": False}
-
-                            ],
-                            "displayNames": {
-                                "engine": "ScanPy version 1.33",
-                                "dataset": "/home/joe/mouse/blorth.csv"
-                            },
+                            }
                         }
-                    }
+                    },
                 }
-            }
+            },
         }
-    })
+    )
     def get(self):
         config = {
             "config": {
@@ -110,99 +104,95 @@ class ConfigAPI(Resource):
                 ],
                 "displayNames": {
                     "engine": f"cellxgene Scanpy engine version {pkg_resources.get_distribution('cellxgene').version}",
-                    "dataset": current_app.config["DATASET_TITLE"]
+                    "dataset": current_app.config["DATASET_TITLE"],
                 },
-                "parameters": {
-                    "max_category_items": current_app.data.max_category_items
-                }
+                "parameters": {"max_category_items": current_app.data.max_category_items},
             }
         }
         return make_response(jsonify(config), HTTPStatus.OK)
 
 
 class AnnotationsObsAPI(Resource):
-    @swagger.doc({
-        "summary": "Fetch annotations (metadata) for all observations.",
-        "tags": ["annotations"],
-        "parameters": [{
-            "in": "query",
-            "name": "annotation-name",
-            "type": "string",
-            "description": "list of 1 or more annotation names"
-        }],
-        "responses": {
-            "200": {
-                "description": "annotations",
-                "examples": {
-                    "application/json": {
-                        "names": [
-                            "tissue_type", "sex", "num_reads", "clusters"
-                        ],
-                        "data": [
-                            [0, "lung", "F", 39844, 99],
-                            [1, "heart", "M", 83, 1],
-                            [49, "spleen", None, 2, "unknown cluster"],
-
-                        ]
-                    }
-
+    @swagger.doc(
+        {
+            "summary": "Fetch annotations (metadata) for all observations.",
+            "tags": ["annotations"],
+            "parameters": [
+                {
+                    "in": "query",
+                    "name": "annotation-name",
+                    "type": "string",
+                    "description": "list of 1 or more annotation names",
                 }
+            ],
+            "responses": {
+                "200": {
+                    "description": "annotations",
+                    "examples": {
+                        "application/json": {
+                            "names": ["tissue_type", "sex", "num_reads", "clusters"],
+                            "data": [
+                                [0, "lung", "F", 39844, 99],
+                                [1, "heart", "M", 83, 1],
+                                [49, "spleen", None, 2, "unknown cluster"],
+                            ],
+                        }
+                    },
+                },
+                "400": {
+                    "description": "one or more of the annotation-name identifiers were not associated with an "
+                    "annotation name"
+                },
             },
-            "400": {
-                "description": "one or more of the annotation-name identifiers were not associated with an "
-                               "annotation name"
-            }
         }
-    })
+    )
     def get(self):
         fields = request.args.getlist("annotation-name", None)
         try:
             annotation_response = current_app.data.annotation({}, "obs", fields)
         except KeyError:
             return make_response(f"Error bad key in {fields}", HTTPStatus.BAD_REQUEST)
-        return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        try:
+            return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
-    @swagger.doc({
-        "summary": "Fetch annotations (metadata) for filtered subset of observations.",
-        "tags": ["annotations"],
-        "parameters": [
-            {
-                "in": "query",
-                "name": "annotation-name",
-                "type": "string",
-                "description": "list of 1 or more annotation names"
+    @swagger.doc(
+        {
+            "summary": "Fetch annotations (metadata) for filtered subset of observations.",
+            "tags": ["annotations"],
+            "parameters": [
+                {
+                    "in": "query",
+                    "name": "annotation-name",
+                    "type": "string",
+                    "description": "list of 1 or more annotation names",
+                },
+                {"name": "filter", "description": "Complex Filter", "in": "body", "schema": FilterModel},
+            ],
+            "responses": {
+                "200": {
+                    "description": "annotations",
+                    "examples": {
+                        "application/json": {
+                            "names": ["tissue_type", "sex", "num_reads", "clusters"],
+                            "data": [
+                                [0, "lung", "F", 39844, 99],
+                                [1, "heart", "M", 83, 1],
+                                [49, "spleen", None, 2, "unknown cluster"],
+                            ],
+                        }
+                    },
+                },
+                "400": {
+                    "description": "malformed filter or one or more of the annotation-name identifiers were"
+                    "not associated with an annotation name"
+                },
             },
-            {
-                "name": "filter",
-                "description": "Complex Filter",
-                "in": "body",
-                "schema": FilterModel
-            }
-        ],
-        "responses": {
-            "200": {
-                "description": "annotations",
-                "examples": {
-                    "application/json": {
-                        "names": [
-                            "tissue_type", "sex", "num_reads", "clusters"
-                        ],
-                        "data": [
-                            [0, "lung", "F", 39844, 99],
-                            [1, "heart", "M", 83, 1],
-                            [49, "spleen", None, 2, "unknown cluster"],
-
-                        ]
-                    }
-
-                }
-            },
-            "400": {
-                "description": "malformed filter or one or more of the annotation-name identifiers were"
-                               "not associated with an annotation name"
-            }
         }
-    })
+    )
     def put(self):
         fields = request.args.getlist("annotation-name", None)
         try:
@@ -211,89 +201,87 @@ class AnnotationsObsAPI(Resource):
             return make_response(f"Error bad key in {fields}", HTTPStatus.BAD_REQUEST)
         except FilterError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
-        return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        try:
+            return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
 
 class AnnotationsVarAPI(Resource):
-    @swagger.doc({
-        "summary": "Fetch annotations (metadata) for all variables.",
-        "tags": ["annotations"],
-        "parameters": [{
-            "in": "query",
-            "name": "annotation-name",
-            "type": "string",
-            "description": "list of 1 or more annotation names"
-        }],
-        "responses": {
-            "200": {
-                "description": "annotations",
-                "examples": {
-                    "application/json": {
-                        "names": [
-                            "name", "category"
-                        ],
-                        "data": [
-                            [0, "ATAD3C", 1],
-                            [1, "RER1", None],
-                            [49, "S100B", 6]
-                        ]
-                    }
-
+    @swagger.doc(
+        {
+            "summary": "Fetch annotations (metadata) for all variables.",
+            "tags": ["annotations"],
+            "parameters": [
+                {
+                    "in": "query",
+                    "name": "annotation-name",
+                    "type": "string",
+                    "description": "list of 1 or more annotation names",
                 }
+            ],
+            "responses": {
+                "200": {
+                    "description": "annotations",
+                    "examples": {
+                        "application/json": {
+                            "names": ["name", "category"],
+                            "data": [[0, "ATAD3C", 1], [1, "RER1", None], [49, "S100B", 6]],
+                        }
+                    },
+                },
+                "400": {
+                    "description": "one or more of the annotation-name identifiers were not associated with an"
+                    " annotation name"
+                },
             },
-            "400": {
-                "description": "one or more of the annotation-name identifiers were not associated with an"
-                               " annotation name"
-            }
         }
-    })
+    )
     def get(self):
         fields = request.args.getlist("annotation-name", None)
         try:
             annotation_response = current_app.data.annotation({}, "var", fields)
         except KeyError:
             return make_response(f"Error bad key in {fields}", HTTPStatus.BAD_REQUEST)
-        return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        try:
+            return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
-    @swagger.doc({
-        "summary": "Fetch annotations (metadata) for filtered subset of variables.",
-        "tags": ["annotations"],
-        "parameters": [
-            {
-                "in": "query",
-                "name": "annotation-name",
-                "type": "string",
-                "description": "list of 1 or more annotation names"
+    @swagger.doc(
+        {
+            "summary": "Fetch annotations (metadata) for filtered subset of variables.",
+            "tags": ["annotations"],
+            "parameters": [
+                {
+                    "in": "query",
+                    "name": "annotation-name",
+                    "type": "string",
+                    "description": "list of 1 or more annotation names",
+                },
+                {"name": "filter", "description": "Complex Filter", "in": "body", "schema": FilterModel},
+            ],
+            "responses": {
+                "200": {
+                    "description": "annotations",
+                    "examples": {
+                        "application/json": {
+                            "names": ["name", "category"],
+                            "data": [[0, "ATAD3C", 1], [1, "RER1", None], [49, "S100B", 6]],
+                        }
+                    },
+                },
+                "400": {
+                    "description": "malformed filter or one or more of the annotation-name identifiers were"
+                    "not associated with an annotation name"
+                },
             },
-            {
-                "name": "filter",
-                "description": "Complex Filter",
-                "in": "body",
-                "schema": FilterModel
-            }
-        ],
-        "responses": {
-            "200": {
-                "description": "annotations",
-                "examples": {
-                    "application/json": {
-                        "names": [
-                            "name", "category"
-                        ],
-                        "data": [
-                            [0, "ATAD3C", 1],
-                            [1, "RER1", None],
-                            [49, "S100B", 6]
-                        ]
-                    }
-                }
-            },
-            "400": {
-                "description": "malformed filter or one or more of the annotation-name identifiers were"
-                               "not associated with an annotation name"
-            }
         }
-    })
+    )
     def put(self):
         fields = request.args.getlist("annotation-name", None)
         try:
@@ -302,99 +290,74 @@ class AnnotationsVarAPI(Resource):
             return make_response(f"Error bad key in {fields}", HTTPStatus.BAD_REQUEST)
         except FilterError:
             return make_response("Malformed filter", HTTPStatus.BAD_REQUEST)
-        return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        try:
+            return make_response(jsonify(annotation_response), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
 
 class DataObsAPI(Resource):
-    @swagger.doc({
-        "summary": "Get data (expression values) from the dataframe.",
-        "tags": ["data"],
-        "parameters": [
-            {
-                "in": "query",
-                "name": "filter",
-                "type": "string",
-                "description": "axis:key:value"
-            },
-            {
-                "in": "query",
-                "name": "accept-type",
-                "type": "string",
-                "description": "MIME type"
-            },
-        ],
-        "responses": {
-            "200": {
-                "description": "expression",
-                "examples": {
-                    "application/json": {
-                        "var": [0, 20000],
-                        "obs": [
-                            [1, 39483, 3902, 203, 0, 0, 28]
-                        ]
-                    }
-                }
-            },
-            "400": {
-                "description": "Malformed filter"
-            },
-            "406": {
-                "description": "Unacceptable MIME type"
+    @swagger.doc(
+        {
+            "summary": "Get data (expression values) from the dataframe.",
+            "tags": ["data"],
+            "parameters": [
+                {"in": "query", "name": "filter", "type": "string", "description": "axis:key:value"},
+                {"in": "query", "name": "accept-type", "type": "string", "description": "MIME type"},
+            ],
+            "responses": {
+                "200": {
+                    "description": "expression",
+                    "examples": {"application/json": {"var": [0, 20000], "obs": [[1, 39483, 3902, 203, 0, 0, 28]]}},
+                },
+                "400": {"description": "Malformed filter"},
+                "406": {"description": "Unacceptable MIME type"},
             },
         }
-    })
+    )
     def get(self):
         accept_type = request.args.get("accept-type", None)
         # request.args is immutable
         args = request.args.copy()
         args.pop("accept-type", None)
         try:
-            filter_ = parse_filter(ImmutableMultiDict(args), current_app.data.schema['annotations'])
+            filter_ = parse_filter(ImmutableMultiDict(args), current_app.data.schema["annotations"])
         except QueryStringError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
         # TODO support CSV
         try:
             # TODO store mime_type when more than one is supported
-            get_mime_type(acceptable_types=["application/json"], query_param=accept_type,
-                          header=request.accept_mimetypes)
+            get_mime_type(
+                acceptable_types=["application/json"], query_param=accept_type, header=request.accept_mimetypes
+            )
         except MimeTypeError as e:
             return make_response(e.message, HTTPStatus.NOT_ACCEPTABLE)
         try:
             return make_response((jsonify(current_app.data.data_frame(filter_, axis=Axis.OBS))), HTTPStatus.OK)
         except FilterError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
-    @swagger.doc({
-        "summary": "Get data (expression values) from the dataframe.",
-        "tags": ["data"],
-        "parameters": [
-            {
-                'name': 'filter',
-                'description': 'Complex Filter',
-                'in': 'body',
-                'schema': FilterModel
-            }
-        ],
-        "responses": {
-            "200": {
-                "description": "expression",
-                "examples": {
-                    "application/json": {
-                        "var": [0, 20000],
-                        "obs": [
-                            [1, 39483, 3902, 203, 0, 0, 28]
-                        ]
-                    }
-                }
-            },
-            "400": {
-                "description": "Malformed filter"
-            },
-            "406": {
-                "description": "Unacceptable MIME type"
+    @swagger.doc(
+        {
+            "summary": "Get data (expression values) from the dataframe.",
+            "tags": ["data"],
+            "parameters": [{"name": "filter", "description": "Complex Filter", "in": "body", "schema": FilterModel}],
+            "responses": {
+                "200": {
+                    "description": "expression",
+                    "examples": {"application/json": {"var": [0, 20000], "obs": [[1, 39483, 3902, 203, 0, 0, 28]]}},
+                },
+                "400": {"description": "Malformed filter"},
+                "406": {"description": "Unacceptable MIME type"},
             },
         }
-    })
+    )
     def put(self):
         if not request.accept_mimetypes.best_match(["application/json", "text/csv"]):
             return make_response(f"Unsupported MIME type '{request.accept_mimetypes}'", HTTPStatus.NOT_ACCEPTABLE)
@@ -403,100 +366,75 @@ class DataObsAPI(Resource):
         except MimeTypeError as e:
             return make_response(e.message, HTTPStatus.NOT_ACCEPTABLE)
         try:
-            return make_response((jsonify(current_app.data.data_frame(request.get_json()["filter"], axis=Axis.OBS))),
-                                 HTTPStatus.OK)
+            return make_response(
+                (jsonify(current_app.data.data_frame(request.get_json()["filter"], axis=Axis.OBS))), HTTPStatus.OK
+            )
         except FilterError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
 
 class DataVarAPI(Resource):
-    @swagger.doc({
-        "summary": "Get data (expression values) from the dataframe.",
-        "tags": ["data"],
-        "parameters": [
-            {
-                "in": "query",
-                "name": "filter",
-                "type": "string",
-                "description": "axis:key:value"
-            },
-            {
-                "in": "query",
-                "name": "accept-type",
-                "type": "string",
-                "description": "MIME type"
-            },
-        ],
-        "responses": {
-            "200": {
-                "description": "expression",
-                "examples": {
-                    "application/json": {
-                        "obs": [0, 20000],
-                        "var": [
-                            [1, 39483, 3902, 203, 0, 0, 28]
-                        ]
-                    }
-                }
-            },
-            "400": {
-                "description": "Malformed filter"
-            },
-            "406": {
-                "description": "Unacceptable MIME type"
+    @swagger.doc(
+        {
+            "summary": "Get data (expression values) from the dataframe.",
+            "tags": ["data"],
+            "parameters": [
+                {"in": "query", "name": "filter", "type": "string", "description": "axis:key:value"},
+                {"in": "query", "name": "accept-type", "type": "string", "description": "MIME type"},
+            ],
+            "responses": {
+                "200": {
+                    "description": "expression",
+                    "examples": {"application/json": {"obs": [0, 20000], "var": [[1, 39483, 3902, 203, 0, 0, 28]]}},
+                },
+                "400": {"description": "Malformed filter"},
+                "406": {"description": "Unacceptable MIME type"},
             },
         }
-    })
+    )
     def get(self):
         accept_type = request.args.get("accept-type", None)
         # request.args is immutable
         args = request.args.copy()
         args.pop("accept-type", None)
         try:
-            filter_ = parse_filter(ImmutableMultiDict(args), current_app.data.schema['annotations'])
+            filter_ = parse_filter(ImmutableMultiDict(args), current_app.data.schema["annotations"])
         except QueryStringError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
         try:
-            get_mime_type(acceptable_types=["application/json"], query_param=accept_type,
-                          header=request.accept_mimetypes)
+            get_mime_type(
+                acceptable_types=["application/json"], query_param=accept_type, header=request.accept_mimetypes
+            )
         except MimeTypeError as e:
             return make_response(e.message, HTTPStatus.NOT_ACCEPTABLE)
         try:
             return make_response((jsonify(current_app.data.data_frame(filter_, axis=Axis.VAR))), HTTPStatus.OK)
         except FilterError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
-    @swagger.doc({
-        "summary": "Get data (expression values) from the dataframe.",
-        "tags": ["data"],
-        "parameters": [
-            {
-                'name': 'filter',
-                'description': 'Complex Filter',
-                'in': 'body',
-                'schema': FilterModel
-            }
-        ],
-        "responses": {
-            "200": {
-                "description": "expression",
-                "examples": {
-                    "application/json": {
-                        "obs": [0, 20000],
-                        "var": [
-                            [1, 39483, 3902, 203, 0, 0, 28]
-                        ]
-                    }
-                }
-            },
-            "400": {
-                "description": "Malformed filter"
-            },
-            "406": {
-                "description": "Unacceptable MIME type"
+    @swagger.doc(
+        {
+            "summary": "Get data (expression values) from the dataframe.",
+            "tags": ["data"],
+            "parameters": [{"name": "filter", "description": "Complex Filter", "in": "body", "schema": FilterModel}],
+            "responses": {
+                "200": {
+                    "description": "expression",
+                    "examples": {"application/json": {"obs": [0, 20000], "var": [[1, 39483, 3902, 203, 0, 0, 28]]}},
+                },
+                "400": {"description": "Malformed filter"},
+                "406": {"description": "Unacceptable MIME type"},
             },
         }
-    })
+    )
     def put(self):
         if not request.accept_mimetypes.best_match(["application/json", "text/csv"]):
             return make_response(f"Unsupported MIME type '{request.accept_mimetypes}'", HTTPStatus.NOT_ACCEPTABLE)
@@ -506,74 +444,76 @@ class DataVarAPI(Resource):
         except MimeTypeError as e:
             return make_response(e.message, HTTPStatus.NOT_ACCEPTABLE)
         try:
-            return make_response((jsonify(current_app.data.data_frame(request.get_json()["filter"], axis=Axis.VAR))),
-                                 HTTPStatus.OK)
+            return make_response(
+                (jsonify(current_app.data.data_frame(request.get_json()["filter"], axis=Axis.VAR))), HTTPStatus.OK
+            )
         except FilterError as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
 
 class DiffExpObsAPI(Resource):
-    @swagger.doc({
-        "summary": "Generate differential expression (DE) statistics for two specified subsets of data, "
-                   "as indicated by the two provided observation complex filters",
-        "tags": ["diffexp"],
-        # TODO sort out params
-        # "parameters": [
-        #     # {
-        #     #     "in": "body",
-        #     #     "name": "mode",
-        #     #     "type": "string",
-        #     #     "required": True,
-        #     #     "description": "topN or varFilter"
-        #     # },
-        #     {
-        #         "in": "query",
-        #         "name": "count",
-        #         "type": "int32",
-        #         "description": "TopN mode: how many vars to return"
-        #     },
-        #     {
-        #         "in": "body",
-        #         "name": "varFilter",
-        #         "schema": FilterModel,
-        #         "description": "varFilter: Complex filter, only var for which vars to return"
-        #     },
-        #     {
-        #         "in": "body",
-        #         "name": "set1",
-        #         "schema": FilterModel,
-        #         "required": True,
-        #         "description": "Complex filter, only obs - observations in set1"
-        #     },
-        #     {
-        #         "in": "body",
-        #         "name": "set2",
-        #         "schema": FilterModel,
-        #         "description": "Complex filter, only obs - observations in set2. If not included, inverse of set1."
-        #     },
-        # ],
-        "responses": {
-            "200": {
-                "description": "Statistics are encoded as an array of arrays, with fields ordered as: "
-                               "varIndex, logfoldchange,  pVal, pValAdj",
-                "examples": {
-                    "application/json": [
-                        [328, -2.569489, 2.655706e-63, 3.642036e-57],
-                        [1250, -2.569489, 2.655706e-63, 3.642036e-57],
-                    ]
-                }
+    @swagger.doc(
+        {
+            "summary": "Generate differential expression (DE) statistics for two specified subsets of data, "
+            "as indicated by the two provided observation complex filters",
+            "tags": ["diffexp"],
+            # TODO sort out params
+            # "parameters": [
+            #     # {
+            #     #     "in": "body",
+            #     #     "name": "mode",
+            #     #     "type": "string",
+            #     #     "required": True,
+            #     #     "description": "topN or varFilter"
+            #     # },
+            #     {
+            #         "in": "query",
+            #         "name": "count",
+            #         "type": "int32",
+            #         "description": "TopN mode: how many vars to return"
+            #     },
+            #     {
+            #         "in": "body",
+            #         "name": "varFilter",
+            #         "schema": FilterModel,
+            #         "description": "varFilter: Complex filter, only var for which vars to return"
+            #     },
+            #     {
+            #         "in": "body",
+            #         "name": "set1",
+            #         "schema": FilterModel,
+            #         "required": True,
+            #         "description": "Complex filter, only obs - observations in set1"
+            #     },
+            #     {
+            #         "in": "body",
+            #         "name": "set2",
+            #         "schema": FilterModel,
+            #         "description": "Complex filter, only obs - observations in set2.
+            # If not included, inverse of set1."
+            #     },
+            # ],
+            "responses": {
+                "200": {
+                    "description": "Statistics are encoded as an array of arrays, with fields ordered as: "
+                    "varIndex, logfoldchange,  pVal, pValAdj",
+                    "examples": {
+                        "application/json": [
+                            [328, -2.569_489, 2.655_706e-63, 3.642_036e-57],
+                            [1250, -2.569_489, 2.655_706e-63, 3.642_036e-57],
+                        ]
+                    },
+                },
+                "400": {"description": "malformed filter"},
+                "403": {"description": "non-interactive request"},
+                "501": {"description": "diffexp is not implemented"},
             },
-            "400": {
-                "description": "malformed filter"
-            },
-            "403": {
-                "description": "non-interactive request"
-            },
-            "501": {
-                "description": "diffexp is not implemented"
-            }
         }
-    })
+    )
     def post(self):
         args = request.get_json()
         # confirm mode is present and legal
@@ -608,46 +548,54 @@ class DiffExpObsAPI(Resource):
         # mode=topN
         count = args.get("count", None)
         try:
-            diffexp = current_app.data.diffexp_topN(set1_filter, set2_filter, count,
-                                                    current_app.data.features["diffexp"]["interactiveLimit"])
+            diffexp = current_app.data.diffexp_topN(
+                set1_filter, set2_filter, count, current_app.data.features["diffexp"]["interactiveLimit"]
+            )
         except (ValueError, FilterError) as e:
             return make_response(e.message, HTTPStatus.BAD_REQUEST)
         except InteractiveError:
             return make_response("Non-interactive request", HTTPStatus.FORBIDDEN)
-        return make_response(jsonify(diffexp), HTTPStatus.OK)
+        try:
+            return make_response(jsonify(diffexp), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
 
 class LayoutObsAPI(Resource):
-    @swagger.doc({
-        "summary": "Get the default layout for all observations.",
-        "tags": ["layout"],
-        "parameters": [],
-        "responses": {
-            "200": {
-                "description": "layout",
-                "examples": {
-                    "application/json": {
-                        "layout": {
-                            "ndims": 2,
-                            "coordinates": [
-                                [0, 0.284483, 0.983744],
-                                [1, 0.038844, 0.739444]
-                            ]
+    @swagger.doc(
+        {
+            "summary": "Get the default layout for all observations.",
+            "tags": ["layout"],
+            "parameters": [],
+            "responses": {
+                "200": {
+                    "description": "layout",
+                    "examples": {
+                        "application/json": {
+                            "layout": {
+                                "ndims": 2,
+                                "coordinates": [[0, 0.284_483, 0.983_744], [1, 0.038_844, 0.739_444]],
+                            }
                         }
-                    }
-                }
+                    },
+                },
+                "400": {"description": "Data preparation error"},
             },
-            "400": {
-                "description": "Data preparation error"
-            }
         }
-    })
+    )
     def get(self):
         try:
             layout = current_app.data.layout({})
         except PrepareError as e:
             return make_response(e.message, HTTPStatus.INTERNAL_SERVER_ERROR)
-        return make_response((jsonify({"layout": layout})), HTTPStatus.OK)
+        try:
+            return make_response((jsonify({"layout": layout})), HTTPStatus.OK)
+        except ValueError as e:
+            # JSON encoding failure, usually due to bad data
+            warnings.warn(JSON_NaN_to_num_warning_msg)
+            return make_response(str(e), HTTPStatus.INTERNAL_SERVER_ERROR)
 
     # @swagger.doc({
     #     "summary": "Observation layout for filtered subset.",

server/app/scanpy_engine/diffexp.py

@@ -1,4 +1,3 @@
-
 import numpy as np
 from scipy import sparse, stats
 
@@ -64,19 +63,19 @@ def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
     sum_vn = vnA + vnB
 
     # degrees of freedom for Welch's t-test
-    with np.errstate(divide='ignore', invalid='ignore'):
-        dof = sum_vn**2 / (vnA**2 / (nA - 1) + vnB**2 / (nB - 1))
+    with np.errstate(divide="ignore", invalid="ignore"):
+        dof = sum_vn ** 2 / (vnA ** 2 / (nA - 1) + vnB ** 2 / (nB - 1))
     dof[np.isnan(dof)] = 1
 
     # Welch's t-test score calculation
-    with np.errstate(divide='ignore', invalid='ignore'):
+    with np.errstate(divide="ignore", invalid="ignore"):
         tscores = (meanA - meanB) / np.sqrt(sum_vn)
     tscores[np.isnan(tscores)] = 0
 
     # p-value
     pvals = stats.t.sf(np.abs(tscores), dof) * 2
     pvals_adj = pvals * adata._X.shape[1]
-    pvals_adj[pvals_adj > 1] = 1        # cap adjusted p-value at 1
+    pvals_adj[pvals_adj > 1] = 1  # cap adjusted p-value at 1
 
     # logfoldchanges: log2(meanA / meanB)
     logfoldchanges = np.log2(np.abs((meanA + 1e-9) / (meanB + 1e-9)))
@@ -106,8 +105,5 @@ def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
     pvals_adj_top_n = pvals_adj[sort_order]
 
     # varIndex, logfoldchange, pval, pval_adj
-    result = [[sort_order[i],
-               logfoldchanges_top_n[i],
-               pvals_top_n[i],
-               pvals_adj_top_n[i]] for i in range(top_n)]
+    result = [[sort_order[i], logfoldchanges_top_n[i], pvals_top_n[i], pvals_adj_top_n[i]] for i in range(top_n)]
     return result

server/app/scanpy_engine/scanpy_engine.py

@@ -22,7 +22,6 @@ Sort order for methods
 
 
 class ScanpyEngine(CXGDriver):
-
     def __init__(self, data, args):
         super().__init__(data, args)
         self._alias_annotation_names(Axis.OBS, args["obs_names"])
@@ -35,6 +34,10 @@ class ScanpyEngine(CXGDriver):
         self.diffexp_options = ["ttest"]
         self._create_schema()
 
+        # TODO: temporary work-arounds
+        if args["nan_to_num"]:
+            self._IEEE754_special_values_workaround()
+
     def _alias_annotation_names(self, axis, name):
         """
         Do all user-specified annotation aliasing.
@@ -57,8 +60,9 @@ class ScanpyEngine(CXGDriver):
             if name not in df_axis.columns:
                 raise KeyError(f"Annotation name {name}, specified in --{ax_name}-name does not exist.")
             if not df_axis[name].is_unique:
-                raise KeyError(f"Values in -{ax_name}-name must be unique. "
-                               "Please prepare data to contain unique values.")
+                raise KeyError(
+                    f"Values in -{ax_name}-name must be unique. " "Please prepare data to contain unique values."
+                )
             # reset index to simple range; alias user-specified annotation to "name"
             df_axis.reset_index(drop=True, inplace=True)
             df_axis.rename(inplace=True, columns={name: "name"})
@@ -69,7 +73,7 @@ class ScanpyEngine(CXGDriver):
     def _can_cast_to_float32(ann):
         if ann.dtype.kind == "f":
             if not np.can_cast(ann.dtype, np.float32):
-                warnings.warn(f"Annotation {ann.name} will be converted to 32 bit float and may loose precision.")
+                warnings.warn(f"Annotation {ann.name} will be converted to 32 bit float and may lose precision.")
             return True
         return False
 
@@ -85,15 +89,8 @@ class ScanpyEngine(CXGDriver):
 
     def _create_schema(self):
         self.schema = {
-            "dataframe": {
-                "nObs": self.cell_count,
-                "nVar": self.gene_count,
-                "type": str(self.data.X.dtype)
-            },
-            "annotations": {
-                "obs": [],
-                "var": []
-            }
+            "dataframe": {"nObs": self.cell_count, "nVar": self.gene_count, "type": str(self.data.X.dtype)},
+            "annotations": {"obs": [], "var": []},
         }
         for ax in Axis:
             curr_axis = getattr(self.data, str(ax))
@@ -125,32 +122,35 @@ class ScanpyEngine(CXGDriver):
         try:
             result = sc.read(data, cache=True)
         except ValueError:
-            raise ScanpyFileError("File must be in the .h5ad format. Please read "
-                                  "https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
-                                  "learn more about this format. You may be able to convert your file into this format "
-                                  "using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
-                                  "information.")
+            raise ScanpyFileError(
+                "File must be in the .h5ad format. Please read "
+                "https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
+                "learn more about this format. You may be able to convert your file into this format "
+                "using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
+                "information."
+            )
         except Exception as e:
-            raise ScanpyFileError(f"Error while loading file: {e}, File must be in the .h5ad format, please check "
-                                  f"that your input and try again.")
+            raise ScanpyFileError(
+                f"Error while loading file: {e}, File must be in the .h5ad format, please check "
+                f"that your input and try again."
+            )
         return result
 
     def _validate_data_types(self):
         if self.data.X.dtype != "float32":
-            warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
-                          f"Precision may be truncated.")
+            warnings.warn(
+                f"Scanpy data matrix is in {self.data.X.dtype} format not float32. " f"Precision may be truncated."
+            )
         for ax in Axis:
             curr_axis = getattr(self.data, str(ax))
             for ann in curr_axis:
                 datatype = curr_axis[ann].dtype
-                downcast_map = {"int64": "int32",
-                                "uint32": "int32",
-                                "uint64": "int32",
-                                "float64": "float32",
-                                }
+                downcast_map = {"int64": "int32", "uint32": "int32", "uint64": "int32", "float64": "float32"}
                 if datatype in downcast_map:
-                    warnings.warn(f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
-                                  f"Data will be downcast to {downcast_map[datatype]}.")
+                    warnings.warn(
+                        f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
+                        f"Data will be downcast to {downcast_map[datatype]}."
+                    )
                 if isinstance(datatype, CategoricalDtype):
                     category_num = len(curr_axis[ann].dtype.categories)
                     if category_num > 500 and category_num > self.max_category_items:
@@ -158,7 +158,8 @@ class ScanpyEngine(CXGDriver):
                             f"{str(ax).title()} annotation '{ann}' has {category_num} categories, this may be "
                             f"cumbersome or slow to display. We recommend setting the "
                             f"--max-category-items option to 500, this will hide categorical "
-                            f"annotations with more than 500 categories in the UI")
+                            f"annotations with more than 500 categories in the UI"
+                        )
 
     def _validate_data_calculations(self):
         layout_key = f"X_{self.layout_method}"
@@ -170,7 +171,68 @@ class ScanpyEngine(CXGDriver):
                 f" layout may have been computed. The requested layout must be pre-calculated and saved "
                 f"back in the h5ad file. You can run "
                 f"`cellxgene prepare --layout {self.layout_method} <datafile>` "
-                f"to solve this problem. ")
+                f"to solve this problem. "
+            )
+
+    def _IEEE754_special_values_workaround(self):
+        """
+        TODO: temporary workaround
+
+        Because all floating point data is serialized to JSON, and JSON has no means of representing
+        non-finite, floating point special values (NaN, +/-Infinity, etc), we include this temporary
+        work-around.
+
+        This will likely be removed in the future, contingent upon improved marshalling.
+
+        Where non-finite floating point is present in obs, var or X:
+        * issue a warning to the user that these values will be convert to finite numbers.
+        * set NaN to zero, and Infinities to min/max of the element.
+        """
+
+        # annotations
+        for ax in Axis:
+            curr_axis = getattr(self.data, str(ax))
+            for ann in curr_axis:
+                dtype = curr_axis[ann].dtype
+                if dtype.kind == "f":
+                    finite_idx = np.isfinite(curr_axis[ann])
+                    if not finite_idx.all():
+                        curr_axis.loc[np.isnan(curr_axis[ann]), ann] = 0
+                        curr_axis.loc[np.isneginf(curr_axis[ann]), ann] = curr_axis[ann][finite_idx].min()
+                        curr_axis.loc[np.isposinf(curr_axis[ann]), ann] = curr_axis[ann][finite_idx].max()
+                        warnings.warn(
+                            f"{str(ax).title()} annotation '{ann}' contains floating point NaN or Infinities. "
+                            f"These will be converted to finite values."
+                        )
+
+        # X
+        non_finite_X_found = False
+        if sparse.issparse(self.data._X):
+            coo = self.data._X.tocoo()
+            finite_idx = np.isfinite(coo.data)
+            if not finite_idx.all():
+                non_finite_X_found = True
+                coo.data[np.isnan(coo.data)] = 0
+                coo.data[np.isneginf(coo.data)] = np.min(coo.data[finite_idx])
+                coo.data[np.isposinf(coo.data)] = np.max(coo.data[finite_idx])
+                coo.eliminate_zeros()
+                _X = coo.asformat(self.data._X.getformat())
+                self.data._X = _X
+        else:
+            _X = self.data._X
+            finite_idx = np.isfinite(_X.flat)
+            if not finite_idx.all():
+                non_finite_X_found = True
+                min_X = _X.flat[finite_idx].min()
+                max_X = _X.flat[finite_idx].max()
+                _X[np.isnan(_X)] = 0
+                _X[np.isneginf(_X)] = min_X
+                _X[np.isposinf(_X)] = max_X
+
+        if non_finite_X_found:
+            warnings.warn(
+                "Dataframe X contains floating point NaN or Infinities. " "These will be converted to finite values."
+            )
 
     def filter_dataframe(self, filter):
         """
@@ -191,7 +253,7 @@ class ScanpyEngine(CXGDriver):
 
     @staticmethod
     def _annotation_filter_to_mask(filter, d_axis, count):
-        mask = np.ones((count, ), dtype=bool)
+        mask = np.ones((count,), dtype=bool)
         for v in filter:
             if d_axis[v["name"]].dtype.name in ["boolean", "category", "object"]:
                 key_idx = np.in1d(getattr(d_axis, v["name"]), v["values"])
@@ -209,24 +271,23 @@ class ScanpyEngine(CXGDriver):
 
     @staticmethod
     def _index_filter_to_mask(filter, count):
-        mask = np.zeros((count, ), dtype=bool)
+        mask = np.zeros((count,), dtype=bool)
         for i in filter:
             if type(i) == list:
-                mask[i[0]:i[1]] = True
+                mask[i[0] : i[1]] = True
             else:
                 mask[i] = True
         return mask
 
     @staticmethod
     def _axis_filter_to_mask(filter, d_axis, count):
-        mask = np.ones((count, ), dtype=bool)
+        mask = np.ones((count,), dtype=bool)
         if "index" in filter:
             mask = np.logical_and(mask, ScanpyEngine._index_filter_to_mask(filter["index"], count))
         if "annotation_value" in filter:
-            mask = np.logical_and(mask,
-                                  ScanpyEngine._annotation_filter_to_mask(filter["annotation_value"],
-                                                                          d_axis,
-                                                                          count))
+            mask = np.logical_and(
+                mask, ScanpyEngine._annotation_filter_to_mask(filter["annotation_value"], d_axis, count)
+            )
         return mask
 
     def _filter_to_mask(self, filter, use_slices=True):
@@ -256,8 +317,9 @@ class ScanpyEngine(CXGDriver):
 
         https://docs.scipy.org/doc/scipy/reference/sparse.html
         """
-        prefer_row_access = sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) \
-            or sparse.isspmatrix_bsr(data._X)
+        prefer_row_access = (
+            sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) or sparse.isspmatrix_bsr(data._X)
+        )
         if prefer_row_access:
             # Row-major slicing
             if obs_selector is not None:
@@ -290,18 +352,12 @@ class ScanpyEngine(CXGDriver):
             obs = self.data.obs[obs_selector]
             if not fields:
                 fields = obs.columns.tolist()
-            result = {
-                "names": fields,
-                "data": DataFrame(obs[fields]).to_records(index=True).tolist()
-            }
+            result = {"names": fields, "data": DataFrame(obs[fields]).to_records(index=True).tolist()}
         else:
             var = self.data.var[var_selector]
             if not fields:
                 fields = var.columns.tolist()
-            result = {
-                "names": fields,
-                "data": DataFrame(var[fields]).to_records(index=True).tolist()
-            }
+            result = {"names": fields, "data": DataFrame(var[fields]).to_records(index=True).tolist()}
         return result
 
     def data_frame(self, filter, axis):
@@ -326,12 +382,12 @@ class ScanpyEngine(CXGDriver):
         if axis == Axis.OBS:
             result = {
                 "var": var_index_sliced.tolist(),
-                "obs": DataFrame(_X, index=obs_index_sliced).to_records(index=True).tolist()
+                "obs": DataFrame(_X, index=obs_index_sliced).to_records(index=True).tolist(),
             }
         else:
             result = {
                 "obs": obs_index_sliced.tolist(),
-                "var": DataFrame(_X.T, index=var_index_sliced).to_records(index=True).tolist()
+                "var": DataFrame(_X.T, index=var_index_sliced).to_records(index=True).tolist(),
             }
         return result
 
@@ -370,11 +426,11 @@ class ScanpyEngine(CXGDriver):
         try:
             df_layout = df.obsm[f"X_{self.layout_method}"]
         except ValueError as e:
-            raise PrepareError(f"Layout has not been calculated using {self.layout_method}, "
-                               f"please prepare your datafile and relaunch cellxgene") from e
-        normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
-                                      index=df.obs.index)
-        return {
-            "ndims": normalized_layout.shape[1],
-            "coordinates": normalized_layout.to_records(index=True).tolist()
-        }
+            raise PrepareError(
+                f"Layout has not been calculated using {self.layout_method}, "
+                f"please prepare your datafile and relaunch cellxgene"
+            ) from e
+        normalized_layout = DataFrame(
+            (df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()), index=df.obs.index
+        )
+        return {"ndims": normalized_layout.shape[1], "coordinates": normalized_layout.to_records(index=True).tolist()}

server/app/util/constants.py

@@ -25,3 +25,6 @@ class Axis(AugmentedEnum):
 class DiffExpMode(AugmentedEnum):
     TOP_N = "topN"
     VAR_FILTER = "varFilter"
+
+
+JSON_NaN_to_num_warning_msg = "JSON encoding failure - suggest trying --nan-to-num command line option"

server/app/util/filter.py

@@ -7,7 +7,6 @@ from server.app.util.constants import Axis
 
 
 class QueryStringError(Exception):
-
     def __init__(self, key, message):
         self.key = key
         self.message = message

server/app/util/models.py

@@ -5,24 +5,13 @@ class AnnotationModel(Schema):
     type = "object"
     description = "Filter by annotation key: value"
     properties = {
-        "name": {
-            "type": "string"
-        },
+        "name": {"type": "string"},
         # TODO update to OpenAPI v3.0 when a library is available that supports it
         # Unfortunately 2.0 doesn't have a way to have a schema that accepts multiple types
         # Overloading the type key with a list seems to work ok and makes it to the page
-        "values": {
-            "type": "array",
-            "items": {
-                "type": ["float32", "string", "int32", "bool"]
-            }
-        },
-        "min": {
-            "type": ["int32", "float32"],
-        },
-        "max": {
-            "type": ["int32", "float32"],
-        }
+        "values": {"type": "array", "items": {"type": ["float32", "string", "int32", "bool"]}},
+        "min": {"type": ["int32", "float32"]},
+        "max": {"type": ["int32", "float32"]},
     }
     required = ["name"]
 
@@ -30,36 +19,16 @@ class AnnotationModel(Schema):
 class IndexModel(Schema):
     type = "object"
     description = "Filter by index of observation/variable ex. [0, 5, 15]"
-    properties = {
-        "index": {
-            "type": "array",
-            "items": {
-                "format": "int32",
-                "type": "integer"
-            }
-
-        }
-    }
+    properties = {"index": {"type": "array", "items": {"format": "int32", "type": "integer"}}}
 
 
 class AxisModel(Schema):
     type = "object"
     description = "Axis of data -- obs or var"
-    properties = {
-        "index": IndexModel,
-        "annotation_value": AnnotationModel.array()
-    }
+    properties = {"index": IndexModel, "annotation_value": AnnotationModel.array()}
 
 
 class FilterModel(Schema):
     type = "object"
     description = "Complex filter"
-    properties = {
-        "filter": {
-            "type": "object",
-            "properties": {
-                "obs": AxisModel,
-                "var": AxisModel
-            }
-        }
-    }
+    properties = {"filter": {"type": "object", "properties": {"obs": AxisModel, "var": AxisModel}}}

server/app/util/utils.py

@@ -7,6 +7,17 @@ from server.app.util.errors import MimeTypeError
 
 
 class Float32JSONEncoder(json.JSONEncoder):
+    def __init__(self, *args, **kwargs):
+        """
+        NaN/Infinities are illegal in standard JSON.  Python extends JSON with
+        non-standard symbols that most JavaScript JSON parsers do not understand.
+        The `allow_nan` parameter will force Python simplejson to throw an ValueError
+        if it runs into non-finite floating point values which are unsupported by
+        standard JSON.
+        """
+        kwargs["allow_nan"] = False
+        super().__init__(*args, **kwargs)
+
     def default(self, obj):
         if isinstance(obj, float32):
             return float(obj)
@@ -15,8 +26,13 @@ class Float32JSONEncoder(json.JSONEncoder):
         return json.JSONEncoder.default(self, obj)
 
 
-def get_mime_type(default="application/json", acceptable_types=["application/json", "text/csv"], query_param=None,
-                  header=None):
+def custom_format_warning(msg, *args, **kwargs):
+    return f"[cellxgene] Warning: {msg} \n"
+
+
+def get_mime_type(
+    default="application/json", acceptable_types=["application/json", "text/csv"], query_param=None, header=None
+):
     mime_type = default
     if query_param:
         if query_param in acceptable_types:

server/app/web/webapp.py

@@ -1,7 +1,5 @@
 import os
-from flask import (
-    Blueprint, render_template, send_from_directory, current_app
-)
+from flask import Blueprint, render_template, send_from_directory, current_app
 
 
 bp = Blueprint("webapp", __name__, template_folder="templates")

server/cli/cli.py

@@ -5,7 +5,7 @@ from .prepare import prepare
 
 
 @click.group(name="cellxgene", context_settings=dict(max_content_width=85))
-@click.version_option(version="0.2.3", prog_name="cellxgene", message="[%(prog)s] Version %(version)s")
+@click.version_option(version="0.4.0", prog_name="cellxgene", message="[%(prog)s] Version %(version)s")
 def cli():
     pass
 

server/cli/launch.py

@@ -1,37 +1,88 @@
-import sys
-import click
 import logging
 from os import devnull
 from os.path import splitext, basename
+import sys
+import warnings
 import webbrowser
 
+import click
+
 from server.app.util.errors import ScanpyFileError
+from server.app.util.utils import custom_format_warning
 
 
 @click.command()
 @click.argument("data", metavar="<data file>", type=click.Path(exists=True, file_okay=True, dir_okay=False))
-@click.option("--layout", "-l", type=click.Choice(["umap", "tsne"]), default="umap", show_default=True,
-              help="Method for layout.")
-@click.option("--diffexp", "-d", type=click.Choice(["ttest"]), default="ttest", show_default=True,
-              help="Method for differential expression.")
+@click.option(
+    "--layout", "-l", type=click.Choice(["umap", "tsne"]), default="umap", show_default=True, help="Method for layout."
+)
+@click.option(
+    "--diffexp",
+    "-d",
+    type=click.Choice(["ttest"]),
+    default="ttest",
+    show_default=True,
+    help="Method for differential expression.",
+)
 @click.option("--title", "-t", help="Title to display (if omitted will use file name).", metavar="")
-@click.option("--verbose", "-v", is_flag=True, default=False, show_default=True,
-              help="Provide verbose output, including warnings and all server requests.")
-@click.option("--debug", "-d", is_flag=True, default=False, show_default=True,
-              help="Run in debug mode.")
-@click.option("--open", "-o", "open_browser", is_flag=True, default=False, show_default=True,
-              help="Open the web browser after launch.")
+@click.option(
+    "--verbose",
+    "-v",
+    is_flag=True,
+    default=False,
+    show_default=True,
+    help="Provide verbose output, including warnings and all server requests.",
+)
+@click.option("--debug", "-d", is_flag=True, default=False, show_default=True, help="Run in debug mode.")
+@click.option(
+    "--open",
+    "-o",
+    "open_browser",
+    is_flag=True,
+    default=False,
+    show_default=True,
+    help="Open the web browser after launch.",
+)
 @click.option("--port", "-p", help="Port to run server on.", metavar="", default=5005, show_default=True)
 @click.option("--obs-names", default=None, metavar="", help="Name of annotation field to use for observations.")
 @click.option("--var-names", default=None, metavar="", help="Name of annotation to use for variables.")
-@click.option("--listen-all", is_flag=True, default=False, show_default=True,
-              help="Bind to all interfaces (this makes the server accessible beyond this computer).")
-@click.option("--max-category-items", default=100, metavar="", show_default=True,
-              help="Limits the number of categorical annotation items displayed.")
-@click.option("--diffexp-lfc-cutoff", default=0.01, show_default=True,
-              help="Relative expression cutoff used when selecting top N differentially expressed genes")
-def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
-           open_browser, port, listen_all, max_category_items, diffexp_lfc_cutoff):
+@click.option("--host", default="127.0.0.1", help="Host IP address")
+@click.option(
+    "--max-category-items",
+    default=100,
+    metavar="",
+    show_default=True,
+    help="Limits the number of categorical annotation items displayed.",
+)
+@click.option(
+    "--diffexp-lfc-cutoff",
+    default=0.01,
+    show_default=True,
+    help="Relative expression cutoff used when selecting top N differentially expressed genes",
+)
+@click.option(
+    "--nan-to-num",
+    is_flag=True,
+    default=False,
+    show_default=True,
+    help="Replace all floating point NaN with zero, and infinities with finite numbers",
+)
+def launch(
+    data,
+    layout,
+    diffexp,
+    title,
+    verbose,
+    debug,
+    obs_names,
+    var_names,
+    open_browser,
+    port,
+    host,
+    max_category_items,
+    diffexp_lfc_cutoff,
+    nan_to_num,
+):
     """Launch the cellxgene data viewer.
     This web app lets you explore single-cell expression data.
     Data must be in a format that cellxgene expects, read the
@@ -46,9 +97,6 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
     # Startup message
     click.echo("[cellxgene] Starting the CLI...")
 
-    # Import Flask app
-    from server.app.app import app
-
     # Argument checking
     name, extension = splitext(data)
     if extension != ".h5ad":
@@ -57,6 +105,8 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
     if debug:
         verbose = True
         open_browser = False
+    else:
+        warnings.formatwarning = custom_format_warning
 
     if not verbose:
         sys.tracebacklimit = 0
@@ -65,19 +115,14 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
         file_parts = splitext(basename(data))
         title = file_parts[0]
 
-    if listen_all:
-        host = "0.0.0.0"
-    else:
-        host = "127.0.0.1"
-
     # Setup app
     cellxgene_url = f"http://{host}:{port}"
     api_base = f"{cellxgene_url}/api/"
 
-    app.config.update(
-        DATASET_TITLE=title,
-        CXG_API_BASE=api_base
-    )
+    # Import Flask app
+    from server.app.app import app
+
+    app.config.update(DATASET_TITLE=title, CXG_API_BASE=api_base)
 
     if not verbose:
         log = logging.getLogger("werkzeug")
@@ -88,7 +133,8 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
     # Fix for anaconda python. matplotlib typically expects python to be installed as a framework TKAgg is usually
     # available and fixes this issue. See https://matplotlib.org/faq/virtualenv_faq.html
     import matplotlib as mpl
-    mpl.use('TkAgg')
+
+    mpl.use("TkAgg")
     from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
 
     args = {
@@ -97,7 +143,8 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
         "max_category_items": max_category_items,
         "diffexp_lfc_cutoff": diffexp_lfc_cutoff,
         "obs_names": obs_names,
-        "var_names": var_names
+        "var_names": var_names,
+        "nan_to_num": nan_to_num,
     }
 
     try:
@@ -114,7 +161,7 @@ def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
     click.echo("[cellxgene] Type CTRL-C at any time to exit.")
 
     if not verbose:
-        f = open(devnull, 'w')
+        f = open(devnull, "w")
         sys.stdout = f
 
     app.run(host=host, debug=debug, port=port, threaded=True)

server/cli/prepare.py

@@ -7,22 +7,48 @@ from scipy.sparse.csc import csc_matrix
 
 @click.command()
 @click.argument("data", nargs=1, metavar="<dataset: file or path to data>", required=True)
-@click.option("--layout", "-l", default=["umap", "tsne"], multiple=True, type=click.Choice(["umap", "tsne"]),
-              help="Layout algorithm", show_default=True)
-@click.option("--recipe", "-r", default="none", type=click.Choice(["none", "seurat", "zheng17"]),
-              help="Preprocessing to run.", show_default=True)
+@click.option(
+    "--layout",
+    "-l",
+    default=["umap", "tsne"],
+    multiple=True,
+    type=click.Choice(["umap", "tsne"]),
+    help="Layout algorithm",
+    show_default=True,
+)
+@click.option(
+    "--recipe",
+    "-r",
+    default="none",
+    type=click.Choice(["none", "seurat", "zheng17"]),
+    help="Preprocessing to run.",
+    show_default=True,
+)
 @click.option("--output", "-o", default="", help="Save a new file to filename.", metavar="<filename>")
 @click.option("--plotting", "-p", default=False, is_flag=True, help="Whether to generate plots.", show_default=True)
 @click.option("--sparse", default=False, is_flag=True, help="Whether to force sparsity.", show_default=True)
 @click.option("--overwrite", default=False, is_flag=True, help="Allow file overwriting.", show_default=True)
 @click.option("--set-obs-names", default="", help="Named field to set as index for obs.", metavar="<name>")
 @click.option("--set-var-names", default="", help="Named field to set as index for var.", metavar="<name>")
-@click.option("--make-obs-names-unique", default=True, is_flag=True,
-              help="Ensure obs index is unique.", show_default=True)
-@click.option("--make-var-names-unique", default=True, is_flag=True,
-              help="Ensure var index is unique.", show_default=True)
-def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
-            set_obs_names, set_var_names, make_obs_names_unique, make_var_names_unique):
+@click.option(
+    "--make-obs-names-unique", default=True, is_flag=True, help="Ensure obs index is unique.", show_default=True
+)
+@click.option(
+    "--make-var-names-unique", default=True, is_flag=True, help="Ensure var index is unique.", show_default=True
+)
+def prepare(
+    data,
+    layout,
+    recipe,
+    output,
+    plotting,
+    sparse,
+    overwrite,
+    set_obs_names,
+    set_var_names,
+    make_obs_names_unique,
+    make_var_names_unique,
+):
     """Preprocesses data for use with cellxgene.
 
     This tool runs a series of scanpy routines for preparing a dataset
@@ -35,6 +61,7 @@ def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
     # collect slow imports here to make CLI startup more responsive
     click.echo("[cellxgene] Starting CLI...")
     import matplotlib
+
     matplotlib.use("Agg")
     import scanpy.api as sc
 
@@ -49,8 +76,10 @@ def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
     output = expanduser(output)
 
     if not output:
-        click.echo("Warning: No file will be saved, to save the results of cellxgene prepare include "
-                   "--output <filename> to save output to a new file")
+        click.echo(
+            "Warning: No file will be saved, to save the results of cellxgene prepare include "
+            "--output <filename> to save output to a new file"
+        )
     if isfile(output) and not overwrite:
         raise click.UsageError(f"Cannot overwrite existing file {output}, try using the flag --overwrite")
 
@@ -119,9 +148,11 @@ def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
         try:
             sc.tl.louvain(adata)
         except ModuleNotFoundError:
-            click.echo("\nWarning: louvain module is not installed, no clusters will be calculated. "
-                       "To fix this please install cellxgene with the optional feature louvain enabled: "
-                       "`pip install cellxgene[louvain]`")
+            click.echo(
+                "\nWarning: louvain module is not installed, no clusters will be calculated. "
+                "To fix this please install cellxgene with the optional feature louvain enabled: "
+                "`pip install cellxgene[louvain]`"
+            )
 
     def run_layout(adata):
         if len(unique(adata.obs["louvain"].values)) < 10:
@@ -142,11 +173,11 @@ def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
     def show_step(item):
         names = {
             "make_sparse": "Ensuring sparsity",
-            "run_recipe": f"Running preprocessing recipe \"{recipe}\"",
+            "run_recipe": f'Running preprocessing recipe "{recipe}"',
             "run_pca": "Running PCA",
             "run_neighbors": "Calculating neighbors",
             "run_louvain": "Calculating clusters",
-            "run_layout": "Computing layout"
+            "run_layout": "Computing layout",
         }
         if item is not None:
             return names[item.__name__]

server/requirements-dev.txt

@@ -1,5 +1,6 @@
+black
+bumpversion>=0.5
 pytest>=3.6.3
 requests>=2.18.4
 twine>=1.12.1
-bumpversion>=0.5
 -r requirements.txt

server/requirements.txt

@@ -11,4 +11,4 @@ numpy>=1.14.5
 pandas>=0.23.1
 scanpy>=1.3.2
 scipy>=1.1.0
-scikit-learn==0.19.1
+scikit-learn>=0.19.1,!=0.20.0

server/test/test_api.py

@@ -9,15 +9,7 @@ LOCAL_URL = "http://127.0.0.1:5005/"
 VERSION = "v0.2"
 URL_BASE = f"{LOCAL_URL}api/{VERSION}/"
 
-BAD_FILTER = {
-            "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "xyz"},
-                    ],
-                }
-            }
-        }
+BAD_FILTER = {"filter": {"obs": {"annotation_value": [{"name": "xyz"}]}}}
 
 
 class EndPoints(unittest.TestCase):
@@ -133,7 +125,7 @@ class EndPoints(unittest.TestCase):
                         {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
                         {"name": "n_counts", "min": 3000},
                     ],
-                    "index": [1, 99, [1000, 2000]]
+                    "index": [1, 99, [1000, 2000]],
                 }
             }
         }
@@ -154,7 +146,7 @@ class EndPoints(unittest.TestCase):
                         {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
                         {"name": "n_counts", "min": 3000},
                     ],
-                    "index": [1, 99, [1000, 2000]]
+                    "index": [1, 99, [1000, 2000]],
                 }
             }
         }
@@ -170,23 +162,9 @@ class EndPoints(unittest.TestCase):
         url = f"{URL_BASE}{endpoint}"
         params = {
             "mode": "topN",
-            "set1": {
-                "filter": {
-                    "obs": {"annotation_value": [
-                        {"name": "louvain", "values": ["NK cells"]}
-                    ]
-                    }
-                }
-            },
-            "set2": {
-                "filter": {
-                    "obs": {"annotation_value": [
-                        {"name": "louvain", "values": ["CD8 T cells"]}
-                    ]
-                    }
-                }
-            },
-            "count": 7
+            "set1": {"filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["NK cells"]}]}}},
+            "set2": {"filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["CD8 T cells"]}]}}},
+            "count": 7,
         }
         result = self.session.post(url, json=params)
         self.assertEqual(result.status_code, HTTPStatus.OK)
@@ -199,20 +177,8 @@ class EndPoints(unittest.TestCase):
         params = {
             "mode": "topN",
             "count": 10,
-            "set1": {
-                "filter": {
-                    "obs": {
-                        "index": [[0, 500]]
-                    }
-                }
-            },
-            "set2": {
-                "filter": {
-                    "obs": {
-                        "index": [[500, 1000]]
-                    }
-                }
-            }
+            "set1": {"filter": {"obs": {"index": [[0, 500]]}}},
+            "set2": {"filter": {"obs": {"index": [[500, 1000]]}}},
         }
         result = self.session.post(url, json=params)
         self.assertEqual(result.status_code, HTTPStatus.OK)
@@ -249,15 +215,7 @@ class EndPoints(unittest.TestCase):
     def test_put_annotations_var(self):
         endpoint = "annotations/var"
         url = f"{URL_BASE}{endpoint}"
-        var_filter = {
-            "filter": {
-                "var": {
-                    "annotation_value": [
-                        {"name": "name", "values": ["ATAD3C", "RER1"]},
-                    ]
-                }
-            }
-        }
+        var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]}}}
         result = self.session.put(url, json=var_filter)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data = result.json()
@@ -268,15 +226,7 @@ class EndPoints(unittest.TestCase):
         endpoint = "annotations/var"
         query = "annotation-name=n_cells"
         url = f"{URL_BASE}{endpoint}?{query}"
-        var_filter = {
-            "filter": {
-                "var": {
-                    "annotation_value": [
-                        {"name": "name", "values": ["ATAD3C", "RER1"]},
-                    ]
-                }
-            }
-        }
+        var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]}}}
         result = self.session.put(url, json=var_filter)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data = result.json()
@@ -335,7 +285,7 @@ class EndPoints(unittest.TestCase):
                             {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
                             {"name": "n_counts", "min": 3000},
                         ],
-                        "index": [1, 99, [1000, 2000]]
+                        "index": [1, 99, [1000, 2000]],
                     }
                 }
             }
@@ -349,15 +299,7 @@ class EndPoints(unittest.TestCase):
             endpoint = f"data/{axis}"
             url = f"{URL_BASE}{endpoint}"
             header = {"Accept": "application/json"}
-            var_filter = {
-                "filter": {
-                    "var": {
-                        "annotation_value": [
-                            {"name": "name", "values": ["RER1"]},
-                        ]
-                    }
-                }
-            }
+            var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
             result = self.session.put(url, headers=header, json=var_filter)
             self.assertEqual(result.status_code, HTTPStatus.OK)
             result_data = result.json()
@@ -371,16 +313,44 @@ class EndPoints(unittest.TestCase):
     def test_cache(self):
         endpoint = "annotations/var"
         url = f"{URL_BASE}{endpoint}"
-        f1 = {"filter": {"var": {"annotation_value": [{"name": "name",
-                                                       "values": ["HLA-DRB1", "HLA-DQA1", "HLA-DQB1", "HLA-DPA1",
-                                                                  "HLA-DPB1", "MS4A1", "IL32", "CCL5", "CD79B",
-                                                                  "CD79A"]}]}}}
+        f1 = {
+            "filter": {
+                "var": {
+                    "annotation_value": [
+                        {
+                            "name": "name",
+                            "values": [
+                                "HLA-DRB1",
+                                "HLA-DQA1",
+                                "HLA-DQB1",
+                                "HLA-DPA1",
+                                "HLA-DPB1",
+                                "MS4A1",
+                                "IL32",
+                                "CCL5",
+                                "CD79B",
+                                "CD79A",
+                            ],
+                        }
+                    ]
+                }
+            }
+        }
         result = self.session.put(url, json=f1)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data1 = result.json()
-        f2 = {"filter": {"var": {"annotation_value": [{"name": "name",
-                                                       "values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH",
-                                                                  "CCL5", "CCL4", "CST7", "NKG7"]}]}}}
+        f2 = {
+            "filter": {
+                "var": {
+                    "annotation_value": [
+                        {
+                            "name": "name",
+                            "values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH", "CCL5", "CCL4", "CST7", "NKG7"],
+                        }
+                    ]
+                }
+            }
+        }
         result = self.session.put(url, json=f2)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data2 = result.json()
@@ -393,9 +363,18 @@ class EndPoints(unittest.TestCase):
         result = self.session.put(url, json=f1)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data1 = result.json()
-        f2 = {"filter": {"var": {"annotation_value": [{"name": "name",
-                                                       "values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH",
-                                                                  "CCL5", "CCL4", "CST7", "NKG7"]}]}}}
+        f2 = {
+            "filter": {
+                "var": {
+                    "annotation_value": [
+                        {
+                            "name": "name",
+                            "values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH", "CCL5", "CCL4", "CST7", "NKG7"],
+                        }
+                    ]
+                }
+            }
+        }
         result = self.session.put(url, json=f2)
         self.assertEqual(result.status_code, HTTPStatus.OK)
         result_data2 = result.json()

server/test/test_filter.py

@@ -54,13 +54,17 @@ class UtilTest(unittest.TestCase):
 
     def test_complex_filter(self):
         filter_dict = ImmutableMultiDict(
-            [("obs:louvain", "NK cells"), ("obs:louvain", "CD8 T cells"), ("obs:n_counts", "3000,*")])
+            [("obs:louvain", "NK cells"), ("obs:louvain", "CD8 T cells"), ("obs:n_counts", "3000,*")]
+        )
         filter_ = parse_filter(filter_dict, self.schema)
         self.assertIn("obs", filter_)
-        self.assertEqual(filter_["obs"]["annotation_value"], [{"name": "louvain",
-                                                               "values": ["NK cells", "CD8 T cells"]},
-                                                              {"name": "n_counts",
-                                                               "max": None, "min": 3000.0}])
+        self.assertEqual(
+            filter_["obs"]["annotation_value"],
+            [
+                {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
+                {"name": "n_counts", "max": None, "min": 3000.0},
+            ],
+        )
 
     def test_bad_filter(self):
         bad_annotation_type = ImmutableMultiDict([("obs:tissue", "lung")])
@@ -71,9 +75,7 @@ class UtilTest(unittest.TestCase):
             parse_filter(bad_axis, self.schema)
 
     def test_boolean_filter(self):
-        schema = {
-            "obs": [{"name": "bool_filter", "type": "boolean"}]
-        }
+        schema = {"obs": [{"name": "bool_filter", "type": "boolean"}]}
         filter_dict = ImmutableMultiDict([("obs:bool_filter", "false")])
         filter_ = parse_filter(filter_dict, schema)
         self.assertIn("obs", filter_)

server/test/test_scanpy_engine.py

@@ -3,7 +3,6 @@ from os import path
 import pytest
 import time
 import unittest
-import argparse
 
 import numpy as np
 from pandas import Series
@@ -13,8 +12,15 @@ from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
 
 class UtilTest(unittest.TestCase):
     def setUp(self):
-        args = {'layout': 'umap', 'diffexp': 'ttest', 'max_category_items': 100,
-                'obs_names': None, 'var_names': None, 'diffexp_lfc_cutoff': 0.01}
+        args = {
+            "layout": "umap",
+            "diffexp": "ttest",
+            "max_category_items": 100,
+            "obs_names": None,
+            "var_names": None,
+            "diffexp_lfc_cutoff": 0.01,
+            "nan_to_num": True,
+        }
 
         self.data = ScanpyEngine("example-dataset/pbmc3k.h5ad", args)
         self.data._create_schema()
@@ -22,8 +28,8 @@ class UtilTest(unittest.TestCase):
     def test_init(self):
         self.assertEqual(self.data.cell_count, 2638)
         self.assertEqual(self.data.gene_count, 1838)
-        epsilon = 0.000005
-        self.assertTrue(self.data.data.X[0, 0] - -0.17146951 < epsilon)
+        epsilon = 0.000_005
+        self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
 
     def test_mandatory_annotations(self):
         self.assertIn("name", self.data.data.obs)
@@ -38,69 +44,36 @@ class UtilTest(unittest.TestCase):
             self.data._validate_data_types()
 
     def test_filter_idx(self):
-        filter_ = {
-            "filter": {
-                "var": {
-                    "index": [1, 99, [200, 300]]
-                },
-                "obs": {
-                    "index": [1, 99, [1000, 2000]]
-                }
-            }
-        }
+        filter_ = {"filter": {"var": {"index": [1, 99, [200, 300]]}, "obs": {"index": [1, 99, [1000, 2000]]}}}
         data = self.data.filter_dataframe(filter_["filter"])
         self.assertEqual(data.shape, (1002, 102))
 
     def test_filter_annotation(self):
         filter_ = {
-            "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
-                    ]
-                }
-            }
+            "filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["NK cells", "CD8 T cells"]}]}}
         }
         data = self.data.filter_dataframe(filter_["filter"])
         self.assertEqual(data.shape, (470, 1838))
-        filter_ = {
-            "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "n_counts", "min": 3000},
-                    ]
-                }
-            }
-        }
+        filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
         data = self.data.filter_dataframe(filter_["filter"])
         self.assertEqual(data.shape, (497, 1838))
 
     def test_filter_annotation_no_uns(self):
-        filter_ = {
-            "filter": {
-                "var": {
-                    "annotation_value": [
-                        {"name": "name", "values": ["RER1"]},
-                    ]
-                }
-            }
-        }
+        filter_ = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
         data = self.data.filter_dataframe(filter_["filter"])
         self.assertEqual(data.shape[1], 1)
 
     def test_filter_complex(self):
         filter_ = {
             "filter": {
-                "var": {
-                    "index": [1, 99, [200, 300]]
-                },
+                "var": {"index": [1, 99, [200, 300]]},
                 "obs": {
                     "annotation_value": [
                         {"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
                         {"name": "n_counts", "min": 3000},
                     ],
-                    "index": [1, 99, [1000, 2000]]
-                }
+                    "index": [1, 99, [1000, 2000]],
+                },
             }
         }
         data = self.data.filter_dataframe(filter_["filter"])
@@ -116,13 +89,14 @@ class UtilTest(unittest.TestCase):
             self.assertEqual(self.data.schema, schema)
 
     def test_schema_produces_error(self):
-        self.data.data.obs["time"] = Series(list([time.time() for i in range(self.data.cell_count)]),
-                                            dtype="datetime64[ns]")
+        self.data.data.obs["time"] = Series(
+            list([time.time() for i in range(self.data.cell_count)]), dtype="datetime64[ns]"
+        )
         with pytest.raises(TypeError):
             self.data._create_schema()
 
     def test_config(self):
-        self.assertEqual(self.data.features["layout"]["obs"], {'available': True, 'interactiveLimit': 50000})
+        self.assertEqual(self.data.features["layout"]["obs"], {"available": True, "interactiveLimit": 50000})
 
     def test_layout(self):
         layout = self.data.layout(None)
@@ -152,16 +126,8 @@ class UtilTest(unittest.TestCase):
     def test_filtered_annotation(self):
         filter_ = {
             "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "n_counts", "min": 3000},
-                    ]
-                },
-                "var": {
-                    "annotation_value": [
-                        {"name": "name", "values": ["ATAD3C", "RER1"]},
-                    ]
-                }
+                "obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]},
+                "var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]},
             }
         }
         annotations = self.data.annotation(filter_["filter"], "obs")
@@ -172,33 +138,13 @@ class UtilTest(unittest.TestCase):
         self.assertEqual(len(annotations["data"]), 2)
 
     def test_filtered_layout(self):
-        filter_ = {
-            "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "n_counts", "min": 3000},
-                    ]
-                }
-            }
-        }
+        filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
         layout = self.data.layout(filter_["filter"])
         self.assertEqual(len(layout["coordinates"]), 497)
 
     def test_diffexp_topN(self):
-        f1 = {
-            "filter": {
-                "obs": {
-                    "index": [[0, 500]]
-                }
-            }
-        }
-        f2 = {
-            "filter": {
-                "obs": {
-                    "index": [[500, 1000]]
-                }
-            }
-        }
+        f1 = {"filter": {"obs": {"index": [[0, 500]]}}}
+        f2 = {"filter": {"obs": {"index": [[500, 1000]]}}}
         result = self.data.diffexp_topN(f1["filter"], f2["filter"])
         self.assertEqual(len(result), 10)
         result = self.data.diffexp_topN(f1["filter"], f2["filter"], 20)
@@ -213,15 +159,7 @@ class UtilTest(unittest.TestCase):
         self.assertEqual(len(data_frame_var["obs"]), 2638)
 
     def test_filtered_data_frame(self):
-        filter_ = {
-            "filter": {
-                "obs": {
-                    "annotation_value": [
-                        {"name": "n_counts", "min": 3000},
-                    ]
-                }
-            }
-        }
+        filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
         data_frame_obs = self.data.data_frame(filter_["filter"], "obs")
         self.assertEqual(len(data_frame_obs["var"]), 1838)
         self.assertEqual(len(data_frame_obs["obs"]), 497)
@@ -235,15 +173,7 @@ class UtilTest(unittest.TestCase):
 
     def test_data_single_gene(self):
         for axis in ["obs", "var"]:
-            filter_ = {
-                "filter": {
-                    "var": {
-                        "annotation_value": [
-                            {"name": "name", "values": ["RER1"]},
-                        ]
-                    }
-                }
-            }
+            filter_ = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
             data_frame_var = self.data.data_frame(filter_["filter"], axis)
             if axis == "obs":
                 self.assertEqual(type(data_frame_var["var"][0]), int)
@@ -252,5 +182,5 @@ class UtilTest(unittest.TestCase):
                 self.assertEqual(type(data_frame_var["obs"][0]), int)
                 self.assertIsInstance(data_frame_var["var"][0], (list, tuple))
 
-    if __name__ == '__main__':
+    if __name__ == "__main__":
         unittest.main()

setup.cfg

@@ -1,2 +1,3 @@
 [flake8]
 max-line-length = 120
+ignore = E203

setup.py

@@ -1,4 +1,13 @@
 from setuptools import setup, find_packages
+import sys
+
+if sys.version_info[0:2] != (3, 6):
+    raise ImportError(
+        "cellxgene currently only supports python 3.6. Python 3.7 is known to fail; we will look at supporting "
+        "versions other than 3.6 in the future."
+        "See https://github.com/chanzuckerberg/cellxgene#conda-and-virtual-environments "
+        "for more help with installation."
+    )
 
 with open("README.md", "rb") as fh:
     long_description = fh.read().decode()
@@ -8,7 +17,7 @@ with open("server/requirements.txt") as fh:
 
 setup(
     name="cellxgene",
-    version="0.2.3",
+    version="0.4.0",
     packages=find_packages(),
     url="https://github.com/chanzuckerberg/cellxgene",
     license="MIT",
@@ -16,19 +25,24 @@ setup(
     author_email="cweaver@chanzuckerberg.com",
     description="Web application for exploration of large scale scRNA-seq datasets",
     long_description=long_description,
-    long_description_content_type='text/markdown',
+    long_description_content_type="text/markdown",
     install_requires=requirements,
     include_package_data=True,
     zip_safe=False,
-    classifiers=(
-        "Programming Language :: Python :: 3",
+    classifiers=[
+        "Framework :: Flask",
+        "Intended Audience :: Science/Research",
         "License :: OSI Approved :: MIT License",
-    ),
-    entry_points={
-        "console_scripts":
-            ["cellxgene = server.cli.cli:cli"]
-    },
-    extras_require=dict(
-        louvain=['python-igraph', 'louvain>=0.6'],
-    ),
+        "Natural Language :: English",
+        "Operating System :: POSIX",
+        "Operating System :: Unix",
+        "Operating System :: MacOS :: MacOS X",
+        "Programming Language :: JavaScript",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3 :: Only",
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
+    ],
+    entry_points={"console_scripts": ["cellxgene = server.cli.cli:cli"]},
+    extras_require=dict(louvain=["python-igraph", "louvain>=0.6"]),
 )