pax_global_header00006660000000000000000000000064145212326030014510gustar00rootroot0000000000000052 comment=55142043d19696ba037e3e93f9ec6c7f8436e82d gophernotes/000077500000000000000000000000001452123260300134115ustar00rootroot00000000000000gophernotes/.gitignore000066400000000000000000000000571452123260300154030ustar00rootroot00000000000000gophernotes .ipynb_checkpoints Untitled*.ipynb gophernotes/.travis.yml000066400000000000000000000004061452123260300155220ustar00rootroot00000000000000language: go go: - 1.15.x - 1.14.x - 1.13.x - master matrix: fast_finish: true allow_failures: - go: master sudo: true before_install: - sudo apt-get install build-essential python3-pip script: - go test -v ./... - go test -race -v ./... gophernotes/CONTRIBUTORS000066400000000000000000000021531452123260300152720ustar00rootroot00000000000000# This is the official list of people who can contribute # (and typically have contributed) code to the gophernotes # repository. # # Names should be added to this file only after verifying that # the individual or the individual's organization has agreed to # the appropriate Contributor License Agreement, found here: # # http://code.google.com/legal/individual-cla-v1.0.html # http://code.google.com/legal/corporate-cla-v1.0.html # # The agreement for individuals can be filled out on the web. # # Names should be added to this file like so: # Name # # Please keep the list sorted. Bobby Norton Dan Kortschak Daniel Whitenack Fransesc Campoy Harry Moreno Josh Cheek Kevin Burke Massimiliano Ghilardi Matthew Steffen Sebastien Binet Spencer Park Thomas Kluyver Yang Yang Yasuhiro Matsumoto gophernotes/Dockerfile000066400000000000000000000031301452123260300154000ustar00rootroot00000000000000FROM alpine:3.15 MAINTAINER dwhitena # Add gophernotes ADD . /go/src/github.com/gopherdata/gophernotes/ # Install Jupyter and gophernotes. RUN set -x \ # install python and dependencies && apk update \ && apk --no-cache \ --repository http://dl-4.alpinelinux.org/alpine/v3.15/community \ --repository http://dl-4.alpinelinux.org/alpine/v3.15/main \ --arch=x86_64 add \ ca-certificates \ g++ \ gcc \ git \ libffi-dev \ pkgconfig \ python3 python3-dev \ py3-pip \ py3-pyzmq \ mercurial \ mesa-dev \ musl-dev \ su-exec \ zeromq-dev \ && pip3 install --upgrade pip==21.3.1 \ && ln -s /usr/bin/python3.9 /usr/bin/python \ ## install Go && apk --update-cache \ --arch=x86_64 add \ go \ ## jupyter notebook && ln -s /usr/include/locale.h /usr/include/xlocale.h \ && pip3 install jupyter notebook pyzmq tornado ipykernel \ ## install gophernotes && cd /go/src/github.com/gopherdata/gophernotes \ && GOPATH=/go GO111MODULE=on go install . \ && cp /go/bin/gophernotes /usr/local/bin/ \ && mkdir -p ~/.local/share/jupyter/kernels/gophernotes \ && cp -r ./kernel/* ~/.local/share/jupyter/kernels/gophernotes \ && cd - \ ## clean && find /usr/lib/python3.9 -name __pycache__ | xargs rm -r \ && rm -rf \ /root/.[acpw]* \ ipaexg00301* \ && rm -rf /var/cache/apk/* # Set GOPATH. ENV GOPATH /go EXPOSE 8888 CMD [ "jupyter", "notebook", "--no-browser", "--allow-root", "--ip=0.0.0.0" ] gophernotes/Dockerfile.DS000066400000000000000000000046741452123260300157230ustar00rootroot00000000000000FROM alpine:3.15 MAINTAINER dwhitena # Add gophernotes ADD . /go/src/github.com/gopherdata/gophernotes/ # Install Jupyter and gophernotes. RUN set -x \ # install python and dependencies && apk update \ && apk --no-cache \ --repository http://dl-4.alpinelinux.org/alpine/v3.15/community \ --repository http://dl-4.alpinelinux.org/alpine/v3.15/main \ --arch=x86_64 add \ ca-certificates \ g++ \ gcc \ git \ libffi-dev \ pkgconfig \ python3 python3-dev \ py3-pip \ py3-pyzmq \ mercurial \ mesa-dev \ musl-dev \ su-exec \ zeromq-dev \ && pip3 install --upgrade pip==21.3.1 \ && cp /usr/bin/python3.9 /usr/bin/python \ ## install Go && apk --update-cache \ --arch=x86_64 add \ go \ ## jupyter notebook && ln -s /usr/include/locale.h /usr/include/xlocale.h \ && pip3 install jupyter notebook pyzmq tornado ipykernel \ ## install gophernotes && cd /go/src/github.com/gopherdata/gophernotes \ && export GOPATH=/go \ && export GO111MODULE=on \ && go install . \ && cp /go/bin/gophernotes /usr/local/bin/ \ && mkdir -p ~/.local/share/jupyter/kernels/gophernotes \ && cp -r ./kernel/* ~/.local/share/jupyter/kernels/gophernotes \ && cd - \ ## get the relevant Go packages && go get gonum.org/v1/plot/... \ && go get gonum.org/v1/gonum/... \ && go get github.com/go-gota/gota/... \ && go get github.com/sajari/regression \ && go get github.com/sjwhitworth/golearn/... \ && go get go-hep.org/x/hep/csvutil/... \ && go get go-hep.org/x/hep/fit \ && go get go-hep.org/x/hep/hbook \ && go get github.com/montanaflynn/stats \ && go get github.com/boltdb/bolt \ && go get github.com/patrickmn/go-cache \ && go get github.com/chewxy/math32 \ && go get github.com/chewxy/hm \ && go get gorgonia.org/vecf64 \ && go get gorgonia.org/vecf32 \ && go get github.com/awalterschulze/gographviz \ && go get github.com/leesper/go_rng \ && go get github.com/pkg/errors \ && go get github.com/stretchr/testify/assert \ ## clean && find /usr/lib/python3.9 -name __pycache__ | xargs rm -r \ && rm -rf \ /root/.[acpw]* \ ipaexg00301* \ && rm -rf /var/cache/apk/* # Set GOPATH. ENV GOPATH /go EXPOSE 8888 CMD [ "jupyter", "notebook", "--no-browser", "--allow-root", "--ip=0.0.0.0" ] gophernotes/Gopkg.lock000066400000000000000000000023431452123260300153340ustar00rootroot00000000000000# This file is autogenerated, do not edit; changes may be undone by the next 'dep ensure'. [[projects]] branch = "stable" name = "github.com/cosmos72/gomacro" packages = [ "ast2", "atomic", "base", "base/dep", "base/untyped", "fast", "gls", "imports", "imports/syscall", "imports/thirdparty", "parser", "scanner", "token", "typeutil", "xreflect" ] revision = "515faf9893ffc98ff66aab992cefbd8065aab308" [[projects]] name = "github.com/mattn/go-runewidth" packages = ["."] revision = "9e777a8366cce605130a531d2cd6363d07ad7317" version = "v0.0.2" [[projects]] branch = "master" name = "github.com/pebbe/zmq4" packages = ["."] revision = "90d69e412a09549f2e90bac70fbb449081f1e5c1" [[projects]] branch = "master" name = "github.com/peterh/liner" packages = ["."] revision = "80ce870644db1b043e8dc2cf836bde5c347e2701" [[projects]] branch = "master" name = "github.com/satori/go.uuid" packages = ["."] revision = "36e9d2ebbde5e3f13ab2e25625fd453271d6522e" [solve-meta] analyzer-name = "dep" analyzer-version = 1 inputs-digest = "5c1eb9af5482e159ee019ac0c87aaf5d16822f45a866946aefea60eb0a29c157" solver-name = "gps-cdcl" solver-version = 1 gophernotes/Gopkg.toml000066400000000000000000000013601452123260300153550ustar00rootroot00000000000000 # Gopkg.toml example # # Refer to https://github.com/golang/dep/blob/master/docs/Gopkg.toml.md # for detailed Gopkg.toml documentation. # # required = ["github.com/user/thing/cmd/thing"] # ignored = ["github.com/user/project/pkgX", "bitbucket.org/user/project/pkgA/pkgY"] # # [[constraint]] # name = "github.com/user/project" # version = "1.0.0" # # [[constraint]] # name = "github.com/user/project2" # branch = "dev" # source = "github.com/myfork/project2" # # [[override]] # name = "github.com/x/y" # version = "2.4.0" [[constraint]] branch = "stable" name = "github.com/cosmos72/gomacro" [[constraint]] branch = "master" name = "github.com/satori/go.uuid" [[constraint]] branch = "master" name = "github.com/pebbe/zmq4" gophernotes/LICENSE000066400000000000000000000020531452123260300144160ustar00rootroot00000000000000The MIT License (MIT) Copyright (c) 2016 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. gophernotes/README.md000066400000000000000000000270261452123260300146770ustar00rootroot00000000000000![alt tag](files/gophernotes-logo.png) [![Build Status](https://travis-ci.org/gopherdata/gophernotes.svg?branch=master)](https://travis-ci.org/gopherdata/gophernotes) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/gopherdata/gophernotes/blob/master/LICENSE) # gophernotes - Use Go in Jupyter notebooks and nteract `gophernotes` is a Go kernel for [Jupyter](http://jupyter.org/) notebooks and [nteract](https://nteract.io/). It lets you use Go interactively in a browser-based notebook or desktop app. Use `gophernotes` to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the [Jupyter Notebook Viewer](http://nbviewer.jupyter.org/). Go forth and do data science, or anything else interesting, with Go notebooks! **Acknowledgements** - This project utilizes a Go interpreter called [gomacro](https://github.com/cosmos72/gomacro) under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant [Marcus Olsson](https://github.com/marcusolsson) and was inspired by Renee French's original Go Gopher design. - [Examples](#examples) - Install gophernotes: - [Prerequisites](#prerequisites) - [FreeBSD](#linux-or-freebsd) - [Linux](#linux-or-freebsd) - [Mac](#mac) - [Windows](#windows) - [Docker](#docker) - [Getting Started](#getting-started) - [Limitations](#limitations) - [Troubleshooting](#troubleshooting) ## Examples ### Jupyter Notebook: ![](files/jupyter.gif) ### nteract: ![](files/nteract.gif) ### Example Notebooks (download and run them locally, follow the links to view in Github, or use the [Jupyter Notebook Viewer](http://nbviewer.jupyter.org/)): - [Worker Pools](examples/Worker_Pools.ipynb) - [Matrix Operations](examples/Matrix_Operations.ipynb) - [Facial Recognition](examples/Facial_Recognition_MachineBox.ipynb) - [Display Images, HTML, LaTeX...](examples/Display.ipynb) ## Installation ### Prerequisites - [Go 1.13+](https://golang.org/doc/install) - including GOPATH/bin added to your PATH (i.e., you can run Go binaries that you `go install`). - [Jupyter Notebook](http://jupyter.readthedocs.io/en/latest/install.html) or [nteract](https://nteract.io/desktop) - [git](https://git-scm.com/download) - usually already present on Linux and Mac OS X. If not present, follow the instructions at [https://git-scm.com/download](https://git-scm.com/download) ### Linux or FreeBSD The instructions below should work both on Linux and on FreeBSD. Method 1: quick installation as module ```sh go install github.com/gopherdata/gophernotes@v0.7.5 mkdir -p ~/.local/share/jupyter/kernels/gophernotes cd ~/.local/share/jupyter/kernels/gophernotes cp "$(go env GOPATH)"/pkg/mod/github.com/gopherdata/gophernotes@v0.7.5/kernel/* "." chmod +w ./kernel.json # in case copied kernel.json has no write permission sed "s|gophernotes|$(go env GOPATH)/bin/gophernotes|" < kernel.json.in > kernel.json ``` Method 2: manual installation from GOPATH ```sh mkdir -p "$(go env GOPATH)"/src/github.com/gopherdata cd "$(go env GOPATH)"/src/github.com/gopherdata git clone https://github.com/gopherdata/gophernotes cd gophernotes git checkout -f v0.7.5 go install mkdir -p ~/.local/share/jupyter/kernels/gophernotes cp kernel/* ~/.local/share/jupyter/kernels/gophernotes cd ~/.local/share/jupyter/kernels/gophernotes chmod +w ./kernel.json # in case copied kernel.json has no write permission sed "s|gophernotes|$(go env GOPATH)/bin/gophernotes|" < kernel.json.in > kernel.json ``` To confirm that the `gophernotes` binary is installed in GOPATH, execute it directly: ```sh "$(go env GOPATH)"/bin/gophernotes ``` and you should see the following: ```sh 2017/09/20 10:33:12 Need a command line argument specifying the connection file. ``` **Note** - if you have the `JUPYTER_PATH` environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory. You can check which directories will be searched by executing: ```sh jupyter --data-dir ``` ### Mac **Important Note** - gomacro relies on the `plugin` package when importing third party libraries. This package works reliably on Mac OS X with Go 1.10.2+ as long as you **never** execute the command `strip gophernotes`. Method 1: quick installation as module ```sh go install github.com/gopherdata/gophernotes@v0.7.5 mkdir -p ~/Library/Jupyter/kernels/gophernotes cd ~/Library/Jupyter/kernels/gophernotes cp "$(go env GOPATH)"/pkg/mod/github.com/gopherdata/gophernotes@v0.7.5/kernel/* "." chmod +w ./kernel.json # in case copied kernel.json has no write permission sed "s|gophernotes|$(go env GOPATH)/bin/gophernotes|" < kernel.json.in > kernel.json ``` Method 2: manual installation from GOPATH ```sh mkdir -p "$(go env GOPATH)"/src/github.com/gopherdata cd "$(go env GOPATH)"/src/github.com/gopherdata git clone https://github.com/gopherdata/gophernotes cd gophernotes git checkout -f v0.7.5 go install mkdir -p ~/Library/Jupyter/kernels/gophernotes cp kernel/* ~/Library/Jupyter/kernels/gophernotes cd ~/Library/Jupyter/kernels/gophernotes chmod +w ./kernel.json # in case copied kernel.json has no write permission sed "s|gophernotes|$(go env GOPATH)/bin/gophernotes|" < kernel.json.in > kernel.json ``` To confirm that the `gophernotes` binary is installed in GOPATH, execute it directly: ```sh "$(go env GOPATH)"/bin/gophernotes ``` and you should see the following: ```sh 2017/09/20 10:33:12 Need a command line argument specifying the connection file. ``` **Note** - if you have the `JUPYTER_PATH` environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory. You can check which directories will be searched by executing: ```sh jupyter --data-dir ``` ### Windows **Important Note** - gomacro relies on the `plugin` package when importing third party libraries. This package is only supported on Linux and Mac OS X currently. Thus, if you need to utilize third party packages in your Go notebooks and you are running on Windows, you should use the [Docker](#docker) install and run gophernotes/Jupyter in Docker. 1. Download gophernotes inside GOPATH, compile and install it ``` go env GOPATH > temp.txt set /p GOPATH= Go. - Have fun! ## Special commands In addition to Go code, the following special commands are also supported - they must be on a line by their own: - %cd [path] - %go111module {on|off} - %help - $ shell_command [args...] ## Limitations gophernotes uses [gomacro](https://github.com/cosmos72/gomacro) under the hood to evaluate Go code interactively. You can evaluate most any Go code with gomacro, but there are some limitations, which are discussed in further detail [here](https://github.com/cosmos72/gomacro#current-status). Most notably, gophernotes does NOT support: - importing third party packages when running natively on Windows - This is a current limitation of the Go `plugin` package. - some corner cases on interpreted interfaces, as interface -> interface type switch and type assertion, are not implemented yet. - some corner cases on recursive types may not work correctly. - conversion from typed constant to interpreted interface is not implemented. Workaround: assign the constant to a variable, then convert the variable to the interpreted interface type. - conversions from/to unsafe.Pointer are not supported. - goto is only partially implemented. - out-of-order code in the same cell is supported, but not heavily tested. It has some known limitations for composite literals. Also, creation of new named types is emulated, and their methods are visible only to interpreted code. ## Troubleshooting ### gophernotes not found Depending on your environment, you may need to manually change the path to the `gophernotes` executable in `kernel/kernel.json` before copying it to `~/.local/share/jupyter/kernels/gophernotes`. You can put the **full path** to the `gophernotes` executable here, and you shouldn't have any further issues. ### "Kernel error" in a running notebook ``` Traceback (most recent call last): File "/usr/local/lib/python2.7/site-packages/notebook/base/handlers.py", line 458, in wrapper result = yield gen.maybe_future(method(self, *args, **kwargs)) File "/usr/local/lib/python2.7/site-packages/tornado/gen.py", line 1008, in run value = future.result() ... File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.py", line 1335, in _execute_child raise child_exception OSError: [Errno 2] No such file or directory ``` Stop jupyter, if it's already running. Add a symlink to `/go/bin/gophernotes` from your path to the gophernotes executable. If you followed the instructions above, this will be: ``` sudo ln -s $HOME/go/bin/gophernotes /go/bin/gophernotes ``` Restart jupyter, and you should now be up and running. ### error "could not import C (no metadata for C)" when importing a package At a first analysis, it seems to be a limitation of the new import mechanism that supports Go modules. You can switch the old (non module-aware) mechanism with the command `%go111module off` To re-enable modules support, execute `%go111module on` ### Look at Jupyter notebook's logs for debugging In order to see the logs for your Jupyter notebook, use the --log-level option ``` jupyter notebook --log-level DEBUG ``` gophernotes/codemeta.json000066400000000000000000000010111452123260300160560ustar00rootroot00000000000000{ "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld", "@type": "Code", "author": [ ], "identifier": "", "codeRepository": "https://github.com/gopherdata/gophernotes", "datePublished": "2017-12-13", "dateModified": "2017-12-13", "dateCreated": "2017-12-13", "description": "The Go kernel for Jupyter notebooks and nteract. ", "keywords": "go, jupyter, nteract, golang, data science", "license": "GPL v3.0", "title": "gophernotes", "version": "v1.0.0-RC" }gophernotes/compiler_version.go000066400000000000000000000002311452123260300173130ustar00rootroot00000000000000// +build !go1.11 // cause a compile error if Go compiler version < 1.11 package main var _ int = "error: Go >= 1.11 required to compile Gophernotes" gophernotes/complete.go000066400000000000000000000023041452123260300155470ustar00rootroot00000000000000package main import ( interp "github.com/cosmos72/gomacro/fast" ) type Completion struct { class, name, typ string } type CompletionResponse struct { partial int completions []Completion } /************************************************************ * entry function ************************************************************/ func handleCompleteRequest(ir *interp.Interp, receipt msgReceipt) error { // Extract the data from the request. reqcontent := receipt.Msg.Content.(map[string]interface{}) code := reqcontent["code"].(string) cursorPos := int(reqcontent["cursor_pos"].(float64)) // autocomplete the code at the cursor position prefix, matches, _ := ir.CompleteWords(code, cursorPos) // prepare the reply content := make(map[string]interface{}) if len(matches) == 0 { content["ename"] = "ERROR" content["evalue"] = "no completions found" content["traceback"] = nil content["status"] = "error" } else { partialWord := interp.TailIdentifier(prefix) content["cursor_start"] = float64(len(prefix) - len(partialWord)) content["cursor_end"] = float64(cursorPos) content["matches"] = matches content["status"] = "ok" } return receipt.Reply("complete_reply", content) } gophernotes/display.go000066400000000000000000000257221452123260300154150ustar00rootroot00000000000000package main import ( "bytes" "errors" "fmt" "image" "io" "io/ioutil" "net/http" "reflect" "strings" basereflect "github.com/cosmos72/gomacro/base/reflect" "github.com/cosmos72/gomacro/xreflect" ) // Support an interface similar - but not identical - to the IPython (canonical Jupyter kernel). // See http://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html#IPython.display.display // for a good overview of the support types. const ( MIMETypeHTML = "text/html" MIMETypeJavaScript = "application/javascript" MIMETypeJPEG = "image/jpeg" MIMETypeJSON = "application/json" MIMETypeLatex = "text/latex" MIMETypeMarkdown = "text/markdown" MIMETypePNG = "image/png" MIMETypePDF = "application/pdf" MIMETypeSVG = "image/svg+xml" MIMETypeText = "text/plain" ) /** * general interface, allows libraries to fully specify * how their data is displayed by Jupyter. * Supports multiple MIME formats. * * Note that Data defined above is an alias: * libraries can implement Renderer without importing gophernotes */ type Renderer = interface { Render() Data } /** * simplified interface, allows libraries to specify * how their data is displayed by Jupyter. * Supports multiple MIME formats. * * Note that MIMEMap defined above is an alias: * libraries can implement SimpleRenderer without importing gophernotes */ type SimpleRenderer = interface { SimpleRender() MIMEMap } /** * specialized interfaces, each is dedicated to a specific MIME type. * * They are type aliases to emphasize that method signatures * are the only important thing, not the interface names. * Thus libraries can implement them without importing gophernotes */ type HTMLer = interface { HTML() string } type JavaScripter = interface { JavaScript() string } type JPEGer = interface { JPEG() []byte } type JSONer = interface { JSON() map[string]interface{} } type Latexer = interface { Latex() string } type Markdowner = interface { Markdown() string } type PNGer = interface { PNG() []byte } type PDFer = interface { PDF() []byte } type SVGer = interface { SVG() string } // injected as placeholder in the interpreter, it's then replaced at runtime // by a closure that knows how to talk with Jupyter func stubDisplay(Data) error { return errors.New("cannot display: connection with Jupyter not available") } // fill kernel.renderer map used to convert interpreted types // to known rendering interfaces func (kernel *Kernel) initRenderers() { kernel.render = make(map[string]xreflect.Type) for name, typ := range kernel.display.Types { if typ.Kind() == reflect.Interface { kernel.render[name] = typ } } } // if vals[] contain a single non-nil value which is auto-renderable, // convert it to Data and return it. // otherwise return MakeData("text/plain", fmt.Sprint(vals...)) func (kernel *Kernel) autoRenderResults(vals []interface{}, types []xreflect.Type) Data { var nilcount int var obj interface{} var typ xreflect.Type for i, val := range vals { if kernel.canAutoRender(val, types[i]) { obj = val typ = types[i] } else if val == nil { nilcount++ } } if obj != nil && nilcount == len(vals)-1 { return kernel.autoRender("", obj, typ) } if nilcount == len(vals) { // if all values are nil, return empty Data return Data{} } return MakeData(MIMETypeText, anyToString(vals...)) } func anyToString(vals ...interface{}) string { var buf strings.Builder for i, val := range vals { if i != 0 { buf.WriteByte(' ') } fmt.Fprint(&buf, val) } return buf.String() } // return true if data type should be auto-rendered graphically func (kernel *Kernel) canAutoRender(data interface{}, typ xreflect.Type) bool { switch data.(type) { case Data, Renderer, SimpleRenderer, HTMLer, JavaScripter, JPEGer, JSONer, Latexer, Markdowner, PNGer, PDFer, SVGer, image.Image: return true } if kernel == nil || typ == nil { return false } // in gomacro, methods of interpreted types are emulated, // thus type-asserting them to interface types as done above cannot succeed. // Manually check if emulated type "pretends" to implement // at least one of the interfaces above for _, xtyp := range kernel.render { if typ.Implements(xtyp) { return true } } return false } var autoRenderers = map[string]func(Data, interface{}) Data{ "Renderer": func(d Data, i interface{}) Data { if r, ok := i.(Renderer); ok { x := r.Render() d.Data = merge(d.Data, x.Data) d.Metadata = merge(d.Metadata, x.Metadata) d.Transient = merge(d.Transient, x.Transient) } return d }, "SimpleRenderer": func(d Data, i interface{}) Data { if r, ok := i.(SimpleRenderer); ok { x := r.SimpleRender() d.Data = merge(d.Data, x) } return d }, "HTMLer": func(d Data, i interface{}) Data { if r, ok := i.(HTMLer); ok { d.Data = ensure(d.Data) d.Data[MIMETypeHTML] = r.HTML() } return d }, "JavaScripter": func(d Data, i interface{}) Data { if r, ok := i.(JavaScripter); ok { d.Data = ensure(d.Data) d.Data[MIMETypeJavaScript] = r.JavaScript() } return d }, "JPEGer": func(d Data, i interface{}) Data { if r, ok := i.(JPEGer); ok { d.Data = ensure(d.Data) d.Data[MIMETypeJPEG] = r.JPEG() } return d }, "JSONer": func(d Data, i interface{}) Data { if r, ok := i.(JSONer); ok { d.Data = ensure(d.Data) d.Data[MIMETypeJSON] = r.JSON() } return d }, "Latexer": func(d Data, i interface{}) Data { if r, ok := i.(Latexer); ok { d.Data = ensure(d.Data) d.Data[MIMETypeLatex] = r.Latex() } return d }, "Markdowner": func(d Data, i interface{}) Data { if r, ok := i.(Markdowner); ok { d.Data = ensure(d.Data) d.Data[MIMETypeMarkdown] = r.Markdown() } return d }, "PNGer": func(d Data, i interface{}) Data { if r, ok := i.(PNGer); ok { d.Data = ensure(d.Data) d.Data[MIMETypePNG] = r.PNG() } return d }, "PDFer": func(d Data, i interface{}) Data { if r, ok := i.(PDFer); ok { d.Data = ensure(d.Data) d.Data[MIMETypePDF] = r.PDF() } return d }, "SVGer": func(d Data, i interface{}) Data { if r, ok := i.(SVGer); ok { d.Data = ensure(d.Data) d.Data[MIMETypeSVG] = r.SVG() } return d }, "Image": func(d Data, i interface{}) Data { if r, ok := i.(image.Image); ok { b, mimeType, err := encodePng(r) if err != nil { d = makeDataErr(err) } else { d.Data = ensure(d.Data) d.Data[mimeType] = b d.Metadata = merge(d.Metadata, imageMetadata(r)) } } return d }, } // detect and render data types that should be auto-rendered graphically func (kernel *Kernel) autoRender(mimeType string, arg interface{}, typ xreflect.Type) Data { var data Data // try Data if x, ok := arg.(Data); ok { data = x } if kernel == nil || typ == nil { // try all autoRenderers for _, fun := range autoRenderers { data = fun(data, arg) } } else { // in gomacro, methods of interpreted types are emulated. // Thus type-asserting them to interface types as done by autoRenderer functions above cannot succeed. // Manually check if emulated type "pretends" to implement one or more of the above interfaces // and, in case, tell the interpreter to convert to them for name, xtyp := range kernel.render { fun := autoRenderers[name] if fun == nil || !typ.Implements(xtyp) { continue } conv := kernel.ir.Comp.Converter(typ, xtyp) x := arg if conv != nil { x = basereflect.ValueInterface(conv(xreflect.ValueOf(x))) if x == nil { continue } } data = fun(data, x) } } return fillDefaults(data, arg, "", nil, "", nil) } func fillDefaults(data Data, arg interface{}, s string, b []byte, mimeType string, err error) Data { if err != nil { return makeDataErr(err) } if data.Data == nil { data.Data = make(MIMEMap) } // cannot autodetect the mime type of a string if len(s) != 0 && len(mimeType) != 0 { data.Data[mimeType] = s } // ensure plain text is set if data.Data[MIMETypeText] == "" { if len(s) == 0 { s = fmt.Sprint(arg) } data.Data[MIMETypeText] = s } // if []byte is available, use it if len(b) != 0 { if len(mimeType) == 0 { mimeType = http.DetectContentType(b) } if len(mimeType) != 0 && mimeType != MIMETypeText { data.Data[mimeType] = b } } return data } // do our best to render data graphically func render(mimeType string, data interface{}) Data { var kernel *Kernel // intentionally nil if kernel.canAutoRender(data, nil) { return kernel.autoRender(mimeType, data, nil) } var s string var b []byte var err error switch data := data.(type) { case string: s = data case []byte: b = data case io.Reader: b, err = ioutil.ReadAll(data) case io.WriterTo: var buf bytes.Buffer data.WriteTo(&buf) b = buf.Bytes() default: panic(fmt.Errorf("unsupported type, cannot render: %T", data)) } return fillDefaults(Data{}, data, s, b, mimeType, err) } func makeDataErr(err error) Data { return Data{ Data: MIMEMap{ "ename": "ERROR", "evalue": err.Error(), "traceback": nil, "status": "error", }, } } func Any(mimeType string, data interface{}) Data { return render(mimeType, data) } // same as Any("", data), autodetects MIME type func Auto(data interface{}) Data { return render("", data) } func MakeData(mimeType string, data interface{}) Data { d := Data{ Data: MIMEMap{ mimeType: data, }, } if mimeType != MIMETypeText { d.Data[MIMETypeText] = fmt.Sprint(data) } return d } func MakeData3(mimeType string, plaintext string, data interface{}) Data { return Data{ Data: MIMEMap{ MIMETypeText: plaintext, mimeType: data, }, } } func File(mimeType string, path string) Data { bytes, err := ioutil.ReadFile(path) if err != nil { panic(err) } return Any(mimeType, bytes) } func HTML(html string) Data { return MakeData(MIMETypeHTML, html) } func JavaScript(javascript string) Data { return MakeData(MIMETypeJavaScript, javascript) } func JPEG(jpeg []byte) Data { return MakeData(MIMETypeJPEG, jpeg) } func JSON(json map[string]interface{}) Data { return MakeData(MIMETypeJSON, json) } func Latex(latex string) Data { return MakeData3(MIMETypeLatex, latex, "$"+strings.Trim(latex, "$")+"$") } func Markdown(markdown string) Data { return MakeData(MIMETypeMarkdown, markdown) } func Math(latex string) Data { return MakeData3(MIMETypeLatex, latex, "$$"+strings.Trim(latex, "$")+"$$") } func PDF(pdf []byte) Data { return MakeData(MIMETypePDF, pdf) } func PNG(png []byte) Data { return MakeData(MIMETypePNG, png) } func SVG(svg string) Data { return MakeData(MIMETypeSVG, svg) } // MIME encapsulates the data and metadata into a Data. // The 'data' map is expected to contain at least one {key,value} pair, // with value being a string, []byte or some other JSON serializable representation, // and key equal to the MIME type of such value. // The exact structure of value is determined by what the frontend expects. // Some easier-to-use functions for common formats supported by the Jupyter frontend // are provided by the various functions above. func MIME(data, metadata MIMEMap) Data { return Data{data, metadata, nil} } gophernotes/examples/000077500000000000000000000000001452123260300152275ustar00rootroot00000000000000gophernotes/examples/Display.ipynb000066400000000000000000033121361452123260300177100ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

The display package

\n", "

\n", "The package `github.com/gopherdata/gophernotes` is automatically imported with the name `display`.\n", "

\n", "It provides several functions that convert `string`, `[]byte`, `image.Image`, `io.Reader` or `io.WriterTo` containing images, HTML, Markdown, LaTeX etc. to `display.Data`, which is shown graphically if returned at prompt. Example:\n", "

" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Hello, World

" ], "text/plain": [ "

Hello, World

" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display.HTML(`

Hello, World

`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Display function

\n", "

\n", "Sometimes, returning `display.Data` at prompt is not handy - for example from deep inside functions or blocks, or because you want to display multiple data at once.\n", "

\n", "In such cases, you can call the function `Display(display.Data) error` to display the data immediately:\n", "

" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "* hello from markdown" ], "text/plain": [ "* hello from markdown" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$e^{i\\pi}+1=0$$" ], "text/plain": [ "e^{i\\pi}+1=0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "{\n", " Display(display.Markdown(\"* hello from markdown\"))\n", " Display(display.Math(`e^{i\\pi}+1=0`))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Images

\n", "

\n", "The functions to display JPEG, PNG or SVG images and `image.Image` are:\n", "

" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import \"image\"\n", "// in package \"display\"\n", "func JPEG([]byte) error\n", "func PNG([]byte) error\n", "func SVG(string) error\n", "func Image(image.Image) error" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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136 36 73 146 36 73 146 36 73 146 36 169 153 43 215 139 195 7 192 133 192 127 198 96 93 146 36 73 146 36 73 146 36 73 146 138 78 54 194 245 58 224 245 44 28 103 95 180 153 80 2 126 10 240 211 2 207 69 146 36 73 146 36 73 146 36 73 146 148 7 135 0 55 3 235 129 38 71 187 227 67 96 49 48 184 59 223 104 73 121 115 36 185 251 61 208 152 199 175 67 146 36 73 146 36 73 146 36 73 69 234 72 66 208 254 239 64 140 194 135 217 197 50 126 11 252 23 160 188 219 223 89 73 249 100 184 46 73 146 36 73 146 36 73 146 164 188 249 2 48 23 120 2 216 66 225 3 238 124 143 15 129 229 192 223 244 244 27 41 41 239 12 215 37 73 146 36 73 146 36 73 146 4 64 36 207 231 43 1 14 5 78 4 142 7 190 12 236 159 231 57 228 195 122 224 121 66 31 245 87 9 65 154 164 222 231 72 224 255 229 232 216 49 160 44 71 199 150 36 73 146 36 73 146 36 73 82 150 229 59 92 207 100 127 224 104 96 90 98 28 9 28 80 208 25 117 221 59 192 239 129 127 5 254 5 216 84 216 233 72 202 18 195 117 73 146 36 73 146 36 73 146 36 1 197 17 174 103 50 140 80 70 125 10 112 112 202 56 8 168 40 224 188 246 0 239 2 235 128 63 37 198 191 3 155 11 56 39 73 185 99 184 46 73 146 36 73 146 36 73 146 36 160 120 195 245 246 140 0 198 0 227 128 47 18 86 190 143 4 134 38 198 176 148 219 126 192 192 148 215 14 35 124 205 13 192 46 160 30 216 77 8 205 235 18 247 235 128 207 128 143 82 198 38 224 99 12 209 165 125 141 225 186 36 73 146 36 73 146 36 73 146 0 248 255 159 30 131 88 243 183 115 242 0 0 0 0 73 69 78 68 174 66 96 130]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Example: download and display a PNG\n", "import (\n", " \"net/http\"\n", " \"io/ioutil\"\n", ")\n", "resp, err := http.Get(\"https://github.com/gopherdata/gophernotes/raw/master/files/gophernotes-logo.png\")\n", "bytes, err := ioutil.ReadAll(resp.Body)\n", "resp.Body.Close()\n", "display.PNG(bytes)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "Group.svg\n", "Created using Figma 0.90\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "\n", "Group.svg\n", "Created using Figma 0.90\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// download and display an SVG\n", "resp, err := http.Get(\"https://jupyter.org/assets/homepage/main-logo.svg\")\n", "bytes, err := ioutil.ReadAll(resp.Body)\n", "resp.Body.Close()\n", "display.SVG(string(bytes))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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63 128 170 141 145 220 173 217 103 82 103 120 75 176 48 226 246 124 246 242 104 176 9 81 245 82 139 77 160 171 191 207 117 14 71 16 202 184 52 251 8 198 56 247 6 138 234 129 14 67 139 38 247 53 41 247 163 170 83 139 100 127 156 157 31 133 101 43 186 16 89 115 92 249 24 198 214 188 158 20 31 46 102 249 228 95 225 7 227 156 110 58 218 192 162 207 205 46 4 15 45 73 105 80 162 164 239 184 82 220 79 153 177 235 164 182 65 244 216 245 173 27 42 62 103 205 83 145 156 248 147 184 53 106 198 173 201 15 65 199 202 140 72 173 15 115 142 32 146 80 146 78 189 162 93 94 194 65 9 233 169 38 23 196 184 127 30 149 220 45 144 210 252 246 18 167 157 187 221 92 66 148 194 71 114 177 216 54 200 31 164 6 192 251 85 197 198 248 34 60 89 242 93 197 203 172 217 7 137 177 7 208 3 13 21 180 110 242 212 15 114 174 196 2 2 137 86 250 146 130 132 164 37 78 41 99 77 92 206 242 91 186 58 46 247 240 110 59 120 90 71 167 87 87 220 190 77 123 200 51 238 254 33 44 201 198 248 91 17 189 223 224 90 37 125 38 69 197 136 193 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132 30 227 220 85 175 179 81 101 70 201 178 14 77 192 174 121 205 153 54 156 102 124 75 132 252 118 205 44 171 207 66 152 108 20 203 144 222 128 243 20 74 84 144 118 0 3 182 187 171 107 169 69 74 42 81 36 147 178 79 190 189 171 78 86 235 119 46 47 87 241 243 244 240 60 165 53 93 244 227 193 104 190 126 14 69 201 144 226 122 84 251 171 30 224 167 9 169 87 132 200 75 151 155 115 94 68 131 184 146 111 145 45 13 235 211 174 19 10 74 191 214 166 162 41 81 66 146 161 234 146 8 175 215 7 231 67 133 249 22 126 35 127 97 106 196 51 235 218 231 89 238 201 246 140 91 171 193 41 92 71 254 75 233 64 66 190 67 215 169 101 189 189 152 227 26 207 60 90 208 212 218 74 82 164 177 193 50 231 210 148 174 148 231 69 41 74 1 74 82 128 82 148 160 20 165 40 5 41 74 1 84 23 17 142 109 153 7 41 89 150 217 96 91 115 187 146 153 107 244 24 120 133 181 175 145 9 39 241 171 245 84 151 53 130 187 15 137 142 76 134 254 146 140 130 211 107 189 196 64 26 246 26 79 209 86 126 242 190 162 127 254 85 126 208 143 74 221 249 96 223 176 151 70 225 121 228 236 87 212 169 72 80 82 9 74 146 118 8 58 32 215 202 87 42 116 230 175 207 173 174 241 210 110 25 238 12 250 32 201 121 230 91 186 88 214 209 92 59 178 220 112 37 42 74 19 162 219 222 209 87 82 117 189 18 52 74 130 164 208 57 6 214 229 217 203 14 82 203 248 78 86 193 9 126 205 124 33 133 117 30 192 180 241 210 29 65 63 85 93 138 135 112 8 239 93 217 22 209 149 114 207 15 226 106 87 75 82 47 238 95 36 251 61 64 183 1 162 226 18 161 240 82 186 211 223 181 92 172 211 143 113 110 69 182 11 110 115 96 183 223 98 39 171 203 76 182 2 212 209 80 209 83 107 250 205 171 95 105 36 31 157 94 91 218 70 230 130 148 222 189 207 203 242 82 215 186 149 189 119 24 45 59 215 153 85 156 142 243 32 23 90 90 1 27 4 164 128 71 200 251 235 136 105 93 135 122 216 79 248 46 195 161 188 181 225 57 86 113 133 177 234 220 43 85 244 253 25 7 227 210 234 86 163 248 170 184 26 240 134 235 219 69 215 152 57 22 67 39 236 199 185 54 194 191 210 232 85 96 246 93 76 233 36 102 182 156 49 172 89 9 92 117 176 202 159 125 31 71 97 35 106 117 226 27 64 31 53 43 64 126 218 133 63 201 214 23 174 73 179 226 40 155 157 228 11 73 83 118 204 113 131 40 145 250 74 120 14 132 164 118 218 129 86 183 178 43 126 90 252 22 113 115 15 38 70 82 221 251 55 150 133 133 161 251 253 229 215 148 147 189 250 54 91 73 31 34 14 235 119 227 120 157 135 14 183 254 79 196 172 182 235 20 30 174 163 30 223 17 17 208 85 239 81 74 0 4 252 207 122 158 158 203 138 214 114 207 166 132 21 54 156 159 98 56 42 246 33 225 167 43 228 57 113 238 60 245 33 139 70 58 210 195 172 97 150 151 250 210 226 210 173 164 204 144 15 231 53 163 236 32 145 220 16 81 221 39 21 225 63 45 177 112 238 83 201 28 63 154 204 137 143 94 34 228 174 205 182 25 139 12 34 107 46 165 8 64 108 171 73 223 66 26 90 83 190 165 37 222 192 244 171 87 58 181 207 40 112 78 1 204 72 97 89 254 60 197 198 92 100 116 49 49 183 22 196 134 211 223 73 14 32 133 20 130 165 16 133 109 59 59 214 234 222 20 227 78 61 24 44 34 170 115 149 73 116 164 242 201 189 210 249 108 177 193 84 235 213 198 29 182 18 19 212 169 18 164 33 166 210 62 37 74 32 106 180 190 71 227 35 133 113 169 79 196 145 155 71 159 37 159 84 219 162 63 45 10 255 0 53 212 32 182 175 193 85 87 252 57 248 98 227 236 239 147 57 142 217 149 219 102 92 45 56 142 66 109 246 182 12 231 27 5 176 244 148 16 226 145 210 165 29 52 223 112 71 191 227 87 103 19 225 30 57 193 153 142 222 45 133 88 160 46 58 122 81 35 232 40 114 70 191 89 229 130 226 143 204 168 154 204 192 213 16 60 120 112 172 199 203 114 47 151 27 122 63 173 145 105 124 167 251 129 71 247 84 251 30 241 53 196 25 51 30 117 183 145 49 246 81 250 51 229 136 43 255 0 65 254 133 126 234 208 223 193 253 143 218 50 94 9 191 71 200 173 80 46 204 12 166 72 13 204 138 135 147 163 22 41 214 148 8 247 214 200 230 239 13 60 95 112 227 108 190 225 109 192 172 176 111 48 108 115 95 183 187 110 143 244 46 137 8 97 106 109 68 50 82 21 165 4 157 40 16 125 224 208 19 76 215 196 127 23 96 182 57 119 91 166 105 102 155 228 55 214 136 86 217 205 74 148 249 251 41 109 164 40 146 73 237 179 164 141 237 74 72 217 173 11 225 191 130 173 156 173 131 231 89 151 50 99 13 175 249 70 189 170 237 18 35 225 73 92 120 253 107 91 110 178 231 103 27 234 83 206 104 130 58 155 9 61 210 190 252 190 15 248 7 140 50 62 32 196 51 123 206 29 2 229 146 72 76 164 200 126 99 142 200 109 74 110 83 173 165 69 133 168 180 15 74 19 232 142 222 181 114 104 10 135 122 240 217 201 88 42 63 233 97 150 196 205 108 173 125 75 62 88 10 101 54 128 7 178 212 198 245 212 73 216 1 65 9 72 215 173 64 238 57 14 81 139 169 77 231 252 85 153 216 150 218 122 157 147 110 140 155 180 54 211 241 47 52 64 31 118 201 21 126 41 170 210 169 101 66 171 203 142 31 145 185 78 242 189 61 19 202 243 60 237 111 156 184 225 197 150 215 148 162 43 163 178 145 38 221 41 5 39 224 116 217 27 252 107 146 79 54 241 204 93 121 153 116 87 73 244 76 120 114 93 39 246 55 175 223 94 132 73 136 196 198 203 82 217 109 246 207 216 113 1 67 246 26 225 133 105 129 109 42 54 248 81 162 21 125 111 37 148 163 127 126 133 106 255 0 197 209 255 0 103 246 248 54 127 228 234 248 47 191 201 70 108 179 51 156 249 104 103 139 184 230 239 33 167 52 69 235 38 108 219 32 33 4 232 56 148 147 214 242 71 169 8 61 95 170 107 117 241 159 133 184 150 155 212 44 187 151 174 223 199 172 194 41 11 134 218 218 13 219 109 138 208 237 30 63 161 33 91 33 197 1 220 37 65 41 80 221 88 122 86 237 27 90 84 53 130 215 196 211 173 115 86 182 146 122 21 87 197 84 117 69 229 78 17 187 41 33 49 76 171 181 177 215 79 245 146 99 161 45 35 241 33 122 168 184 173 225 226 115 142 230 242 39 20 205 70 62 84 50 59 4 150 175 182 80 158 253 82 227 117 40 35 95 104 169 10 113 0 122 117 41 36 250 85 125 199 114 40 217 126 61 106 200 32 36 54 197 210 48 127 203 74 182 26 94 202 92 111 127 170 180 169 63 133 84 237 74 109 78 53 62 133 174 204 168 156 101 15 169 146 168 71 50 195 84 238 42 202 3 74 45 191 13 150 103 48 226 78 148 219 141 60 131 212 147 238 61 37 99 127 58 155 212 91 147 92 75 92 103 153 169 122 215 228 119 147 220 251 201 72 31 188 138 170 162 218 171 22 188 87 50 206 170 205 57 39 224 249 23 163 28 188 35 33 199 173 55 118 71 75 119 24 108 202 64 30 224 226 2 135 248 214 78 162 28 80 194 226 241 118 16 195 192 165 214 172 16 16 176 125 196 71 64 53 47 174 212 227 133 41 74 1 74 82 128 82 184 164 73 102 35 11 126 83 173 176 203 99 107 113 197 4 165 35 226 73 236 43 150 128 82 148 160 20 165 40 5 84 143 17 177 227 199 241 11 199 18 162 104 78 155 143 93 227 79 35 212 199 109 30 99 64 252 131 138 89 31 58 182 245 74 57 10 236 156 187 196 206 93 61 147 213 15 17 176 69 176 161 65 93 72 114 75 235 47 184 161 238 5 41 42 66 135 174 210 55 90 151 146 81 183 158 124 57 155 86 145 114 175 12 120 156 180 165 43 144 58 195 179 195 104 76 159 21 108 34 66 66 145 19 142 221 122 54 254 202 213 112 74 84 161 243 209 82 126 225 87 38 169 22 47 41 203 55 136 222 37 184 50 175 45 187 164 123 181 158 97 214 250 219 75 62 123 105 255 0 214 16 127 10 187 181 214 216 180 237 227 143 221 78 86 245 53 113 44 254 232 41 74 86 233 166 41 74 80 10 26 82 128 170 158 13 79 211 111 156 227 119 7 168 78 206 101 123 95 29 41 106 255 0 238 85 171 170 55 133 242 3 158 14 121 11 50 197 121 94 211 53 56 110 77 121 122 237 100 200 225 176 94 66 186 251 20 57 173 108 132 4 133 37 35 169 42 7 217 82 22 149 9 62 91 227 22 103 32 42 70 41 225 111 26 186 229 185 68 134 250 69 217 216 126 84 72 9 81 3 206 41 115 91 35 184 5 222 132 5 116 147 214 54 146 7 207 224 228 73 28 31 125 39 237 101 82 8 255 0 217 98 213 165 203 124 191 226 181 239 233 26 12 254 79 145 230 111 211 167 203 86 234 149 113 226 121 103 193 108 39 108 119 236 44 242 14 5 53 209 57 219 134 60 22 183 97 72 82 16 29 216 41 234 40 1 191 182 148 36 251 36 44 119 72 200 231 62 47 156 231 12 86 237 129 240 14 15 147 221 50 91 228 117 65 125 233 145 218 105 184 76 59 236 45 194 164 56 176 14 137 72 82 202 18 146 160 162 163 211 210 64 217 126 4 252 207 250 28 49 239 51 125 63 75 157 209 191 209 250 66 253 63 29 213 146 168 23 10 113 194 56 147 139 113 156 57 47 137 46 219 34 145 33 228 146 82 183 220 90 156 116 167 96 30 159 49 106 233 216 222 181 186 158 208 10 82 148 2 148 165 0 165 41 64 13 121 243 198 16 145 103 178 228 86 38 0 17 172 57 109 222 217 28 15 115 109 186 130 7 237 89 171 241 121 187 67 176 90 39 221 174 207 8 240 45 241 156 149 37 213 122 54 211 105 42 82 143 200 0 77 80 110 37 250 76 156 25 171 205 205 191 38 118 73 114 155 124 125 0 246 73 144 239 109 124 138 91 73 251 136 170 173 166 214 229 103 196 180 217 169 239 158 60 9 173 66 57 90 44 139 238 53 3 14 181 171 166 235 153 221 226 89 162 157 19 208 146 234 92 113 213 1 223 161 33 40 234 248 5 110 167 9 73 82 130 82 9 81 58 0 123 205 119 188 61 216 15 33 115 101 239 51 112 121 152 246 6 211 150 59 58 199 213 118 226 234 119 45 212 157 250 165 10 242 251 130 10 86 130 15 106 168 177 165 189 174 188 22 165 173 237 93 213 23 226 244 45 252 118 27 138 195 108 71 64 109 150 146 16 132 39 209 41 3 64 15 192 87 37 41 93 97 203 10 82 148 2 148 165 1 73 121 147 149 27 241 33 30 14 41 133 217 231 14 62 143 119 109 251 198 67 49 102 59 55 22 216 90 183 30 50 1 234 113 42 58 61 125 186 74 70 192 214 234 109 225 99 44 184 88 114 92 147 137 110 243 222 185 64 180 67 102 235 140 61 33 93 79 55 109 113 65 38 58 213 234 67 75 40 74 79 126 196 142 201 8 72 214 211 177 183 56 139 149 47 124 123 33 165 53 96 188 58 245 239 15 119 164 134 252 181 158 169 16 146 125 54 218 182 82 145 223 164 108 247 112 10 200 225 247 47 226 183 137 30 56 186 245 37 168 217 21 182 126 57 45 213 157 0 83 252 229 148 255 0 156 167 10 82 42 170 53 234 171 221 220 248 119 115 201 104 232 83 118 123 200 241 239 229 130 238 82 148 171 82 172 82 155 175 203 142 33 166 214 227 170 8 109 0 169 74 81 208 0 122 146 104 12 14 115 151 65 192 176 235 238 79 120 63 204 173 16 157 150 234 66 130 75 157 9 36 33 36 246 234 81 210 64 247 146 5 81 254 49 183 205 139 136 53 114 191 30 171 246 75 41 235 253 213 122 215 83 242 85 214 59 123 189 142 131 175 113 82 170 123 226 67 146 45 92 189 120 178 241 102 17 116 143 119 178 166 74 110 121 132 216 47 165 214 91 140 202 129 110 33 90 73 73 90 220 3 96 29 165 72 71 235 107 166 235 133 215 20 178 2 74 142 244 6 128 249 15 149 81 109 58 220 41 47 87 248 46 246 109 46 53 31 162 63 21 250 66 10 213 161 161 216 146 84 116 0 29 201 36 246 0 14 228 251 133 113 186 235 81 163 191 38 91 205 69 139 29 5 199 223 121 97 13 180 129 234 165 40 246 2 181 254 13 45 191 17 220 162 156 5 169 55 44 123 6 77 177 203 156 151 210 202 153 147 125 101 14 33 29 9 82 191 201 176 165 40 250 2 84 16 174 173 18 2 42 237 237 231 113 44 71 234 203 58 247 16 183 142 101 236 109 31 14 152 202 249 71 147 94 228 199 219 112 98 56 178 30 181 226 202 90 74 68 217 75 246 101 76 79 126 232 214 219 78 193 7 99 209 77 145 87 14 177 246 43 29 187 25 179 193 179 216 33 181 111 182 64 101 44 70 142 202 116 150 208 145 160 7 255 0 159 83 239 172 133 117 180 233 198 148 20 35 193 28 173 74 146 171 55 57 113 98 148 165 72 70 41 74 80 10 82 148 7 4 200 113 238 17 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74 101 246 92 27 75 141 168 16 164 159 145 4 215 95 0 229 92 167 195 196 86 45 87 166 38 102 252 85 25 42 12 201 101 33 87 59 19 94 161 43 79 96 243 9 244 10 237 210 61 233 9 75 103 13 155 90 156 51 9 104 223 238 12 246 141 42 147 196 227 170 69 221 165 71 240 204 231 29 228 75 11 23 220 38 241 22 245 106 123 178 95 142 189 244 171 64 244 45 39 74 66 192 80 218 20 2 134 198 192 169 5 116 5 8 172 102 69 145 90 177 43 36 235 222 75 61 139 101 170 11 69 217 50 159 95 74 27 79 251 73 36 0 7 114 72 3 100 215 98 233 116 135 100 182 76 185 221 228 183 14 223 9 133 200 147 33 213 116 161 166 144 146 165 45 71 220 0 4 154 163 89 61 254 111 137 220 133 139 166 78 204 168 124 91 21 229 11 5 144 21 54 229 205 125 209 244 231 245 163 210 54 122 19 248 122 117 149 193 90 180 40 67 167 50 106 52 103 94 125 8 27 96 243 230 119 201 79 202 87 11 226 136 135 143 68 83 137 118 247 125 140 162 167 82 19 190 180 50 167 25 109 177 221 43 30 99 221 101 42 73 242 128 53 192 239 46 114 127 16 46 5 203 152 152 182 100 56 92 144 75 247 75 92 31 38 68 126 174 146 149 164 33 231 16 226 82 158 165 41 26 74 138 118 164 169 125 10 77 86 158 50 202 44 12 88 209 138 102 166 227 152 94 45 211 31 183 67 179 90 157 91 198 90 227 16 217 246 82 94 91 104 83 5 164 182 228 102 80 165 121 46 133 184 116 13 89 12 115 135 178 254 90 118 210 239 45 91 227 98 152 5 169 13 38 219 136 70 74 82 167 90 72 29 40 117 8 82 146 210 61 148 109 61 107 89 210 147 249 164 149 33 83 39 149 148 68 214 30 25 181 57 231 135 217 230 28 40 193 136 250 45 185 61 177 225 63 31 185 146 82 98 76 71 116 146 160 9 232 86 186 84 52 125 202 209 41 77 84 2 229 195 145 109 38 219 33 73 196 121 55 15 186 199 156 252 87 154 235 250 13 194 58 189 151 130 6 250 227 175 123 4 117 1 180 253 97 211 213 232 133 106 158 87 240 247 136 242 212 184 151 91 153 184 88 178 104 73 232 139 125 178 201 250 44 196 35 244 10 244 66 211 242 80 36 108 244 148 245 29 235 92 80 223 98 81 120 146 224 205 154 21 247 89 140 150 98 248 163 72 255 0 46 220 247 228 121 95 146 56 207 233 27 255 0 170 126 147 47 202 214 255 0 171 235 235 244 249 254 21 14 200 228 230 247 102 155 184 242 199 51 221 45 17 220 81 13 71 177 202 106 195 17 59 245 108 58 173 41 225 238 246 129 58 247 214 203 87 133 78 66 14 20 71 231 73 137 137 189 36 59 139 199 113 240 143 135 157 230 111 171 245 181 83 60 63 194 63 28 99 211 85 116 201 162 75 228 27 235 136 41 92 252 165 225 63 177 3 105 13 40 121 64 111 100 109 37 67 103 218 173 117 74 238 122 78 105 47 37 242 77 188 180 134 177 131 111 204 170 168 181 96 9 10 13 114 189 217 1 93 212 7 33 180 55 247 215 89 120 79 14 78 121 46 93 111 214 203 243 227 68 46 229 154 37 227 253 215 83 87 171 249 9 226 207 252 90 97 159 251 187 19 255 0 215 95 133 240 39 21 185 245 184 215 14 31 230 216 35 15 240 69 29 157 71 255 0 107 61 87 116 215 253 72 166 208 243 44 74 200 243 88 207 25 90 83 147 93 95 252 227 118 108 70 42 94 234 58 215 152 235 201 218 0 29 129 89 43 41 7 184 2 167 112 120 215 158 111 13 135 25 196 48 172 107 168 157 53 120 188 191 37 196 252 58 140 125 164 254 21 109 113 156 51 28 194 226 187 23 15 176 90 177 248 207 44 45 214 173 176 155 140 151 20 6 186 148 16 6 206 187 108 214 114 189 133 133 24 246 186 207 204 242 119 213 165 217 209 121 21 95 23 240 167 122 201 174 17 46 28 253 145 66 189 65 134 239 154 206 49 98 105 108 91 84 224 250 171 121 106 211 143 124 122 84 6 136 214 202 74 146 122 238 176 197 143 199 149 146 28 38 91 141 22 70 5 228 178 203 72 8 67 104 66 220 210 82 145 216 36 6 187 1 240 171 97 85 43 55 127 200 254 16 78 53 64 254 159 14 121 7 240 250 122 191 250 107 122 16 141 53 209 138 194 52 167 57 77 244 164 242 203 107 74 82 179 48 20 165 40 5 41 74 1 74 82 128 82 148 160 20 165 40 5 41 74 1 74 82 128 82 148 160 20 165 40 5 85 142 121 224 8 120 212 44 167 148 184 166 228 113 11 228 40 47 92 175 22 244 48 29 183 221 144 203 106 113 97 108 122 33 197 107 235 167 223 179 160 165 21 213 167 173 127 206 223 239 35 201 127 250 41 116 255 0 146 57 88 202 42 107 18 89 70 81 147 139 204 94 25 74 49 174 90 102 93 174 195 43 62 179 200 194 205 241 143 54 221 62 70 213 109 156 1 210 188 183 207 249 53 3 234 133 147 211 177 181 13 141 236 159 206 197 117 36 21 52 224 210 146 65 215 99 232 65 248 124 235 100 120 98 198 108 217 159 133 60 50 203 149 91 34 222 45 82 225 188 151 162 202 104 56 133 106 75 186 58 62 132 16 8 35 184 32 16 65 21 5 202 188 55 230 124 96 167 101 240 148 161 150 98 221 106 89 196 47 18 186 94 138 10 135 104 114 149 232 0 39 216 89 244 78 207 152 163 84 183 27 53 62 181 31 98 226 134 209 107 74 190 228 13 236 70 85 130 246 172 179 137 166 140 63 49 65 234 87 144 122 32 92 199 169 102 75 31 83 74 253 32 6 143 180 70 244 164 216 78 55 241 89 131 229 80 30 137 157 92 225 113 246 91 110 233 110 233 105 190 74 68 64 219 154 245 105 199 8 75 136 62 163 71 122 209 32 2 9 174 12 242 198 46 220 197 91 178 119 166 225 87 182 192 47 91 50 40 78 69 117 191 135 181 210 82 65 245 4 244 146 61 213 151 75 152 119 32 169 166 131 152 198 94 235 9 211 109 149 71 150 235 105 248 37 39 107 3 229 173 84 20 110 171 218 174 141 88 182 185 19 86 182 163 115 214 167 36 153 37 231 206 85 182 115 164 232 156 107 199 87 19 116 197 88 146 220 188 194 243 15 218 140 166 91 87 83 112 154 119 209 197 173 73 10 234 65 208 233 78 138 128 112 39 3 98 195 46 252 253 149 93 49 59 35 174 88 56 242 194 248 133 145 220 226 144 135 38 58 148 141 219 163 118 210 80 145 164 172 252 53 190 221 33 92 57 30 73 111 227 107 52 68 179 110 109 50 157 115 201 177 216 96 70 13 185 58 81 32 33 182 153 64 244 43 41 10 80 7 215 93 212 82 13 148 240 217 198 115 184 175 138 45 150 172 141 40 25 44 231 157 185 222 212 133 5 117 75 125 93 74 10 41 37 37 73 72 67 100 164 144 74 54 59 106 183 40 55 121 83 123 53 213 143 15 95 19 82 186 86 116 247 80 125 103 199 211 192 156 97 120 6 49 199 86 118 237 24 61 142 21 142 222 132 164 22 227 52 18 167 10 70 130 156 95 214 113 90 29 212 178 84 125 228 212 142 148 171 114 168 82 148 160 20 165 40 5 41 74 1 74 82 128 85 125 203 248 131 37 187 248 182 192 57 46 10 34 43 25 179 88 222 131 48 169 253 60 151 10 38 1 164 107 184 38 67 125 193 248 252 59 216 42 80 10 82 148 2 148 165 0 165 41 64 41 74 80 10 82 148 2 148 165 0 165 41 64 41 74 80 10 82 148 2 148 165 0 173 125 206 231 92 35 201 123 255 0 193 75 167 252 149 202 216 53 132 204 191 236 67 32 255 0 205 178 63 213 170 128 212 254 14 71 79 134 188 8 31 251 218 65 255 0 230 158 173 231 90 87 194 71 253 174 120 15 254 66 231 250 247 43 117 80 24 235 197 130 211 144 199 76 107 253 178 21 214 58 79 80 106 100 100 60 128 126 58 80 34 181 158 85 225 127 136 178 248 222 84 252 18 207 1 196 146 166 228 90 152 22 247 80 179 246 186 152 233 234 32 247 210 182 55 238 53 183 105 64 105 222 47 240 209 133 241 117 245 89 12 87 46 249 54 74 27 45 49 119 200 38 9 146 99 52 83 211 229 180 66 82 148 13 108 108 39 171 74 82 119 162 69 110 42 82 188 73 45 17 235 121 226 41 74 87 167 135 255 217]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// download and display a JPEG\n", "resp, err := http.Get(\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/df/Go_gopher_app_engine_color.jpg/320px-Go_gopher_app_engine_color.jpg\")\n", "bytes, err := ioutil.ReadAll(resp.Body)\n", "resp.Body.Close()\n", "display.JPEG(bytes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, if you prefer to use `image.Image`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 6, "metadata": { "image/png": { "height": 400, "width": 2007 } }, "output_type": "execute_result" } ], "source": [ "import \"image\"\n", "resp, err := http.Get(\"https://github.com/gopherdata/gophernotes/raw/master/files/gophernotes-logo.png\")\n", "img, ext, err := image.Decode(resp.Body)\n", "resp.Body.Close()\n", "display.Image(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Plotting with gonum/plot

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the functions above, you can also display plots with `gonum/plot`.\n", "\n", "This requires importing third-party packages, which is currently only supported on Linux and Mac OS X.\n", "\n", "For example:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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155 38 118 96 121 65 52 127 175 115 153 177 3 3 129 72 24 96 76 151 237 8 81 177 115 128 5 67 196 237 38 37 37 86 47 68 6 52 155 118 136 24 199 12 226 144 46 192 146 38 111 106 150 166 13 71 6 41 244 217 182 140 190 179 7 135 44 21 57 214 34 26 142 16 65 42 178 5 24 111 104 224 156 203 118 13 172 208 167 216 118 7 86 19 68 19 250 124 201 246 8 17 85 136 32 8 217 2 140 233 13 233 187 152 229 218 129 217 248 8 209 172 9 81 180 241 205 19 26 208 82 79 164 68 162 167 15 194 14 12 196 33 93 128 213 71 104 81 161 226 118 91 189 144 124 74 5 152 61 143 16 163 81 166 235 106 37 74 16 243 131 4 2 189 222 203 140 34 14 16 136 108 1 38 211 32 149 46 182 157 105 105 134 194 169 231 40 106 232 243 132 250 253 169 95 242 122 190 151 25 59 48 16 135 108 1 198 116 121 6 169 116 177 237 17 34 11 134 105 113 49 41 46 178 122 33 178 240 122 136 203 213 203 14 12 173 164 64 28 210 5 88 68 151 172 130 163 51 192 98 113 133 49 171 23 50 208 204 16 46 128 229 25 9 104 189 244 243 197 14 12 196 33 91 128 113 189 65 182 155 192 108 60 81 133 213 132 208 5 49 191 168 230 239 101 46 51 122 33 130 56 100 11 48 22 145 173 13 71 87 67 122 197 126 117 28 102 48 168 86 160 7 71 62 17 77 227 61 142 181 68 17 7 8 68 170 0 227 45 173 60 97 200 119 13 76 177 229 14 140 55 53 241 182 118 84 112 228 23 13 4 112 132 8 210 24 68 131 195 95 120 225 133 181 107 215 118 189 26 12 6 23 45 90 148 211 103 224 186 158 250 29 83 190 35 68 91 206 180 100 193 48 33 68 197 53 176 188 34 154 159 191 183 187 135 15 192 14 12 4 50 136 2 236 252 180 174 87 127 250 211 159 230 250 25 88 68 39 14 7 241 23 231 123 105 86 163 148 20 184 121 187 189 26 210 155 161 16 41 25 38 217 45 125 150 35 1 173 151 145 96 232 133 8 226 144 234 8 81 190 65 42 93 210 35 193 236 21 96 44 24 70 19 169 188 163 154 198 218 218 149 100 178 187 15 64 43 41 16 136 84 1 38 223 32 149 46 54 188 21 204 172 9 82 76 81 201 55 162 249 21 206 123 42 68 196 53 48 16 135 84 1 38 225 32 149 207 144 194 66 123 93 3 227 156 133 176 3 203 63 26 208 82 255 82 186 15 48 92 3 3 129 200 21 96 17 157 200 87 130 152 70 10 125 182 218 129 177 67 117 220 48 176 3 203 63 159 143 56 157 188 177 155 74 122 35 125 180 136 0 3 65 72 21 96 233 187 152 229 189 6 102 171 0 11 134 136 195 65 203 70 90 189 16 9 17 205 223 237 88 75 195 80 16 96 32 14 137 2 204 48 88 75 139 180 71 136 54 107 72 207 66 225 84 122 169 170 213 11 145 16 213 180 238 142 16 121 58 192 112 132 8 162 144 39 192 88 68 79 125 61 146 22 113 40 62 175 173 102 90 154 65 52 145 234 47 36 208 253 92 230 204 14 12 85 136 32 8 121 2 140 71 116 66 136 204 101 244 118 10 48 22 12 225 22 230 126 66 52 127 119 205 56 176 3 3 177 200 19 96 76 215 73 113 177 226 24 68 183 102 231 145 189 142 16 13 131 213 30 162 232 130 216 63 104 15 247 50 39 112 13 12 68 34 81 128 201 91 130 104 183 153 150 44 252 41 103 12 53 244 253 132 104 129 238 142 16 177 3 3 177 200 19 96 18 151 32 118 238 192 12 163 179 202 89 118 102 40 76 60 5 164 180 196 234 133 200 137 106 126 222 218 170 152 230 49 222 135 42 68 16 138 60 1 198 228 109 195 241 121 63 95 123 108 194 88 48 132 41 42 253 135 4 52 206 57 111 110 57 198 251 58 3 76 206 115 120 144 143 68 1 166 235 242 245 161 239 98 171 153 150 44 20 198 32 230 254 67 210 205 56 142 121 43 24 218 112 128 88 100 9 48 206 121 67 163 132 147 192 62 211 185 3 107 107 179 122 33 3 193 172 9 162 130 163 255 144 194 66 162 170 199 46 68 68 35 68 16 138 36 1 198 155 155 121 50 41 241 53 48 197 83 64 8 177 67 67 122 222 222 206 26 26 81 67 223 175 210 115 153 143 81 136 200 19 216 129 129 72 36 9 48 22 105 72 253 179 148 119 7 166 16 162 120 60 118 56 66 100 193 112 234 121 137 29 88 127 162 1 141 29 179 29 162 97 224 46 102 16 136 36 1 198 35 17 82 224 38 133 133 86 47 164 31 17 123 52 227 96 161 16 245 251 73 113 145 213 11 145 89 119 237 16 113 13 12 196 34 73 128 177 136 180 131 84 186 16 159 207 14 247 50 155 193 48 173 194 246 171 127 117 119 132 136 107 96 32 22 89 2 76 234 18 196 12 155 76 84 97 161 176 138 41 42 253 44 125 132 136 29 24 8 79 146 0 227 17 157 14 147 183 130 35 205 38 237 16 205 96 16 109 124 251 27 209 252 199 110 198 145 192 14 12 68 34 73 128 49 189 193 6 71 136 242 183 67 228 122 3 111 239 192 14 172 191 145 128 198 91 91 21 206 143 124 7 142 16 65 40 178 4 88 125 132 200 219 134 35 195 14 59 48 51 20 34 132 208 242 50 171 23 34 57 170 5 56 99 71 183 244 229 134 65 80 133 8 226 144 34 192 162 49 222 209 33 255 14 204 6 13 233 89 48 76 74 75 20 183 219 234 133 72 142 104 254 84 92 29 125 138 104 24 24 34 10 2 145 33 192 152 158 30 101 41 241 77 96 105 118 104 72 207 66 33 181 170 210 234 85 200 143 20 23 17 85 61 122 46 51 55 12 89 7 18 129 148 36 9 48 66 9 25 18 176 122 33 253 204 43 255 125 96 102 48 140 10 142 129 64 8 241 23 31 115 7 134 35 68 16 136 12 1 198 35 13 68 211 20 42 195 215 210 131 244 53 176 168 213 171 232 79 140 177 240 167 168 224 24 24 196 239 103 71 223 203 156 76 162 136 3 4 34 195 15 125 166 235 18 15 82 233 66 124 94 110 154 74 60 110 245 66 250 11 59 88 203 13 3 59 176 129 65 53 255 209 247 50 243 100 18 247 129 129 64 164 8 176 136 252 119 49 127 222 144 94 222 58 14 22 10 19 135 131 142 28 97 245 66 108 129 4 52 126 116 59 68 148 209 131 80 100 8 48 30 137 72 95 130 120 88 128 73 219 144 222 12 134 104 121 153 244 71 193 131 4 9 4 142 110 198 129 110 244 32 22 25 126 88 176 72 131 244 109 56 62 159 105 41 239 68 21 22 10 171 232 130 56 80 168 223 127 244 125 96 216 129 129 88 196 15 48 198 120 99 163 29 142 16 21 183 155 168 170 196 149 244 44 24 198 20 149 1 67 2 26 111 110 57 162 25 7 110 100 6 177 8 31 96 188 177 137 51 102 135 34 142 244 41 162 71 218 107 96 134 193 234 234 40 230 88 14 20 26 208 184 105 242 150 214 47 188 21 59 48 16 138 240 1 214 121 23 179 244 59 48 198 140 87 94 229 70 50 241 226 203 198 203 175 42 140 89 189 160 60 99 161 48 103 76 197 14 108 160 16 127 186 25 199 17 133 136 8 48 16 138 248 1 86 31 33 94 175 226 41 176 122 33 253 137 243 232 3 191 37 69 69 106 69 185 99 250 201 196 95 28 189 239 193 99 116 98 21 153 89 19 36 30 15 25 42 255 181 204 65 130 104 126 66 9 107 250 66 33 34 198 169 128 88 132 15 48 174 235 210 55 145 74 190 241 150 227 63 166 58 102 157 154 110 198 17 117 204 156 225 60 101 154 177 117 155 213 235 202 39 84 112 12 52 74 73 209 81 205 56 48 78 5 132 34 124 128 177 72 3 149 253 215 246 228 187 187 157 167 158 146 41 68 76 188 188 57 254 236 11 169 55 110 255 167 76 155 48 51 24 162 149 232 130 56 160 200 17 247 50 51 198 25 195 14 12 4 34 124 227 78 174 235 164 180 196 234 85 244 51 135 202 13 131 184 221 174 249 243 20 66 146 255 220 153 88 191 145 39 147 201 255 249 46 45 27 169 86 150 211 138 10 181 170 146 86 85 16 77 179 122 173 125 196 66 97 231 140 233 86 175 194 94 142 156 203 108 24 169 63 81 133 8 226 16 62 192 88 68 119 86 79 180 122 21 253 203 57 235 84 227 213 191 187 190 190 80 173 158 224 169 158 160 40 74 98 253 159 73 97 17 241 122 204 154 32 171 9 38 54 255 149 215 71 56 231 164 176 80 173 40 167 21 229 169 84 171 170 84 43 43 133 184 58 200 219 219 89 99 19 106 232 7 24 9 104 252 176 118 136 60 29 96 216 129 129 64 36 8 176 136 244 215 192 212 201 147 146 187 247 198 215 62 231 60 125 174 66 136 241 143 173 138 105 186 206 93 152 250 255 55 235 212 206 15 138 199 89 248 83 51 24 98 53 33 51 24 76 190 181 157 165 43 164 233 208 161 233 45 90 57 173 172 80 43 43 105 121 153 226 24 116 3 159 88 77 40 245 101 162 134 126 96 17 191 159 5 195 159 191 158 72 239 192 16 96 32 14 193 3 172 189 157 199 226 118 40 93 115 127 99 137 249 193 191 83 209 197 185 99 250 201 234 137 227 142 250 8 55 29 51 154 142 25 221 245 6 222 210 202 66 33 51 24 78 253 185 111 127 226 229 87 121 60 78 84 149 142 24 158 222 162 85 164 254 172 170 36 165 165 10 25 232 47 231 8 102 77 144 6 52 197 231 179 120 29 54 67 3 154 113 120 51 14 236 192 64 52 98 7 24 139 52 216 226 38 176 52 245 196 177 234 137 99 179 255 120 82 92 164 78 170 86 39 85 119 190 206 57 175 143 152 161 112 38 213 140 55 222 98 7 107 185 105 18 183 59 149 100 169 45 90 185 90 81 65 171 42 137 191 184 191 190 134 110 176 16 42 56 44 64 2 129 195 171 16 51 71 136 216 129 129 64 4 15 48 93 39 14 7 9 136 90 185 48 160 8 33 165 37 142 210 18 101 250 201 157 111 73 38 217 193 90 22 12 165 82 45 24 74 188 248 10 171 143 164 126 33 40 46 166 233 83 199 84 158 85 166 246 106 74 65 255 94 72 99 193 176 58 225 196 126 253 43 224 104 196 239 231 201 36 111 107 35 133 133 10 138 56 64 64 98 7 24 215 117 50 36 160 16 171 143 192 4 229 112 208 202 10 90 89 241 249 147 32 26 77 135 89 216 12 133 88 40 156 124 253 13 214 218 150 138 180 146 97 106 250 35 211 27 181 10 90 54 82 81 243 121 33 205 12 133 157 11 230 229 241 19 66 54 104 250 55 63 222 212 156 9 48 20 113 128 112 196 14 48 86 111 139 65 42 3 199 227 81 79 28 167 158 56 174 235 103 24 111 106 234 204 179 96 56 249 222 158 196 95 94 226 137 4 81 85 58 114 68 186 202 49 93 193 95 81 126 60 119 50 112 189 129 71 163 42 230 88 14 56 162 249 9 33 188 177 73 201 124 243 81 196 1 162 17 60 192 34 186 29 6 169 88 136 104 154 170 105 234 212 201 157 175 115 206 14 213 177 96 106 127 102 6 67 198 150 109 236 96 45 103 140 20 20 164 54 103 85 21 169 60 171 44 167 149 149 164 168 48 155 207 207 62 62 144 120 109 11 33 196 38 237 152 7 23 85 37 133 62 214 216 148 217 77 115 195 32 148 98 30 27 8 68 236 0 227 122 3 157 50 201 234 85 216 9 33 116 196 112 58 98 184 146 238 12 162 100 46 164 133 15 154 193 96 38 213 18 187 222 235 108 175 156 190 144 166 86 86 210 170 10 181 178 130 150 151 43 110 215 23 62 149 105 198 30 126 140 12 29 170 36 18 164 184 56 122 255 67 238 197 139 232 216 209 150 124 89 182 69 2 129 207 167 130 25 6 46 128 129 88 196 14 48 166 235 78 252 230 110 45 135 131 142 170 164 163 14 171 33 236 136 154 161 16 171 9 154 193 176 121 224 147 196 63 182 240 246 14 66 8 41 25 150 185 132 166 86 85 210 202 138 228 91 59 28 51 79 117 156 124 82 244 190 7 213 241 227 60 87 94 30 253 229 125 158 31 92 133 35 172 129 68 52 141 53 118 246 243 69 39 95 16 142 200 1 150 52 121 83 51 174 129 13 58 94 143 58 254 68 117 252 137 95 184 144 86 19 50 107 130 44 20 74 238 122 55 177 241 197 212 207 74 66 104 101 133 177 117 155 241 250 27 238 37 139 20 151 203 49 227 148 228 158 125 142 255 152 106 237 242 109 133 106 254 207 43 233 49 75 5 68 35 112 128 113 93 231 156 19 217 219 112 72 160 243 66 218 73 83 58 95 103 140 213 213 199 30 92 173 78 157 194 106 130 106 217 72 71 250 28 152 20 250 20 121 231 77 15 78 36 160 177 125 239 119 190 130 0 3 209 8 28 96 159 141 178 68 17 135 104 40 165 35 134 147 97 195 220 103 253 167 226 245 118 189 57 185 119 159 251 204 255 180 116 101 182 67 3 154 241 217 14 140 39 112 132 8 130 17 181 226 200 252 247 71 137 205 127 167 110 55 111 107 183 122 45 208 23 238 175 47 140 62 248 8 111 107 203 188 106 188 182 133 80 149 12 47 181 122 93 246 66 252 56 66 4 129 9 184 3 227 60 246 216 227 164 168 136 122 61 44 16 136 61 185 214 57 99 186 99 246 76 171 151 5 185 161 149 21 238 165 231 199 255 176 134 27 134 194 152 99 242 36 247 55 47 180 122 81 182 67 2 90 234 251 223 17 85 188 30 110 24 4 85 136 32 20 241 2 204 248 219 107 234 184 177 206 47 207 141 173 126 148 86 150 123 174 250 110 166 140 141 12 193 89 162 96 104 69 121 193 119 47 179 122 21 182 70 3 1 69 81 88 99 35 245 122 176 3 3 225 136 119 132 152 124 119 183 243 75 167 29 222 134 195 53 255 12 99 251 191 172 94 23 128 120 50 141 155 51 167 136 40 163 7 225 136 183 3 83 210 183 211 42 138 226 185 226 178 76 215 0 226 241 42 177 152 213 107 2 16 144 211 73 124 62 214 220 172 226 26 24 8 72 188 29 24 29 54 148 133 194 169 220 26 18 32 154 63 181 39 219 249 182 58 113 130 213 235 2 16 18 13 104 157 59 176 4 2 12 4 35 94 128 185 206 93 24 123 98 45 11 127 154 121 53 249 250 27 172 62 130 97 28 0 125 67 186 238 101 70 17 7 136 70 188 35 68 82 84 232 185 114 89 226 79 127 102 13 141 233 234 181 234 130 203 46 181 122 81 0 162 162 1 141 101 218 33 226 8 17 68 35 94 128 165 51 172 200 125 49 74 174 1 242 128 104 1 246 193 135 153 34 14 138 0 3 161 136 119 132 8 0 121 68 52 127 103 63 95 236 192 64 52 8 48 0 91 163 1 45 51 81 5 173 164 64 56 8 48 0 91 35 1 141 199 226 74 52 134 29 24 8 7 1 6 96 107 212 239 87 20 133 53 53 161 149 20 8 7 1 6 96 107 36 160 165 103 182 53 99 7 6 194 65 128 1 216 155 203 69 188 94 222 216 136 0 3 225 32 192 0 236 142 106 126 214 216 132 94 136 32 28 4 24 128 221 17 191 159 55 55 43 104 37 5 162 65 128 1 216 29 9 104 44 162 115 198 176 3 3 177 32 192 0 236 142 6 52 86 87 159 122 9 85 136 32 20 33 91 73 1 64 30 17 191 159 71 34 169 23 176 3 147 87 109 109 237 189 247 222 27 141 70 211 3 169 72 85 85 213 228 201 147 231 207 159 79 105 79 219 152 189 123 247 174 94 189 250 206 59 239 84 85 117 0 23 155 45 236 192 0 236 46 181 3 107 109 83 210 227 193 172 94 11 244 151 17 35 70 220 114 203 45 207 63 255 124 77 77 205 178 101 203 198 141 27 183 114 229 202 243 206 59 175 163 163 163 135 71 237 222 189 123 205 154 53 45 45 45 61 127 242 142 142 14 198 88 190 151 220 59 4 24 128 221 145 64 160 243 5 4 152 212 8 33 148 210 226 226 226 234 234 234 115 206 57 231 247 191 255 253 134 13 27 94 124 241 197 30 30 114 193 5 23 132 195 225 192 103 207 144 238 92 124 241 197 117 117 117 249 94 111 239 112 132 8 96 119 153 193 176 41 14 252 64 176 145 241 227 199 43 138 178 99 199 142 243 207 63 95 81 148 237 219 183 111 216 176 161 177 177 241 164 147 78 186 228 146 75 92 46 87 125 125 253 211 79 63 173 40 202 165 151 94 234 243 249 30 126 248 225 104 52 186 120 241 226 205 155 55 111 223 190 125 202 148 41 203 150 45 51 12 227 153 103 158 89 183 110 221 204 153 51 39 76 152 176 120 241 226 79 62 249 100 245 234 213 13 13 13 85 85 85 223 250 214 183 70 140 24 145 243 202 226 9 197 229 204 76 222 239 89 30 158 175 239 188 243 206 158 61 123 84 85 157 58 117 234 196 137 19 143 120 239 182 109 219 116 93 207 188 60 98 196 136 25 51 102 28 255 223 8 0 121 68 53 173 243 37 4 152 157 132 66 33 69 81 42 42 42 20 69 121 242 201 39 127 252 227 31 63 247 220 115 35 70 140 88 181 106 213 195 15 63 188 101 203 150 146 146 146 105 211 166 205 157 59 119 209 162 69 62 159 239 204 51 207 172 172 172 60 112 224 192 244 233 211 139 139 139 175 184 226 138 64 32 176 112 225 194 88 44 198 57 159 60 121 242 232 209 163 163 209 232 105 167 157 246 203 95 254 242 107 95 251 218 93 119 221 245 254 251 239 231 20 96 201 237 255 52 254 177 149 20 22 242 142 14 90 54 210 189 248 188 158 159 147 121 120 190 94 125 245 213 55 222 120 163 207 231 187 226 138 43 206 59 239 188 107 174 185 230 240 247 222 114 203 45 93 47 207 153 51 7 1 6 48 232 120 10 50 255 197 17 162 125 52 52 52 92 115 205 53 39 156 112 194 69 23 93 164 40 202 138 21 43 150 47 95 62 121 242 100 69 81 238 188 243 206 210 210 210 231 159 127 126 233 210 165 62 159 175 235 33 30 143 71 81 148 165 75 151 206 154 53 75 81 148 117 235 214 237 220 185 115 233 210 165 39 159 124 178 162 40 179 102 205 26 62 124 248 158 61 123 14 30 60 56 123 246 108 159 207 183 98 197 10 195 48 178 95 82 242 95 111 155 251 63 244 92 253 253 204 222 203 220 187 47 246 251 39 11 254 231 146 30 30 146 135 0 187 254 250 235 191 250 213 175 102 146 236 250 235 175 63 34 192 42 42 42 126 243 155 223 28 255 223 2 0 253 14 1 102 3 175 188 242 202 89 103 157 69 8 153 62 125 250 234 213 171 253 126 127 36 18 9 133 66 227 198 141 203 124 64 81 81 81 69 69 197 174 93 187 150 46 93 218 195 231 25 54 108 216 209 249 52 110 220 184 177 99 199 206 156 57 243 234 171 175 254 230 55 191 57 114 228 200 47 188 59 145 80 146 102 119 159 208 216 252 87 207 247 46 87 162 177 204 171 234 168 81 201 183 254 201 14 212 208 210 146 238 30 146 135 0 59 243 204 51 51 47 124 248 225 135 67 134 12 57 254 79 8 0 3 137 29 172 205 188 96 188 249 150 243 180 217 74 143 117 213 32 186 5 11 22 60 250 232 163 135 191 165 181 181 85 81 148 130 130 130 174 183 20 20 20 196 98 177 62 124 114 151 203 245 230 155 111 222 127 255 253 15 60 240 192 205 55 223 188 110 221 186 5 11 22 116 189 55 250 192 111 141 183 118 244 240 112 227 138 171 142 120 75 252 165 87 122 248 248 252 28 121 191 250 234 171 155 54 109 170 175 175 127 252 241 199 143 120 87 36 18 185 225 134 27 154 155 155 135 14 29 186 108 217 178 170 170 170 188 252 141 0 144 23 201 109 111 38 119 239 37 14 7 79 38 137 195 17 253 213 3 158 239 94 166 184 221 86 175 11 6 78 69 69 133 170 170 135 151 17 214 214 214 102 142 19 115 149 76 38 139 139 139 87 173 90 117 253 245 215 47 90 180 232 238 187 239 62 60 192 220 23 156 239 250 175 5 221 61 54 254 244 115 238 111 44 57 252 45 198 150 215 213 177 99 232 200 110 175 162 229 39 192 198 143 31 239 112 56 30 127 252 241 159 255 252 231 15 60 240 192 225 239 122 234 169 167 156 233 115 137 103 158 121 102 246 236 217 251 246 237 43 46 46 206 203 95 10 0 199 137 183 180 26 59 254 229 249 254 247 146 255 122 155 55 55 59 102 207 164 165 37 241 117 27 142 248 57 2 114 115 58 157 23 93 116 209 186 117 235 46 188 240 66 69 81 54 111 222 108 154 102 230 194 80 54 50 63 210 235 234 234 134 15 31 254 246 219 111 191 247 222 123 151 94 122 169 203 229 26 63 126 252 161 67 135 14 255 72 90 94 166 148 119 251 121 28 179 79 101 117 117 206 175 124 57 243 42 175 143 240 104 212 249 213 175 244 240 87 231 39 192 42 210 230 206 157 59 108 216 176 75 46 185 100 206 156 57 93 239 114 126 118 170 190 116 233 210 203 47 191 124 251 246 237 243 231 207 207 188 101 229 202 149 7 14 28 232 238 115 6 131 193 69 139 22 229 101 121 0 112 76 201 119 222 115 158 54 75 81 20 82 232 83 154 155 83 63 98 198 140 102 235 55 90 189 46 200 191 214 214 214 219 111 191 221 52 205 119 222 121 103 213 170 85 223 249 206 119 70 143 30 221 245 222 123 239 189 247 202 43 175 92 178 100 73 73 73 201 129 3 7 214 173 91 55 106 212 168 221 187 119 63 242 200 35 195 134 13 251 245 175 127 125 217 101 151 61 251 236 179 195 134 13 123 226 137 39 198 140 25 179 117 235 86 93 215 119 237 218 245 238 187 239 78 157 58 245 148 83 78 57 235 172 179 230 205 155 183 106 213 170 219 110 187 237 149 87 94 73 36 18 135 14 29 122 248 225 135 179 95 161 235 204 255 140 63 251 66 236 129 223 170 227 198 176 122 157 69 34 5 151 126 179 231 135 16 206 249 113 124 79 82 169 91 87 87 55 101 202 148 204 171 67 135 14 125 225 133 23 78 63 253 244 204 171 166 105 110 217 178 229 43 95 233 140 208 64 32 176 105 211 166 76 5 75 175 226 241 184 211 233 236 185 205 9 0 28 15 227 181 45 164 184 216 113 242 73 236 227 3 198 155 219 221 23 46 85 20 37 122 239 253 158 255 115 165 213 75 3 11 112 206 13 195 112 185 92 125 120 108 52 26 205 148 41 102 126 122 43 138 226 238 211 65 52 111 107 103 161 48 209 252 116 196 240 94 63 248 120 227 161 181 181 245 222 123 239 109 105 105 73 36 18 191 250 213 175 70 143 30 61 123 246 236 154 154 154 137 19 39 190 255 254 251 170 170 222 121 231 157 141 141 141 137 68 226 158 123 238 153 58 117 106 246 101 244 110 183 27 233 5 208 175 28 83 38 37 223 220 158 250 65 48 250 132 76 122 177 131 181 159 223 215 12 54 67 8 233 91 122 117 21 217 103 184 211 250 184 134 66 159 58 113 124 54 233 149 135 35 196 242 242 242 51 206 56 227 190 251 238 107 111 111 159 48 97 194 214 173 91 93 46 215 144 33 67 150 44 89 50 124 248 240 204 45 220 15 61 244 80 91 91 91 117 117 245 230 205 155 7 103 71 72 0 123 34 67 134 208 81 85 241 167 159 115 159 123 182 226 241 152 251 63 72 172 223 88 112 197 119 172 94 23 64 86 142 247 8 17 0 68 103 238 219 111 188 246 15 30 79 168 163 170 92 103 46 64 9 34 136 2 1 6 0 0 66 194 69 38 0 0 16 18 2 12 0 0 132 132 0 3 0 0 33 33 192 0 0 64 72 8 48 0 0 16 18 2 12 0 0 132 132 0 3 0 0 33 33 192 0 0 64 72 249 233 70 111 9 93 215 143 24 221 34 153 182 182 182 194 194 66 171 87 49 24 181 182 182 22 21 21 89 189 138 193 8 223 153 238 224 59 211 29 225 190 51 213 213 213 75 150 116 142 251 17 184 19 71 34 145 216 191 127 191 213 171 232 71 63 250 209 143 86 174 92 121 120 139 76 200 88 190 124 249 93 119 221 101 245 42 6 35 124 103 186 131 239 76 119 132 251 206 248 253 254 202 202 202 204 203 2 239 192 92 46 87 215 24 23 41 5 2 129 234 234 106 108 194 142 230 247 251 229 254 95 223 103 248 206 116 7 223 153 238 8 253 157 193 53 48 0 0 16 18 2 12 0 0 132 132 0 3 0 0 33 33 192 64 60 119 220 113 135 213 75 0 193 224 57 35 37 4 24 136 103 212 168 81 86 47 1 4 131 231 140 148 16 96 0 0 32 36 4 24 0 0 8 9 1 6 0 0 66 66 128 13 94 231 156 115 142 203 229 178 122 21 32 146 197 139 23 91 189 4 16 140 208 207 25 129 91 73 1 0 128 157 9 220 74 74 110 29 29 29 119 221 117 87 85 85 213 183 191 253 109 171 215 2 2 88 185 114 101 93 93 221 208 161 67 107 107 107 111 186 233 166 241 227 199 91 189 34 24 236 174 190 250 234 109 219 182 101 94 158 51 103 206 61 247 220 99 245 138 114 134 0 27 140 246 238 221 187 118 237 218 77 155 54 93 112 193 5 86 175 5 196 96 24 198 35 143 60 162 40 202 154 53 107 22 45 90 180 103 207 30 171 87 4 131 93 83 83 211 47 126 241 139 204 203 154 166 89 189 156 190 64 128 13 70 213 213 213 55 223 124 243 190 125 251 172 94 8 8 227 39 63 249 73 230 133 81 163 70 237 223 191 159 49 70 41 174 112 67 79 220 110 247 236 217 179 173 94 197 113 193 83 28 64 6 94 175 87 81 148 104 52 250 216 99 143 93 114 201 37 72 47 200 70 83 83 211 129 3 7 226 241 184 213 11 233 35 236 192 0 36 177 126 253 250 7 31 124 80 85 213 174 221 24 64 15 102 204 152 241 224 131 15 18 66 54 108 216 48 119 238 220 219 110 187 205 234 21 229 12 85 136 131 215 133 23 94 56 99 198 140 235 174 187 206 234 133 128 72 130 193 224 180 105 211 182 108 217 50 113 226 68 171 215 2 98 136 70 163 154 166 109 219 182 109 250 244 233 86 175 37 55 56 103 0 144 1 99 44 243 66 101 101 229 216 177 99 183 108 217 98 245 138 96 176 51 77 51 243 130 199 227 169 168 168 168 169 169 177 122 69 57 67 128 1 8 175 173 173 237 214 91 111 205 188 28 139 197 62 254 248 227 201 147 39 91 189 40 24 236 110 188 241 198 204 11 13 13 13 245 245 245 211 166 77 179 122 69 57 195 53 176 193 104 207 158 61 47 191 252 242 135 31 126 216 222 222 238 245 122 151 46 93 90 82 82 98 245 162 96 240 242 120 60 155 54 109 106 107 107 243 122 189 59 118 236 248 225 15 127 56 103 206 28 171 23 5 131 221 91 111 189 117 195 13 55 184 221 238 29 59 118 60 244 208 67 34 54 236 199 53 48 0 73 112 206 59 58 58 124 62 159 213 11 1 97 112 206 163 209 104 166 132 85 68 8 48 0 0 16 18 174 129 1 0 128 144 16 96 0 0 32 36 4 24 0 0 8 9 1 6 0 0 66 66 25 61 128 0 26 27 27 111 191 253 246 253 251 247 171 170 58 97 194 132 235 174 187 46 16 8 220 119 223 125 175 191 254 186 219 237 190 248 226 139 231 207 159 111 245 26 1 6 26 170 16 1 132 177 108 217 178 71 31 125 116 235 214 173 179 102 205 82 20 165 174 174 238 236 179 207 222 184 113 99 105 105 169 213 75 3 176 0 2 12 64 24 173 173 173 83 167 78 245 120 60 59 119 238 116 185 92 11 23 46 92 177 98 197 25 103 156 97 245 186 0 172 129 0 3 16 201 171 175 190 186 96 193 130 235 174 187 174 168 168 72 81 148 155 110 186 201 234 21 1 88 6 1 6 32 152 239 127 255 251 247 223 127 255 188 121 243 94 122 233 37 204 253 2 59 195 179 31 64 48 215 94 123 45 231 252 147 79 62 137 197 98 86 175 5 192 74 8 48 0 145 48 198 174 188 242 202 59 238 184 227 163 143 62 90 177 98 133 213 203 1 176 18 202 232 1 68 114 235 173 183 158 126 250 233 203 151 47 175 171 171 187 251 238 187 23 47 94 60 111 222 60 171 23 5 96 13 92 3 3 16 198 223 254 246 183 159 253 236 103 235 215 175 39 132 196 98 177 233 211 167 119 116 116 188 251 238 187 153 130 14 0 187 193 17 34 128 24 234 234 234 150 47 95 254 216 99 143 17 66 20 69 41 40 40 248 221 239 126 23 10 133 174 189 246 90 171 151 6 96 13 4 24 128 0 254 248 199 63 126 239 123 223 43 43 43 219 184 113 99 230 45 137 68 98 253 250 245 115 231 206 221 185 115 231 181 215 94 27 10 133 172 94 35 192 64 195 17 34 0 0 8 9 59 48 0 0 16 18 2 12 0 0 132 132 0 3 0 0 33 33 192 0 0 64 72 255 63 0 0 255 255 170 38 154 200 179 101 150 215 0 0 0 0 73 69 78 68 174 66 96 130]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import (\n", " \"math/rand\"\n", " \"bytes\"\n", " \"gonum.org/v1/plot\"\n", " \"gonum.org/v1/plot/plotter\"\n", " \"gonum.org/v1/plot/plotutil\"\n", " \"gonum.org/v1/plot/vg\"\n", ")\n", "\n", "func displayPlot(p *plot.Plot) {\n", " var buf bytes.Buffer\n", " c, err := p.WriterTo(6*vg.Inch, 6*vg.Inch, \"png\")\n", " if err != nil {\n", " panic(err)\n", " }\n", " if _, err := c.WriteTo(&buf); err != nil {\n", " panic(err)\n", " }\n", " Display(display.PNG(buf.Bytes()))\n", "}\n", "\n", "func randomPoints(n int) plotter.XYs {\n", " pts := make(plotter.XYs, n)\n", " for i := range pts {\n", " if i == 0 {\n", " pts[i].X = rand.Float64()\n", " } else {\n", " pts[i].X = pts[i-1].X + rand.Float64()\n", " }\n", " pts[i].Y = pts[i].X + 10*rand.Float64()\n", " }\n", " return pts\n", "}\n", "\n", "p := plot.New()\n", "\n", "p.Title.Text = \"Plotutil example\"\n", "p.X.Label.Text = \"X\"\n", "p.Y.Label.Text = \"Y\"\n", "\n", "err = plotutil.AddLinePoints(p, \"Points\", randomPoints(10))\n", "\n", "displayPlot(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Reference

\n", "

The list of available constants, types, and functions in package display is:

" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "// in package \"display\"\n", "\n", "const (\n", "\tMIMETypeHTML = \"text/html\"\n", "\tMIMETypeJavaScript = \"application/javascript\"\n", "\tMIMETypeJPEG = \"image/jpeg\"\n", "\tMIMETypeJSON = \"application/json\"\n", "\tMIMETypeLatex = \"text/latex\"\n", "\tMIMETypeMarkdown = \"text/markdown\"\n", "\tMIMETypePNG = \"image/png\"\n", "\tMIMETypePDF = \"application/pdf\"\n", "\tMIMETypeSVG = \"image/svg+xml\"\n", "\tMIMETypeText = \"text/plain\"\n", ")\n", "\n", "// MIMEMap holds data that can be presented in multiple formats. The keys are MIME types\n", "// and the values are the data formatted with respect to its MIME type.\n", "// If a map does not contain a \"text/plain\" key with a string value,\n", "// it will be set automatically.\n", "type MIMEMap = map[string]interface{}\n", "\n", "// Data is the exact structure sent to the front-end (Jupyter...) for displaying.\n", "// It contains all the information needed to graphically show a value.\n", "type Data = struct {\n", "\tData MIMEMap\n", "\tMetadata MIMEMap\n", "\tTransient MIMEMap\n", "}\n", "\n", "// the functions Any() and Auto() accept string, []byte, image.Image, io.Reader, io.WriterTo\n", "func Any(mimeType string, data interface{}) Data // if mimeType is empty, autodetects it\n", "func Auto(data interface{}) Data // autodetects mimeType\n", "\n", "func HTML(html string) Data\n", "func JSON(json map[string]interface{}) Data\n", "func JavaScript(javascript string) Data\n", "func JPEG(jpeg []byte) Data\n", "func Image(img image.Image) Data\n", "func Latex(latex string) Data\n", "func Markdown(markdown string) Data\n", "func Math(latex string) Data // LaTeX formula, without starting and ending '$$' \n", "func PDF(pdf []byte) Data\n", "func PNG(png []byte) Data\n", "func SVG(svg string) Data\n", "\n", "func MakeData(mimeType string, data interface{}) Data\n", "func MakeData3(mimeType string, plaintext string, data interface{}) Data\n", "func MIME(data, metadata MIMEMap) Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Advanced usage

\n", "

gophernotes also provides the following interfaces, mainly targeted at authors of Go libraries.

\n", "

If the only non-nil value returned at prompt has type Data or implements one of these interfaces, it will be automatically rendered graphically

\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "/**\n", " * general interface, allows libraries to fully specify\n", " * how their data is displayed by Jupyter.\n", " * Supports multiple MIME formats.\n", " *\n", " * Note that Data defined above is an alias:\n", " * libraries can implement Renderer without importing gophernotes\n", " */\n", "type Renderer = interface {\n", "\tRender() Data\n", "}\n", "\n", "/**\n", " * simplified interface, allows libraries to specify\n", " * how their data is displayed by Jupyter.\n", " * Supports multiple MIME formats.\n", " *\n", " * Note that MIMEMap defined above is an alias:\n", " * libraries can implement SimpleRenderer without importing gophernotes\n", " */\n", "type SimpleRenderer = interface {\n", "\tSimpleRender() MIMEMap\n", "}\n", "\n", "/**\n", " * specialized interfaces, each is dedicated to a specific MIME type.\n", " *\n", " * They are type aliases to emphasize that method signatures\n", " * are the only important thing, not the interface names.\n", " * Thus libraries can implement them without importing gophernotes\n", " */\n", "type HTMLer = interface {\n", "\tHTML() string\n", "}\n", "type JavaScripter = interface {\n", "\tJavaScript() string\n", "}\n", "type JPEGer = interface {\n", "\tJPEG() []byte\n", "}\n", "type JSONer = interface {\n", "\tJSON() map[string]interface{}\n", "}\n", "type Latexer = interface {\n", "\tLatex() string\n", "}\n", "type Markdowner = interface {\n", "\tMarkdown() string\n", "}\n", "type PNGer = interface {\n", "\tPNG() []byte\n", "}\n", "type PDFer = interface {\n", "\tPDF() []byte\n", "}\n", "type SVGer = interface {\n", "\tSVG() string\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

If you want, interpreted code can implement the above interfaces too:

" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Hello again, world!

" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// implement HTMLer\n", "type H struct {\n", "}\n", "func (h H) HTML() string {\n", " return `

Hello again, world!

`\n", "}\n", "H{}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\partial_\\alpha F^{\\alpha\\beta} = \\mu_0 J^\\beta$" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// implement SimpleRenderer\n", "type S struct {\n", "}\n", "func (s S) SimpleRender() display.MIMEMap {\n", " return display.MIMEMap{\n", " // Maxwell equations, in covariant formulation\n", " display.MIMETypeLatex: `$\\partial_\\alpha F^{\\alpha\\beta} = \\mu_0 J^\\beta$`,\n", " }\n", "}\n", "S{}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Or also, if you want to fully control how your data should be rendered:

" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$G_{\\mu\\nu} = \\frac{8 \\pi G}{c^4} T_{\\mu\\nu}$" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// implement Renderer\n", "type R struct {\n", "}\n", "func (r R) Render() display.Data {\n", " return display.Data{\n", " Data: display.MIMEMap{\n", " // Einstein general relativity equations\n", " display.MIMETypeLatex: `$G_{\\mu\\nu} = \\frac{8 \\pi G}{c^4} T_{\\mu\\nu}$`,\n", " },\n", " }\n", "}\n", "R{}" ] } ], "metadata": { "kernelspec": { "display_name": "Go", "language": "go", "name": "gophernotes" }, "language_info": { "codemirror_mode": "", "file_extension": ".go", "mimetype": "", "name": "go", "nbconvert_exporter": "", "pygments_lexer": "", "version": "go1.18" } }, "nbformat": 4, "nbformat_minor": 4 } gophernotes/examples/Facial_Recognition_MachineBox.ipynb000066400000000000000000000301361452123260300241110ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import (\n", " \"flag\"\n", " \"io/ioutil\"\n", " \"log\"\n", " \"os\"\n", " \"path/filepath\"\n", " \"strings\"\n", " \"time\"\n", " \"unicode\"\n", " \"fmt\"\n", " \"encoding/json\"\n", "\n", " \"github.com/machinebox/sdk-go/facebox\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Connect to MachineBox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To perform facial recognition, we are going to use MachineBox's FaceBox. If you haven't heard of MachineBox, [check them out](https://machinebox.io/)! They provide state of the art machine learning technology inside a Docker container which you can run, deploy and scale.\n", "\n", "Here we will assume that we have MachineBox's facebox running locally." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "faceboxClient := facebox.New(\"http://localhost:8080\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train FaceBox to recognize a face" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now going to walk over images of faces in a directory and provide these faces to facebox. FaceBox will learn how to recognize these faces in images. The faces that we are going to use here are of someone that is probably familiar to most:\n", "\n", "trump1.jpg | trump2.jpg | trump3.jpg \n", ":-------------------------:|:-------------------------:|:-------------------------:\n", "![](https://raw.githubusercontent.com/dwhitena/pach-machine-box/master/data/train/faces1/trump1.jpg) | ![](https://raw.githubusercontent.com/dwhitena/pach-machine-box/master/data/train/faces1/trump2.jpg) | ![](https://raw.githubusercontent.com/dwhitena/pach-machine-box/master/data/train/faces1/trump3.jpg)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "inDir := \"/home/dwhitena/go/src/github.com/dwhitena/pach-machine-box/data/train/faces1\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "ERROR", "evalue": "// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n", "output_type": "error", "traceback": [ "// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n// warning: redefined identifier: err\n// warning: call to deferred function f.Close() returned [] values, expecting zero: %!v(MISSING)\n" ] } ], "source": [ "// Walk over images in the training directory.\n", "if err := filepath.Walk(inDir, func(path string, info os.FileInfo, err error) error {\n", "\n", " // Skip any directories.\n", " if info.IsDir() {\n", " return nil\n", " }\n", "\n", " // Open the training image file.\n", " f, err := os.Open(filepath.Join(inDir, info.Name()))\n", " if err != nil {\n", " return err\n", " }\n", " defer f.Close()\n", "\n", " // Teach FaceBox the input image.\n", " if err := faceboxClient.Teach(f, info.Name(), \"trump\"); err != nil {\n", " return err\n", " }\n", "\n", " return nil\n", "}); err != nil {\n", " log.Println(err)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using the trained FaceBox to recognize a face" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have trained FaceBox to recognize our face of interest, we can try to identify this face in an image. For example, let's try to recognize this face in the following image:\n", "\n", "![](https://raw.githubusercontent.com/dwhitena/pach-machine-box/master/data/unidentified/image1.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's create a struct that will allow us to unmarshal the JSON response from FaceBox. This JSON response should include an indication that faces where detected along with labels of recognized faces and their locations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "// IdentifiedFaces includes information about the faces\n", "// identified in an image.\n", "type IdentifiedFaces struct {\n", " Success bool `json:\"success\"`\n", " FacesCount int `json:\"facesCount\"`\n", " Faces []facebox.Face `json:\"faces\"`\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can use the `Check()` method on the FaceBox client to check the image for recognized faces:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "ERROR", "evalue": "// warning: redefined identifier: err\n", "output_type": "error", "traceback": [ "// warning: redefined identifier: err\n" ] } ], "source": [ "// Open the input image.\n", "f, err := os.Open(\"/home/dwhitena/go/src/github.com/dwhitena/pach-machine-box/data/unidentified/image1.jpg\")\n", "if err != nil {\n", " log.Println(err)\n", "}\n", "defer f.Close()\n", "\n", "// Teach FaceBox the input image.\n", "faces, err := faceboxClient.Check(f)\n", "if err != nil {\n", " log.Println(err)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can output the detected face information:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"Success\": true,\n", " \"FacesCount\": 13,\n", " \"Faces\": [\n", " {\n", " \"Rect\": {\n", " \"Top\": 199,\n", " \"Left\": 677,\n", " \"Width\": 107,\n", " \"Height\": 108\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 96,\n", " \"Left\": 1808,\n", " \"Width\": 89,\n", " \"Height\": 90\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 163,\n", " \"Left\": 509,\n", " \"Width\": 108,\n", " \"Height\": 108\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 186,\n", " \"Left\": 2186,\n", " \"Width\": 89,\n", " \"Height\": 89\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 166,\n", " \"Left\": 1638,\n", " \"Width\": 90,\n", " \"Height\": 89\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 116,\n", " \"Left\": 1453,\n", " \"Width\": 107,\n", " \"Height\": 107\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 405,\n", " \"Left\": 1131,\n", " \"Width\": 89,\n", " \"Height\": 89\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 206,\n", " \"Left\": 1300,\n", " \"Width\": 90,\n", " \"Height\": 89\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 176,\n", " \"Left\": 1957,\n", " \"Width\": 90,\n", " \"Height\": 89\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 495,\n", " \"Left\": 1462,\n", " \"Width\": 62,\n", " \"Height\": 62\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 1158,\n", " \"Left\": 2181,\n", " \"Width\": 62,\n", " \"Height\": 63\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 175,\n", " \"Left\": 963,\n", " \"Width\": 108,\n", " \"Height\": 108\n", " },\n", " \"ID\": \"trump3.jpg\",\n", " \"Name\": \"trump\",\n", " \"Matched\": true\n", " },\n", " {\n", " \"Rect\": {\n", " \"Top\": 544,\n", " \"Left\": 1647,\n", " \"Width\": 75,\n", " \"Height\": 75\n", " },\n", " \"ID\": \"\",\n", " \"Name\": \"\",\n", " \"Matched\": false\n", " }\n", " ]\n", "}\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "ename": "ERROR", "evalue": "// warning: redefined identifier: err\n", "output_type": "error", "traceback": [ "// warning: redefined identifier: err\n" ] } ], "source": [ "// Prepare the output.\n", "output := IdentifiedFaces{\n", " Success: true,\n", " FacesCount: len(faces),\n", " Faces: faces,\n", "}\n", "\n", "// Marshal the output.\n", "outputData, err := json.MarshalIndent(output, \"\", \" \")\n", "if err != nil {\n", " return log.Println(err)\n", "}\n", "\n", "fmt.Println(string(outputData))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Go", "language": "go", "name": "gophernotes" }, "language_info": { "codemirror_mode": "", "file_extension": ".go", "mimetype": "", "name": "go", "nbconvert_exporter": "", "pygments_lexer": "", "version": "go1.9" } }, "nbformat": 4, "nbformat_minor": 2 } gophernotes/examples/Matrix_Operations.ipynb000066400000000000000000000074721452123260300217530ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import (\n", " \"fmt\"\n", " \"math\"\n", "\n", " \"gonum.org/v1/gonum/mat\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create some matrices" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "// Create two matrices of the same size, a and b.\n", "a := mat.NewDense(3, 3, []float64{1, 2, 3, 0, 4, 5, 0, 0, 6})\n", "b := mat.NewDense(3, 3, []float64{8, 9, 10, 1, 4, 2, 9, 0, 2})\n", "c := mat.NewDense(3, 2, []float64{3, 2, 1, 4, 0, 8})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "a = ⎡1 2 3⎤\n", " ⎢0 4 5⎥\n", " ⎣0 0 6⎦\n", "\n", "b = ⎡ 8 9 10⎤\n", " ⎢ 1 4 2⎥\n", " ⎣ 9 0 2⎦\n", "\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fa := mat.Formatted(a, mat.Prefix(\" \"))\n", "fb := mat.Formatted(b, mat.Prefix(\" \"))\n", "fmt.Printf(\"\\na = %0.4v\\n\\n\", fa)\n", "fmt.Printf(\"b = %0.4v\\n\\n\", fb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrix addition, multiplication, powers, and apply" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "d = a + b = ⎡ 9 11 13⎤\n", " ⎢ 1 8 7⎥\n", " ⎣ 9 0 8⎦\n", "\n", "f = a c = ⎡ 5 34⎤\n", " ⎢ 4 56⎥\n", " ⎣ 0 48⎦\n", "\n", "g = a^5 = ⎡ 1 682 1.074e+04⎤\n", " ⎢ 0 1024 1.688e+04⎥\n", " ⎣ 0 0 7776⎦\n", "\n", "h = sqrt(a) = ⎡ 1 1.414 1.732⎤\n", " ⎢ 0 2 2.236⎥\n", " ⎣ 0 0 2.449⎦\n", "\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Add a and b.\n", "d := new(mat.Dense)\n", "d.Add(a, b)\n", "fd := mat.Formatted(d, mat.Prefix(\" \"))\n", "fmt.Printf(\"d = a + b = %0.4v\\n\\n\", fd)\n", "\n", "// Multiply a and c.\n", "f := new(mat.Dense)\n", "f.Mul(a, c)\n", "ff := mat.Formatted(f, mat.Prefix(\" \"))\n", "fmt.Printf(\"f = a c = %0.4v\\n\\n\", ff)\n", "\n", "// Raising a matrix to a power.\n", "g := new(mat.Dense)\n", "g.Pow(a, 5)\n", "fg := mat.Formatted(g, mat.Prefix(\" \"))\n", "fmt.Printf(\"g = a^5 = %0.4v\\n\\n\", fg)\n", "\n", "// Apply a function to each of the elements of a.\n", "h := new(mat.Dense)\n", "sqrt := func(_, _ int, v float64) float64 { return math.Sqrt(v) }\n", "h.Apply(sqrt, a)\n", "fh := mat.Formatted(h, mat.Prefix(\" \"))\n", "fmt.Printf(\"h = sqrt(a) = %0.4v\\n\\n\", fh)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Go", "language": "go", "name": "gophernotes" }, "language_info": { "codemirror_mode": "", "file_extension": ".go", "mimetype": "", "name": "go", "nbconvert_exporter": "", "pygments_lexer": "", "version": "go1.9" } }, "nbformat": 4, "nbformat_minor": 2 } gophernotes/examples/Worker_Pools.ipynb000066400000000000000000000067361452123260300207330ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Worker Pools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we’ll look at how to implement a worker pool using goroutines and channels.\n", "\n", "Comments from [Go by Example](https://gobyexample.com/worker-pools)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import (\n", " \"fmt\"\n", " \"time\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Worker Function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here’s the worker, of which we’ll run several concurrent instances. These workers will receive work on the jobs channel and send the corresponding results on results. We’ll sleep a second per job to simulate an expensive task." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "func worker(id int, jobs <-chan int, results chan<- int) {\n", " for j := range jobs {\n", " fmt.Println(\"worker\", id, \"processing job\", j)\n", " time.Sleep(time.Second)\n", " results <- j * 2\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Utilize Workers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to use our pool of workers we need to send them work and collect their results. We make 2 channels for this." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "jobs := make(chan int, 100)\n", "results := make(chan int, 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, start up 3 workers, initially blocked because there are no jobs yet:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for w := 1; w <= 3; w++ {\n", " go worker(w, jobs, results)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Send 9 jobs and then close that channel to indicate that’s all the work we have:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "worker 4 processing job 1\n", "worker 4 processing job 2\n", "worker 4 processing job 3\n", "worker 4 processing job 4\n", "worker 4 processing job 5\n", "worker 4 processing job 6\n", "worker 4 processing job 7\n", "worker 4 processing job 8\n", "worker 4 processing job 9\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for j := 1; j <= 9; j++ {\n", " jobs <- j\n", "}\n", "time.Sleep(3*time.Second)\n", "close(jobs)" ] } ], "metadata": { "kernelspec": { "display_name": "Go", "language": "go", "name": "gophernotes" }, "language_info": { "codemirror_mode": "", "file_extension": ".go", "mimetype": "", "name": "go", "nbconvert_exporter": "", "pygments_lexer": "", "version": "go1.9" } }, "nbformat": 4, "nbformat_minor": 2 } gophernotes/files/000077500000000000000000000000001452123260300145135ustar00rootroot00000000000000gophernotes/files/gophernotes-gopher.png000066400000000000000000003554241452123260300210550ustar00rootroot00000000000000PNG  IHDResBIT|d pHYs@@r.tEXtSoftwarewww.inkscape.org< IDATxwxTEݚCI{HA k/?{*/PRAzB u˖7DBdsw yr̜!B!D]5(N_ J!B!OVV|\k*@!B! 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?&&U9i1dh8U0O%qu,ٯS23 2VG-Ǝ.#g.ڍ$7VKjgꂲf) R[p+oS+'ײАƉ*/-'d-&3+]n¥kZ330ᭀpYZ51#3^vs 2E0j0*/2;I}ыM<Ji-Yo >#0.4?P1`>BB=a)GNj@ 4FwӟvrGݹa 4B蔶B'L)N6ErNwu--qE1+#ICCְGC5=G5V1YfWΔ,c u(5['"aCJ{\%nh:ﵗ ߍJv(W"5c V@6H*bO68qx$-1grĊ UvpmhvEdC4lI__Xv(^.ge-vNeAvZu!5ɵsoiѺdW7]Ws.w?Im,xwvWv~x6|6H6|ǶEps~/tg.vIemZ 68 L X<7wwk\d|oxwT2W]Rƛ;3:?uO[QgǸLIwG7;p8YMX$s'&A@+GZ5BiW9w!.\4SVpnwj9Sڨ)N9\!n6~ 7Yqڸ{,iz#fINgo0c>Wz{l2Ʀe^g1k/l#홱:rHzlJxvxM'zsI3lƝ9kw)O;gX?!ٶ9^y e쫟;837{q?/ۻu-io5'Gjl{SG0L~/wHznă&ꠚgv3Ұ6|vhi/>z]%|<|[lۼj*O}l]a!]; U+WfI4|R(5Xf7w ,/Ńן0!SP"xFOl\zc>WL.[E=~{VWbyw>?-"F˰޲>܌֮\qW&qBs8 R@! ,;gophernotes/fixtures/000077500000000000000000000000001452123260300152625ustar00rootroot00000000000000gophernotes/fixtures/connection_file.json000066400000000000000000000003661452123260300213200ustar00rootroot00000000000000{ "control_port": 50160, "shell_port": 57503, "transport": "tcp", "signature_scheme": "hmac-sha256", "stdin_port": 52597, "hb_port": 42540, "ip": "127.0.0.1", "iopub_port": 40885, "key": "a0436f6c-1916-498b-8eb9-e81ab9368e84" } gophernotes/go.mod000066400000000000000000000003151452123260300145160ustar00rootroot00000000000000module github.com/gopherdata/gophernotes go 1.13 require ( github.com/cosmos72/gomacro v0.0.0-20231101204900-3dc08f5f8fea github.com/go-zeromq/zmq4 v0.15.0 github.com/gofrs/uuid v4.4.0+incompatible ) gophernotes/go.sum000066400000000000000000000143761452123260300145570ustar00rootroot00000000000000github.com/cosmos72/gomacro v0.0.0-20231101204900-3dc08f5f8fea h1:UaXbB+Mv9i2roIj0Ka1ATyHrC8JFnycDdj9dZ9RK/sg= github.com/cosmos72/gomacro v0.0.0-20231101204900-3dc08f5f8fea/go.mod h1:5e35wR9cfXppxd99a3QlRkj0E5qVg2nSA8z5XG0biTc= github.com/go-zeromq/goczmq/v4 v4.2.2 h1:HAJN+i+3NW55ijMJJhk7oWxHKXgAuSBkoFfvr8bYj4U= github.com/go-zeromq/goczmq/v4 v4.2.2/go.mod h1:Sm/lxrfxP/Oxqs0tnHD6WAhwkWrx+S+1MRrKzcxoaYE= github.com/go-zeromq/zmq4 v0.15.0 h1:SLqukpmLTx0JsLaOaCCjwy5eBdfJ+ouJX/677HoFbJM= github.com/go-zeromq/zmq4 v0.15.0/go.mod h1:sD47DcXifeUFsVTB2ps8ijqTpEuTAlYgfuLoiWEXdCE= github.com/gofrs/uuid v4.4.0+incompatible h1:3qXRTX8/NbyulANqlc0lchS1gqAVxRgsuW1YrTJupqA= github.com/gofrs/uuid v4.4.0+incompatible/go.mod h1:b2aQJv3Z4Fp6yNu3cdSllBxTCLRxnplIgP/c0N/04lM= github.com/mattn/go-runewidth v0.0.3/go.mod h1:LwmH8dsx7+W8Uxz3IHJYH5QSwggIsqBzpuz5H//U1FU= github.com/mattn/go-runewidth v0.0.15 h1:UNAjwbU9l54TA3KzvqLGxwWjHmMgBUVhBiTjelZgg3U= github.com/mattn/go-runewidth v0.0.15/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w= github.com/peterh/liner v1.2.2 h1:aJ4AOodmL+JxOZZEL2u9iJf8omNRpqHc/EbrK+3mAXw= github.com/peterh/liner v1.2.2/go.mod h1:xFwJyiKIXJZUKItq5dGHZSTBRAuG/CpeNpWLyiNRNwI= github.com/rivo/uniseg v0.2.0 h1:S1pD9weZBuJdFmowNwbpi7BJ8TNftyUImj/0WQi72jY= github.com/rivo/uniseg v0.2.0/go.mod h1:J6wj4VEh+S6ZtnVlnTBMWIodfgj8LQOQFoIToxlJtxc= github.com/yuin/goldmark v1.4.13/go.mod h1:6yULJ656Px+3vBD8DxQVa3kxgyrAnzto9xy5taEt/CY= golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w= golang.org/x/crypto v0.0.0-20210921155107-089bfa567519/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc= golang.org/x/crypto v0.13.0/go.mod h1:y6Z2r+Rw4iayiXXAIxJIDAJ1zMW4yaTpebo8fPOliYc= golang.org/x/crypto v0.14.0/go.mod h1:MVFd36DqK4CsrnJYDkBA3VC4m2GkXAM0PvzMCn4JQf4= golang.org/x/mod v0.6.0-dev.0.20220419223038-86c51ed26bb4/go.mod h1:jJ57K6gSWd91VN4djpZkiMVwK6gcyfeH4XE8wZrZaV4= golang.org/x/mod v0.8.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs= golang.org/x/mod v0.12.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs= golang.org/x/mod v0.13.0 h1:I/DsJXRlw/8l/0c24sM9yb0T4z9liZTduXvdAWYiysY= golang.org/x/mod v0.13.0/go.mod h1:hTbmBsO62+eylJbnUtE2MGJUyE7QWk4xUqPFrRgJ+7c= golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s= golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg= golang.org/x/net v0.0.0-20220722155237-a158d28d115b/go.mod h1:XRhObCWvk6IyKnWLug+ECip1KBveYUHfp+8e9klMJ9c= golang.org/x/net v0.6.0/go.mod h1:2Tu9+aMcznHK/AK1HMvgo6xiTLG5rD5rZLDS+rp2Bjs= golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg= golang.org/x/net v0.15.0/go.mod h1:idbUs1IY1+zTqbi8yxTbhexhEEk5ur9LInksu6HrEpk= golang.org/x/net v0.16.0/go.mod h1:NxSsAGuq816PNPmqtQdLE42eU2Fs7NoRIZrHJAlaCOE= golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20220601150217-0de741cfad7f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= golang.org/x/sync v0.3.0/go.mod h1:FU7BRWz2tNW+3quACPkgCx/L+uEAv1htQ0V83Z9Rj+Y= golang.org/x/sync v0.4.0 h1:zxkM55ReGkDlKSM+Fu41A+zmbZuaPVbGMzvvdUPznYQ= golang.org/x/sync v0.4.0/go.mod h1:FU7BRWz2tNW+3quACPkgCx/L+uEAv1htQ0V83Z9Rj+Y= golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY= golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs= golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.0.0-20211117180635-dee7805ff2e1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.0.0-20220722155257-8c9f86f7a55f/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.8.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.12.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/sys v0.13.0 h1:Af8nKPmuFypiUBjVoU9V20FiaFXOcuZI21p0ycVYYGE= golang.org/x/sys v0.13.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg= golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo= golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8= golang.org/x/term v0.5.0/go.mod h1:jMB1sMXY+tzblOD4FWmEbocvup2/aLOaQEp7JmGp78k= golang.org/x/term v0.8.0/go.mod h1:xPskH00ivmX89bAKVGSKKtLOWNx2+17Eiy94tnKShWo= golang.org/x/term v0.12.0/go.mod h1:owVbMEjm3cBLCHdkQu9b1opXd4ETQWc3BhuQGKgXgvU= golang.org/x/term v0.13.0/go.mod h1:LTmsnFJwVN6bCy1rVCoS+qHT1HhALEFxKncY3WNNh4U= golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ= golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ= golang.org/x/text v0.3.7/go.mod h1:u+2+/6zg+i71rQMx5EYifcz6MCKuco9NR6JIITiCfzQ= golang.org/x/text v0.7.0/go.mod h1:mrYo+phRRbMaCq/xk9113O4dZlRixOauAjOtrjsXDZ8= golang.org/x/text v0.9.0/go.mod h1:e1OnstbJyHTd6l/uOt8jFFHp6TRDWZR/bV3emEE/zU8= golang.org/x/text v0.13.0 h1:ablQoSUd0tRdKxZewP80B+BaqeKJuVhuRxj/dkrun3k= golang.org/x/text v0.13.0/go.mod h1:TvPlkZtksWOMsz7fbANvkp4WM8x/WCo/om8BMLbz+aE= golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ= golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo= golang.org/x/tools v0.1.12/go.mod h1:hNGJHUnrk76NpqgfD5Aqm5Crs+Hm0VOH/i9J2+nxYbc= golang.org/x/tools v0.6.0/go.mod h1:Xwgl3UAJ/d3gWutnCtw505GrjyAbvKui8lOU390QaIU= golang.org/x/tools v0.13.0/go.mod h1:HvlwmtVNQAhOuCjW7xxvovg8wbNq7LwfXh/k7wXUl58= golang.org/x/tools v0.14.0 h1:jvNa2pY0M4r62jkRQ6RwEZZyPcymeL9XZMLBbV7U2nc= golang.org/x/tools v0.14.0/go.mod h1:uYBEerGOWcJyEORxN+Ek8+TT266gXkNlHdJBwexUsBg= golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= gophernotes/image.go000066400000000000000000000016331452123260300150250ustar00rootroot00000000000000package main import ( "bytes" "image" "image/png" ) // Image converts an image.Image to DisplayData containing PNG []byte, // or to DisplayData containing error if the conversion fails func Image(img image.Image) Data { bytes, mimeType, err := encodePng(img) if err != nil { return makeDataErr(err) } return Data{ Data: MIMEMap{ mimeType: bytes, }, Metadata: MIMEMap{ mimeType: imageMetadata(img), }, } } // encodePng converts an image.Image to PNG []byte func encodePng(img image.Image) (data []byte, mimeType string, err error) { var buf bytes.Buffer err = png.Encode(&buf, img) if err != nil { return nil, "", err } return buf.Bytes(), MIMETypePNG, nil } // imageMetadata returns image size, represented as MIMEMap{"width": width, "height": height} func imageMetadata(img image.Image) MIMEMap { rect := img.Bounds() return MIMEMap{ "width": rect.Dx(), "height": rect.Dy(), } } gophernotes/imports/000077500000000000000000000000001452123260300151065ustar00rootroot00000000000000gophernotes/imports/README.md000066400000000000000000000006161452123260300163700ustar00rootroot00000000000000The command `import` will write here import files for third-party packages if they are to be imported and statically linked into gophernotes. This is currently needed on Windows, *BSD, Android etc. (to be exact, as of Go 1.13 it's needed on all operating system except Linux and Mac OS X). It is safe to remove files in this directory, as long as you **keep at least** README.md and a_package.go gophernotes/imports/a_package.go000066400000000000000000000010751452123260300173330ustar00rootroot00000000000000package imports import ( "github.com/cosmos72/gomacro/base/paths" "github.com/cosmos72/gomacro/imports" ) type PackageUnderlying = imports.PackageUnderlying type Package = imports.Package type PackageMap = imports.PackageMap // imports.Packages is a map, copying it only makes a reference // => Packages and imports.Packages are kept in sync automatically var Packages = imports.Packages // used to locate this package path via reflection type findMe struct{} func init() { // tell gomacro where to write import files paths.SymbolFromImportsPackage = findMe{} } gophernotes/kernel.go000066400000000000000000000515751452123260300152350ustar00rootroot00000000000000package main import ( "bufio" "context" "encoding/json" "errors" "fmt" "io" "io/ioutil" "log" "os" "os/exec" "runtime" "strings" "sync" "time" "github.com/go-zeromq/zmq4" "github.com/cosmos72/gomacro/ast2" "github.com/cosmos72/gomacro/base" basereflect "github.com/cosmos72/gomacro/base/reflect" interp "github.com/cosmos72/gomacro/fast" mp "github.com/cosmos72/gomacro/go/parser" "github.com/cosmos72/gomacro/xreflect" // compile and link files generated in imports/ _ "github.com/gopherdata/gophernotes/imports" ) // ExecCounter is incremented each time we run user code in the notebook. var ExecCounter int // ConnectionInfo stores the contents of the kernel connection // file created by Jupyter. type ConnectionInfo struct { SignatureScheme string `json:"signature_scheme"` Transport string `json:"transport"` StdinPort int `json:"stdin_port"` ControlPort int `json:"control_port"` IOPubPort int `json:"iopub_port"` HBPort int `json:"hb_port"` ShellPort int `json:"shell_port"` Key string `json:"key"` IP string `json:"ip"` } // Socket wraps a zmq socket with a lock which should be used to control write access. type Socket struct { Socket zmq4.Socket Lock *sync.Mutex } // SocketGroup holds the sockets needed to communicate with the kernel, // and the key for message signing. type SocketGroup struct { ShellSocket Socket ControlSocket Socket StdinSocket Socket IOPubSocket Socket HBSocket Socket Key []byte } // KernelLanguageInfo holds information about the language that this kernel executes code in. type kernelLanguageInfo struct { Name string `json:"name"` Version string `json:"version"` MIMEType string `json:"mimetype"` FileExtension string `json:"file_extension"` PygmentsLexer string `json:"pygments_lexer"` CodeMirrorMode string `json:"codemirror_mode"` NBConvertExporter string `json:"nbconvert_exporter"` } // HelpLink stores data to be displayed in the help menu of the notebook. type helpLink struct { Text string `json:"text"` URL string `json:"url"` } // KernelInfo holds information about the igo kernel, for kernel_info_reply messages. type kernelInfo struct { ProtocolVersion string `json:"protocol_version"` Implementation string `json:"implementation"` ImplementationVersion string `json:"implementation_version"` LanguageInfo kernelLanguageInfo `json:"language_info"` Banner string `json:"banner"` HelpLinks []helpLink `json:"help_links"` } // shutdownReply encodes a boolean indication of shutdown/restart. type shutdownReply struct { Restart bool `json:"restart"` } // isCompleteReply holds information about the statement is complete or not, for is_complete_reply messages. type isCompleteReply struct { Status string `json:"status"` Indent string `json:"indent"` } const ( kernelStarting = "starting" kernelBusy = "busy" kernelIdle = "idle" ) // RunWithSocket invokes the `run` function after acquiring the `Socket.Lock` and releases the lock when done. func (s *Socket) RunWithSocket(run func(socket zmq4.Socket) error) error { s.Lock.Lock() defer s.Lock.Unlock() return run(s.Socket) } type Kernel struct { ir *interp.Interp display *interp.Import // map name -> HTMLer, JSONer, Renderer... // used to convert interpreted types to one of these interfaces render map[string]xreflect.Type } // runKernel is the main entry point to start the kernel. func runKernel(connectionFile string) { // Create a new interpreter for evaluating notebook code. ir := interp.New() // Throw out the error/warning messages that gomacro outputs writes to these streams. ir.Comp.Stdout = ioutil.Discard ir.Comp.Stderr = ioutil.Discard // Inject the "display" package to render HTML, JSON, PNG, JPEG, SVG... from interpreted code // maybe a dot-import is easier to use? display := importPackage(ir, "display", "display") // Inject the stub "Display" function. declare a variable // instead of a function, because we want to later change // its value to the closure that holds a reference to msgReceipt ir.DeclVar("Display", nil, stubDisplay) // Parse the connection info. var connInfo ConnectionInfo connData, err := ioutil.ReadFile(connectionFile) if err != nil { log.Fatal(err) } if err = json.Unmarshal(connData, &connInfo); err != nil { log.Fatal(err) } // Set up the ZMQ sockets through which the kernel will communicate. sockets, err := prepareSockets(connInfo) if err != nil { log.Fatal(err) } // TODO connect all channel handlers to a WaitGroup to ensure shutdown before returning from runKernel. // Start up the heartbeat handler. startHeartbeat(sockets.HBSocket, &sync.WaitGroup{}) // TODO gracefully shutdown the heartbeat handler on kernel shutdown by closing the chan returned by startHeartbeat. type msgType struct { Msg zmq4.Msg Err error } var ( shell = make(chan msgType) stdin = make(chan msgType) ctl = make(chan msgType) quit = make(chan int) ) defer close(quit) poll := func(msgs chan msgType, sck zmq4.Socket) { defer close(msgs) for { msg, err := sck.Recv() select { case msgs <- msgType{Msg: msg, Err: err}: case <-quit: return } } } go poll(shell, sockets.ShellSocket.Socket) go poll(stdin, sockets.StdinSocket.Socket) go poll(ctl, sockets.ControlSocket.Socket) kernel := Kernel{ ir, display, nil, } kernel.initRenderers() // Start a message receiving loop. for { select { case v := <-shell: // Handle shell messages. if v.Err != nil { log.Println(v.Err) continue } msg, ids, err := WireMsgToComposedMsg(v.Msg.Frames, sockets.Key) if err != nil { log.Println(err) return } kernel.handleShellMsg(msgReceipt{msg, ids, sockets}) case <-stdin: // TODO Handle stdin socket. continue case v := <-ctl: if v.Err != nil { log.Println(v.Err) return } msg, ids, err := WireMsgToComposedMsg(v.Msg.Frames, sockets.Key) if err != nil { log.Println(err) return } kernel.handleShellMsg(msgReceipt{msg, ids, sockets}) } } } func importPackage(ir *interp.Interp, path string, alias string) *interp.Import { packages, err := ir.ImportPackagesOrError( map[string]interp.PackageName{ path: interp.PackageName(alias), }) if err != nil { log.Print(err) } return packages[path] } // prepareSockets sets up the ZMQ sockets through which the kernel // will communicate. func prepareSockets(connInfo ConnectionInfo) (SocketGroup, error) { // Initialize the socket group. var ( sg SocketGroup err error ctx = context.Background() ) // Create the shell socket, a request-reply socket that may receive messages from multiple frontend for // code execution, introspection, auto-completion, etc. sg.ShellSocket.Socket = zmq4.NewRouter(ctx) sg.ShellSocket.Lock = &sync.Mutex{} // Create the control socket. This socket is a duplicate of the shell socket where messages on this channel // should jump ahead of queued messages on the shell socket. sg.ControlSocket.Socket = zmq4.NewRouter(ctx) sg.ControlSocket.Lock = &sync.Mutex{} // Create the stdin socket, a request-reply socket used to request user input from a front-end. This is analogous // to a standard input stream. sg.StdinSocket.Socket = zmq4.NewRouter(ctx) sg.StdinSocket.Lock = &sync.Mutex{} // Create the iopub socket, a publisher for broadcasting data like stdout/stderr output, displaying execution // results or errors, kernel status, etc. to connected subscribers. sg.IOPubSocket.Socket = zmq4.NewPub(ctx) sg.IOPubSocket.Lock = &sync.Mutex{} // Create the heartbeat socket, a request-reply socket that only allows alternating recv-send (request-reply) // calls. It should echo the byte strings it receives to let the requester know the kernel is still alive. sg.HBSocket.Socket = zmq4.NewRep(ctx) sg.HBSocket.Lock = &sync.Mutex{} // Bind the sockets. address := fmt.Sprintf("%v://%v:%%v", connInfo.Transport, connInfo.IP) err = sg.ShellSocket.Socket.Listen(fmt.Sprintf(address, connInfo.ShellPort)) if err != nil { return sg, fmt.Errorf("could not listen on shell-socket: %w", err) } err = sg.ControlSocket.Socket.Listen(fmt.Sprintf(address, connInfo.ControlPort)) if err != nil { return sg, fmt.Errorf("could not listen on control-socket: %w", err) } err = sg.StdinSocket.Socket.Listen(fmt.Sprintf(address, connInfo.StdinPort)) if err != nil { return sg, fmt.Errorf("could not listen on stdin-socket: %w", err) } err = sg.IOPubSocket.Socket.Listen(fmt.Sprintf(address, connInfo.IOPubPort)) if err != nil { return sg, fmt.Errorf("could not listen on iopub-socket: %w", err) } err = sg.HBSocket.Socket.Listen(fmt.Sprintf(address, connInfo.HBPort)) if err != nil { return sg, fmt.Errorf("could not listen on hbeat-socket: %w", err) } // Set the message signing key. sg.Key = []byte(connInfo.Key) return sg, nil } // handleShellMsg responds to a message on the shell ROUTER socket. func (kernel *Kernel) handleShellMsg(receipt msgReceipt) { // Tell the front-end that the kernel is working and when finished notify the // front-end that the kernel is idle again. if err := receipt.PublishKernelStatus(kernelBusy); err != nil { log.Printf("Error publishing kernel status 'busy': %v\n", err) } defer func() { if err := receipt.PublishKernelStatus(kernelIdle); err != nil { log.Printf("Error publishing kernel status 'idle': %v\n", err) } }() ir := kernel.ir switch receipt.Msg.Header.MsgType { case "kernel_info_request": if err := sendKernelInfo(receipt); err != nil { log.Fatal(err) } case "is_complete_request": if err := kernel.handleIsCompleteRequest(receipt); err != nil { log.Fatal(err) } case "complete_request": if err := handleCompleteRequest(ir, receipt); err != nil { log.Fatal(err) } case "execute_request": if err := kernel.handleExecuteRequest(receipt); err != nil { log.Fatal(err) } case "shutdown_request": handleShutdownRequest(receipt) default: log.Println("Unhandled shell message: ", receipt.Msg.Header.MsgType) } } // sendKernelInfo sends a kernel_info_reply message. func sendKernelInfo(receipt msgReceipt) error { return receipt.Reply("kernel_info_reply", kernelInfo{ ProtocolVersion: ProtocolVersion, Implementation: "gophernotes", ImplementationVersion: Version, Banner: fmt.Sprintf("Go kernel: gophernotes - v%s", Version), LanguageInfo: kernelLanguageInfo{ Name: "go", Version: runtime.Version(), FileExtension: ".go", }, HelpLinks: []helpLink{ {Text: "Go", URL: "https://golang.org/"}, {Text: "gophernotes", URL: "https://github.com/gopherdata/gophernotes"}, }, }, ) } // checkComplete checks whether the `code` is complete or not. func checkComplete(code string, ir *interp.Interp) (status, indent string) { status, indent = "unknown", "" if len(code) == 0 { return status, indent } readline := base.MakeBufReadline(bufio.NewReader(strings.NewReader(code))) for { _, _, err := base.ReadMultiline(readline, base.ReadOptions(0), "") if err == io.EOF { return "complete", indent } else if err == io.ErrUnexpectedEOF { return "incomplete", indent } else if err != nil { return "invalid", indent } } var parser mp.Parser g := ir.Comp parser.Configure(g.ParserMode, g.MacroChar) parser.Init(g.Fileset, g.Filepath, g.Line, []byte(code)) _, err := parser.Parse() if err != nil { status = "invalid" } else { status = "complete" } return status, indent } // handleIsCompleteRequest sends a is_complete_reply message. func (kernel *Kernel) handleIsCompleteRequest(receipt msgReceipt) error { // Extract the data from the request. reqcontent := receipt.Msg.Content.(map[string]interface{}) code := reqcontent["code"].(string) status, indent := checkComplete(code, kernel.ir) return receipt.Reply("is_complete_reply", isCompleteReply{ Status: status, Indent: indent, }, ) } // handleExecuteRequest runs code from an execute_request method, // and sends the various reply messages. func (kernel *Kernel) handleExecuteRequest(receipt msgReceipt) error { // Extract the data from the request. reqcontent := receipt.Msg.Content.(map[string]interface{}) code := reqcontent["code"].(string) silent := reqcontent["silent"].(bool) if !silent { ExecCounter++ } // Prepare the map that will hold the reply content. content := make(map[string]interface{}) content["execution_count"] = ExecCounter // Tell the front-end what the kernel is about to execute. if err := receipt.PublishExecutionInput(ExecCounter, code); err != nil { log.Printf("Error publishing execution input: %v\n", err) } // Redirect the standard out from the REPL. oldStdout := os.Stdout rOut, wOut, err := os.Pipe() if err != nil { return err } os.Stdout = wOut // Redirect the standard error from the REPL. oldStderr := os.Stderr rErr, wErr, err := os.Pipe() if err != nil { return err } os.Stderr = wErr var writersWG sync.WaitGroup writersWG.Add(2) jupyterStdOut := JupyterStreamWriter{StreamStdout, &receipt} jupyterStdErr := JupyterStreamWriter{StreamStderr, &receipt} outerr := OutErr{&jupyterStdOut, &jupyterStdErr} // Forward all data written to stdout/stderr to the front-end. go func() { defer writersWG.Done() io.Copy(&jupyterStdOut, rOut) }() go func() { defer writersWG.Done() io.Copy(&jupyterStdErr, rErr) }() // inject the actual "Display" closure that displays multimedia data in Jupyter ir := kernel.ir displayPlace := ir.ValueOf("Display") displayPlace.Set(xreflect.ValueOf(receipt.PublishDisplayData)) defer func() { // remove the closure before returning displayPlace.Set(xreflect.ValueOf(stubDisplay)) }() // eval vals, types, executionErr := doEval(ir, outerr, code) // Close and restore the streams. wOut.Close() os.Stdout = oldStdout wErr.Close() os.Stderr = oldStderr // Wait for the writers to finish forwarding the data. writersWG.Wait() if executionErr == nil { // if the only non-nil value should be auto-rendered graphically, render it data := kernel.autoRenderResults(vals, types) content["status"] = "ok" content["user_expressions"] = make(map[string]string) if !silent && len(data.Data) != 0 { // Publish the result of the execution. if err := receipt.PublishExecutionResult(ExecCounter, data); err != nil { log.Printf("Error publishing execution result: %v\n", err) } } } else { content["status"] = "error" content["ename"] = "ERROR" content["evalue"] = executionErr.Error() content["traceback"] = nil if err := receipt.PublishExecutionError(executionErr.Error(), []string{executionErr.Error()}); err != nil { log.Printf("Error publishing execution error: %v\n", err) } } // Send the output back to the notebook. return receipt.Reply("execute_reply", content) } // doEval evaluates the code in the interpreter. This function captures an uncaught panic // as well as the values of the last statement/expression. func doEval(ir *interp.Interp, outerr OutErr, code string) (val []interface{}, typ []xreflect.Type, err error) { // Capture a panic from the evaluation if one occurs and store it in the `err` return parameter. defer func() { if r := recover(); r != nil { var ok bool if err, ok = r.(error); !ok { err = errors.New(fmt.Sprint(r)) } } }() code = evalSpecialCommands(ir, outerr, code) // Prepare and perform the multiline evaluation. compiler := ir.Comp // Don't show the gomacro prompt. compiler.Options &^= base.OptShowPrompt // Don't swallow panics as they are recovered above and handled with a Jupyter `error` message instead. compiler.Options &^= base.OptTrapPanic // Reset the error line so that error messages correspond to the lines from the cell. compiler.Line = 0 // Parse the input code (and don't perform gomacro's macroexpansion). // These may panic but this will be recovered by the deferred recover() above so that the error // may be returned instead. nodes := compiler.ParseBytes([]byte(code)) srcAst := ast2.AnyToAst(nodes, "doEval") // If there is no srcAst then we must be evaluating nothing. The result must be nil then. if srcAst == nil { return nil, nil, nil } // Compile the ast. compiledSrc := ir.CompileAst(srcAst) // Evaluate the code. results, types := ir.RunExpr(compiledSrc) // Convert results from xreflect.Value to interface{} values := make([]interface{}, len(results)) for i, result := range results { values[i] = basereflect.ValueInterface(result) } return values, types, nil } // handleShutdownRequest sends a "shutdown" message. func handleShutdownRequest(receipt msgReceipt) { content := receipt.Msg.Content.(map[string]interface{}) restart := content["restart"].(bool) reply := shutdownReply{ Restart: restart, } if err := receipt.Reply("shutdown_reply", reply); err != nil { log.Fatal(err) } log.Println("Shutting down in response to shutdown_request") os.Exit(0) } // startHeartbeat starts a go-routine for handling heartbeat ping messages sent over the given `hbSocket`. The `wg`'s // `Done` method is invoked after the thread is completely shutdown. To request a shutdown the returned `shutdown` channel // can be closed. func startHeartbeat(hbSocket Socket, wg *sync.WaitGroup) (shutdown chan struct{}) { quit := make(chan struct{}) // Start the handler that will echo any received messages back to the sender. wg.Add(1) go func() { defer wg.Done() type msgType struct { Msg zmq4.Msg Err error } msgs := make(chan msgType) go func() { defer close(msgs) for { msg, err := hbSocket.Socket.Recv() select { case msgs <- msgType{msg, err}: case <-quit: return } } }() timeout := time.NewTimer(500 * time.Second) defer timeout.Stop() for { timeout.Reset(500 * time.Second) select { case <-quit: return case <-timeout.C: continue case v := <-msgs: hbSocket.RunWithSocket(func(echo zmq4.Socket) error { if v.Err != nil { log.Fatalf("Error reading heartbeat ping bytes: %v\n", v.Err) return v.Err } // Send the received byte string back to let the front-end know that the kernel is alive. if err := echo.Send(v.Msg); err != nil { log.Printf("Error sending heartbeat pong bytes: %b\n", err) return err } return nil }) } } }() return quit } // find and execute special commands in code, remove them from returned string func evalSpecialCommands(ir *interp.Interp, outerr OutErr, code string) string { lines := strings.Split(code, "\n") stop := false for i, line := range lines { line = strings.TrimSpace(line) if len(line) != 0 { switch line[0] { case '%': evalSpecialCommand(ir, outerr, line) lines[i] = "" case '$': evalShellCommand(ir, outerr, line) lines[i] = "" default: // if a line is NOT a special command, // stop processing special commands stop = true } } if stop { break } } return strings.Join(lines, "\n") } // execute special command. line must start with '%' func evalSpecialCommand(ir *interp.Interp, outerr OutErr, line string) { const help string = ` available special commands (%): %cd [path] %go111module {on|off} %help execute shell commands ($): $command [args...] example: $ls -l ` args := strings.SplitN(line, " ", 2) cmd := args[0] arg := "" if len(args) > 1 { arg = args[1] } switch cmd { case "%cd": if arg == "" { home, err := os.UserHomeDir() if err != nil { panic(fmt.Errorf("error getting user home directory: %v", err)) } arg = home } err := os.Chdir(arg) if err != nil { panic(fmt.Errorf("error setting current directory to %q: %v", arg, err)) } case "%go111module": if arg == "on" { ir.Comp.CompGlobals.Options |= base.OptModuleImport } else if arg == "off" { ir.Comp.CompGlobals.Options &^= base.OptModuleImport } else { panic(fmt.Errorf("special command %s: expecting a single argument 'on' or 'off', found: %q", cmd, arg)) } case "%help": outerr.out.Write([]byte(help)) default: panic(fmt.Errorf("unknown special command: %q\n%s", line, help)) } } // execute shell command. line must start with '$' func evalShellCommand(ir *interp.Interp, outerr OutErr, line string) { args := strings.Fields(line[1:]) if len(args) <= 0 { return } var writersWG sync.WaitGroup writersWG.Add(2) cmd := exec.Command(args[0], args[1:]...) stdout, err := cmd.StdoutPipe() if err != nil { panic(fmt.Errorf("Command.StdoutPipe() failed: %v", err)) } stderr, err := cmd.StderrPipe() if err != nil { panic(fmt.Errorf("Command.StderrPipe() failed: %v", err)) } go func() { defer writersWG.Done() io.Copy(outerr.out, stdout) }() go func() { defer writersWG.Done() io.Copy(outerr.err, stderr) }() err = cmd.Start() if err != nil { panic(fmt.Errorf("error starting command '%s': %v", line[1:], err)) } err = cmd.Wait() if err != nil { panic(fmt.Errorf("error waiting for command '%s': %v", line[1:], err)) } writersWG.Wait() } gophernotes/kernel/000077500000000000000000000000001452123260300146715ustar00rootroot00000000000000gophernotes/kernel/kernel.json000066400000000000000000000002151452123260300170420ustar00rootroot00000000000000{ "argv": [ "gophernotes", "{connection_file}" ], "display_name": "Go", "language": "go", "name": "go" } gophernotes/kernel/kernel.json.in000066400000000000000000000002151452123260300174470ustar00rootroot00000000000000{ "argv": [ "gophernotes", "{connection_file}" ], "display_name": "Go", "language": "go", "name": "go" } gophernotes/kernel/logo-32x32.png000066400000000000000000000043211452123260300171160ustar00rootroot00000000000000PNG  IHDR szzgAMA a cHRMz&u0`:pQ< pHYs  YiTXtXML:com.adobe.xmp 1 L'YIDATX WYlEgg|Ďmꐄ҃i BP+Bxp^P*B PS< )PJSzp UWRz:ݝvR`}_3f%b/nZ8A$J1É"ZXNh!h㇅◪j lPܥ77qͲk}Eu.iq]DGzwx_QN^f/Жm|2Qq6s>.'&3i ǡ#>\hAb_>"%v3g-3ugS%ݰ?!442/j^bOyꅙTokW2JCYb"idYaξaTgDze퍳xPψyQ}r3H3f2zgrѮBYtoܹ:$шX\B4jd0'e. ktٷ;v4_Ҡr3[ߵDLuHloDco;OIxG[e`L.ɤPIduKoKz*H (p{u?a =j({^h{Ͱn CN arϜ`gʊUa V[[il%Э {u|ϚqG^d +Hbm7/WuLJ H6a99ٔ-ĬibK#Zsf x_r`+5МKjꌅ}jn]+$bFr2'Dit3쌧qk4΂![͉ fe5K.-sVs FqyʅH9k ٟ?;eGD0~ Եl&0U3DD 0gN,pSRە7cYÀ,pzXJ (4]7Ճi =gFWɓ]_:C4/מY{qw(Np@ߧVeL-7~Z>9:W5ΆL Y)=!---VRӺeM*)];SNբl-?FĜ/`5:LR}DjM> PK"N[!f+j&Ӗ/N o7,ja 5Y9<. 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"}", }, []string{"0\n", "1\n", "2\n"}}, } t.Logf("Should produce stdout stream messages when writing to stdout") cases: for k, tc := range cases { // Give a progress report. t.Logf(" Evaluating code snippet %d/%d.", k+1, len(cases)) // Get the result. stdout, _ := testOutputStream(t, strings.Join(tc.Input, "\n")) // Compare the result. if len(stdout) != len(tc.Output) { t.Errorf("\t%s Test case expected %d message(s) on stdout but got %d.", failure, len(tc.Output), len(stdout)) continue } for i, expected := range tc.Output { if stdout[i] != expected { t.Errorf("\t%s Test case returned unexpected messages on stdout.", failure) continue cases } } t.Logf("\t%s Returned the expected messages on stdout.", success) } } // TestPrintStderr tests that data written to stderr publishes the same data in a "stderr" "stream" message. func TestPrintStderr(t *testing.T) { cases := []struct { Input []string Output []string }{ {[]string{ `import "fmt"`, `import "os"`, "a := 1", "fmt.Fprintln(os.Stderr, a)", }, []string{"1\n"}}, {[]string{ `os.Stderr.WriteString("2")`, }, []string{"2"}}, {[]string{ `import "time"`, "for i := 0; i < 3; i++ {", " fmt.Fprintln(os.Stderr, i)", " time.Sleep(500 * time.Millisecond)", // Stall to prevent prints from buffering into single message. "}", }, []string{"0\n", "1\n", "2\n"}}, } t.Logf("Should produce stderr stream messages when writing to stderr") cases: for k, tc := range cases { // Give a progress report. t.Logf(" Evaluating code snippet %d/%d.", k+1, len(cases)) // Get the result. _, stderr := testOutputStream(t, strings.Join(tc.Input, "\n")) // Compare the result. if len(stderr) != len(tc.Output) { t.Errorf("\t%s Test case expected %d message(s) on stderr but got %d.", failure, len(tc.Output), len(stderr)) continue } for i, expected := range tc.Output { if stderr[i] != expected { t.Errorf("\t%s Test case returned unexpected messages on stderr.", failure) continue cases } } t.Logf("\t%s Returned the expected messages on stderr.", success) } } //============================================================================== // testJupyterClient holds references to the 2 sockets it uses to communicate with the kernel. type testJupyterClient struct { shellSocket zmq4.Socket ioSocket zmq4.Socket } // newTestJupyterClient creates and connects a fresh client to the kernel. Upon error, newTestJupyterClient // will Fail the test. func newTestJupyterClient(t *testing.T) (testJupyterClient, func()) { t.Helper() var ( err error ctx = context.Background() addrShell = fmt.Sprintf("%s://%s:%d", transport, ip, shellPort) addrIO = fmt.Sprintf("%s://%s:%d", transport, ip, iopubPort) ) // Prepare the shell socket. shell := zmq4.NewReq(ctx) if err = shell.Dial(addrShell); err != nil { t.Fatalf("\t%s shell.Connect: %s", failure, err) } // Prepare the IOPub socket. iopub := zmq4.NewSub(ctx) if err = iopub.Dial(addrIO); err != nil { t.Fatalf("\t%s iopub.Connect: %s", failure, err) } if err = iopub.SetOption(zmq4.OptionSubscribe, ""); err != nil { t.Fatalf("\t%s iopub.SetSubscribe: %s", failure, err) } // Wait for a second to give the tcp connection time to complete to avoid missing the early pub messages. time.Sleep(1 * time.Second) return testJupyterClient{shell, iopub}, func() { if err := shell.Close(); err != nil { t.Errorf("\t%s shell.Close: %s", failure, err) } if err = iopub.Close(); err != nil { t.Errorf("\t%s iopub.Close: %s", failure, err) } } } // sendShellRequest sends a message to the kernel over the shell channel. Upon error, sendShellRequest // will Fail the test. func (client *testJupyterClient) sendShellRequest(t *testing.T, request ComposedMsg) { t.Helper() var ( frames [][]byte err error ) frames = append(frames, []byte("")) reqMsgParts, err := request.ToWireMsg([]byte(connectionKey)) if err != nil { t.Fatalf("\t%s request.ToWireMsg: %s", failure, err) } frames = append(frames, reqMsgParts...) if err = client.shellSocket.SendMulti(zmq4.NewMsgFrom(frames...)); err != nil { t.Fatalf("\t%s shellSocket.SendMessage: %s", failure, err) } } // recvShellReply tries to read a reply message from the shell channel. It will timeout after the given // timeout delay. Upon error or timeout, recvShellReply will Fail the test. func (client *testJupyterClient) recvShellReply(t *testing.T, timeout time.Duration) ComposedMsg { t.Helper() ch := make(chan ComposedMsg) go func() { repMsgParts, err := client.shellSocket.Recv() if err != nil { t.Fatalf("\t%s Shell socket RecvMessageBytes: %s", failure, err) } msgParsed, _, err := WireMsgToComposedMsg(repMsgParts.Frames, []byte(connectionKey)) if err != nil { t.Fatalf("\t%s Could not parse wire message: %s", failure, err) } ch <- msgParsed }() var reply ComposedMsg select { case reply = <-ch: return reply case <-time.After(timeout): t.Fatalf("\t%s recvShellReply timed out", failure) } return reply } // recvIOSub tries to read a published message from the IOPub channel. It will timeout after the given // timeout delay. Upon error or timeout, recvIOSub will Fail the test. func (client *testJupyterClient) recvIOSub(t *testing.T, timeout time.Duration) ComposedMsg { t.Helper() ch := make(chan ComposedMsg) go func() { repMsgParts, err := client.ioSocket.Recv() if err != nil { t.Fatalf("\t%s IOPub socket RecvMessageBytes: %s", failure, err) } msgParsed, _, err := WireMsgToComposedMsg(repMsgParts.Frames, []byte(connectionKey)) if err != nil { t.Fatalf("\t%s Could not parse wire message: %s", failure, err) } ch <- msgParsed }() var sub ComposedMsg select { case sub = <-ch: case <-time.After(timeout): t.Fatalf("\t%s recvIOSub timed out", failure) } return sub } // performJupyterRequest preforms a request and awaits a reply on the shell channel. Additionally all messages on the // IOPub channel between the opening 'busy' messages and closing 'idle' message are captured and returned. The request // will timeout after the given timeout delay. Upon error or timeout, request will Fail the test. func (client *testJupyterClient) performJupyterRequest(t *testing.T, request ComposedMsg, timeout time.Duration) (ComposedMsg, []ComposedMsg) { t.Helper() client.sendShellRequest(t, request) reply := client.recvShellReply(t, timeout) // Read the expected 'busy' message and ensure it is in fact, a 'busy' message. subMsg := client.recvIOSub(t, 1*time.Second) assertMsgTypeEquals(t, subMsg, "status") subData := getMsgContentAsJSONObject(t, subMsg) execState := getString(t, "content", subData, "execution_state") if execState != kernelBusy { t.Fatalf("\t%s Expected a 'busy' status message but got '%s'", failure, execState) } var pub []ComposedMsg // Read messages from the IOPub channel until an 'idle' message is received. for { subMsg = client.recvIOSub(t, 100*time.Millisecond) // If the message is a 'status' message, ensure it is an 'idle' status. if subMsg.Header.MsgType == "status" { subData = getMsgContentAsJSONObject(t, subMsg) execState = getString(t, "content", subData, "execution_state") if execState != kernelIdle { t.Fatalf("\t%s Expected a 'idle' status message but got '%s'", failure, execState) } // Break from the loop as we don't expect any other IOPub messages after the 'idle'. break } // Add the message to the pub collection. pub = append(pub, subMsg) } return reply, pub } // executeCode creates an execute request for the given code and preforms the request. It returns the content of the // reply as well as all of the messages captured from the IOPub channel during the execution. func (client *testJupyterClient) executeCode(t *testing.T, code string) (map[string]interface{}, []ComposedMsg) { t.Helper() // Create a message. request, err := NewMsg("execute_request", ComposedMsg{}) if err != nil { t.Fatalf("\t%s NewMsg: %s", failure, err) } // Fill in remaining header information. request.Header.Session = sessionID request.Header.Username = "KernelTester" // Fill in Metadata. request.Metadata = make(map[string]interface{}) // Fill in content. content := make(map[string]interface{}) content["code"] = code content["silent"] = false request.Content = content // Make the request. reply, pub := client.performJupyterRequest(t, request, 10*time.Second) // Ensure the reply is an execute_reply and extract the content from the reply. assertMsgTypeEquals(t, reply, "execute_reply") content = getMsgContentAsJSONObject(t, reply) return content, pub } // assertMsgTypeEquals is a test helper that fails the test if the message header's MsgType is not the // expectedType. func assertMsgTypeEquals(t *testing.T, msg ComposedMsg, expectedType string) { t.Helper() if msg.Header.MsgType != expectedType { t.Fatalf("\t%s Expected message of type '%s' but was '%s'", failure, expectedType, msg.Header.MsgType) } } // getMsgContentAsJSONObject is a test helper that fails the rest if the message content is not a // map[string]interface{} and returns the content as a map[string]interface{} if it is of the correct type. func getMsgContentAsJSONObject(t *testing.T, msg ComposedMsg) map[string]interface{} { t.Helper() content, ok := msg.Content.(map[string]interface{}) if !ok { t.Fatalf("\t%s Message content is not a JSON object", failure) } return content } // getString is a test helper that retrieves a value as a string from the content at the given key. If the key // does not exist in the content map or the value is not a string this will fail the test. The jsonObjectName // parameter is a string used to name the content for more helpful fail messages. func getString(t *testing.T, jsonObjectName string, content map[string]interface{}, key string) string { t.Helper() raw, ok := content[key] if !ok { t.Fatalf("\t%s %s[\"%s\"] field not present", failure, jsonObjectName, key) } value, ok := raw.(string) if !ok { t.Fatalf("\t%s %s[\"%s\"] is not a string", failure, jsonObjectName, key) } return value } // getJSONObject is a test helper that retrieves a value as a map[string]interface{} from the content at the given key. // If the key does not exist in the content map or the value is not a map[string]interface{} this will fail the test. // The jsonObjectName parameter is a string used to name the content for more helpful fail messages. func getJSONObject(t *testing.T, jsonObjectName string, content map[string]interface{}, key string) map[string]interface{} { t.Helper() raw, ok := content[key] if !ok { t.Fatalf("\t%s %s[\"%s\"] field not present", failure, jsonObjectName, key) } value, ok := raw.(map[string]interface{}) if !ok { t.Fatalf("\t%s %s[\"%s\"] is not a JSON object", failure, jsonObjectName, key) } return value } // testOutputStream is a test helper that collects "stream" messages upon executing the codeIn. func testOutputStream(t *testing.T, codeIn string) ([]string, []string) { t.Helper() client, closeClient := newTestJupyterClient(t) defer closeClient() _, pub := client.executeCode(t, codeIn) var stdout, stderr []string for _, pubMsg := range pub { if pubMsg.Header.MsgType == "stream" { content := getMsgContentAsJSONObject(t, pubMsg) streamType := getString(t, "content", content, "name") streamData := getString(t, "content", content, "text") switch streamType { case StreamStdout: stdout = append(stdout, streamData) case StreamStderr: stderr = append(stderr, streamData) default: t.Fatalf("Unknown stream type '%s'", streamType) } } } return stdout, stderr } gophernotes/main.go000066400000000000000000000006511452123260300146660ustar00rootroot00000000000000package main import ( "flag" "log" ) const ( // Version defines the gophernotes version. Version string = "1.0.0" // ProtocolVersion defines the Jupyter protocol version. ProtocolVersion string = "5.0" ) func main() { // Parse the connection file. flag.Parse() if flag.NArg() < 1 { log.Fatalln("Need a command line argument specifying the connection file.") } // Run the kernel. runKernel(flag.Arg(0)) } gophernotes/messages.go000066400000000000000000000217711452123260300155570ustar00rootroot00000000000000package main import ( "crypto/hmac" "crypto/sha256" "encoding/hex" "encoding/json" "io" "time" "github.com/go-zeromq/zmq4" "github.com/gofrs/uuid" ) // MsgHeader encodes header info for ZMQ messages. type MsgHeader struct { MsgID string `json:"msg_id"` Username string `json:"username"` Session string `json:"session"` MsgType string `json:"msg_type"` ProtocolVersion string `json:"version"` Timestamp string `json:"date"` } // ComposedMsg represents an entire message in a high-level structure. type ComposedMsg struct { Header MsgHeader ParentHeader MsgHeader Metadata map[string]interface{} Content interface{} } // msgReceipt represents a received message, its return identities, and // the sockets for communication. type msgReceipt struct { Msg ComposedMsg Identities [][]byte Sockets SocketGroup } // MIMEMap holds data that can be presented in multiple formats. The keys are MIME types // and the values are the data formatted with respect to its MIME type. // All maps should contain at least a "text/plain" representation with a string value. type MIMEMap = map[string]interface{} // Data is the exact structure returned to Jupyter. // It allows to fully specify how a value should be displayed. type Data = struct { Data MIMEMap Metadata MIMEMap Transient MIMEMap } // InvalidSignatureError is returned when the signature on a received message does not // validate. type InvalidSignatureError struct{} func (e *InvalidSignatureError) Error() string { return "A message had an invalid signature" } // WireMsgToComposedMsg translates a multipart ZMQ messages received from a socket into // a ComposedMsg struct and a slice of return identities. This includes verifying the // message signature. func WireMsgToComposedMsg(msgparts [][]byte, signkey []byte) (ComposedMsg, [][]byte, error) { i := 0 for string(msgparts[i]) != "" { i++ } identities := msgparts[:i] // Validate signature. var msg ComposedMsg if len(signkey) != 0 { mac := hmac.New(sha256.New, signkey) for _, msgpart := range msgparts[i+2 : i+6] { mac.Write(msgpart) } signature := make([]byte, hex.DecodedLen(len(msgparts[i+1]))) hex.Decode(signature, msgparts[i+1]) if !hmac.Equal(mac.Sum(nil), signature) { return msg, nil, &InvalidSignatureError{} } } // Unmarshal contents. json.Unmarshal(msgparts[i+2], &msg.Header) json.Unmarshal(msgparts[i+3], &msg.ParentHeader) json.Unmarshal(msgparts[i+4], &msg.Metadata) json.Unmarshal(msgparts[i+5], &msg.Content) return msg, identities, nil } // ToWireMsg translates a ComposedMsg into a multipart ZMQ message ready to send, and // signs it. This does not add the return identities or the delimiter. func (msg ComposedMsg) ToWireMsg(signkey []byte) ([][]byte, error) { msgparts := make([][]byte, 5) header, err := json.Marshal(msg.Header) if err != nil { return msgparts, err } msgparts[1] = header parentHeader, err := json.Marshal(msg.ParentHeader) if err != nil { return msgparts, err } msgparts[2] = parentHeader if msg.Metadata == nil { msg.Metadata = make(map[string]interface{}) } metadata, err := json.Marshal(msg.Metadata) if err != nil { return msgparts, err } msgparts[3] = metadata content, err := json.Marshal(msg.Content) if err != nil { return msgparts, err } msgparts[4] = content // Sign the message. if len(signkey) != 0 { mac := hmac.New(sha256.New, signkey) for _, msgpart := range msgparts[1:] { mac.Write(msgpart) } msgparts[0] = make([]byte, hex.EncodedLen(mac.Size())) hex.Encode(msgparts[0], mac.Sum(nil)) } return msgparts, nil } // SendResponse sends a message back to return identities of the received message. func (receipt *msgReceipt) SendResponse(socket zmq4.Socket, msg ComposedMsg) error { msgParts, err := msg.ToWireMsg(receipt.Sockets.Key) if err != nil { return err } var frames = make([][]byte, 0, len(receipt.Identities)+1+len(msgParts)) frames = append(frames, receipt.Identities...) frames = append(frames, []byte("")) frames = append(frames, msgParts...) err = socket.SendMulti(zmq4.NewMsgFrom(frames...)) if err != nil { return err } return nil } // NewMsg creates a new ComposedMsg to respond to a parent message. // This includes setting up its headers. func NewMsg(msgType string, parent ComposedMsg) (ComposedMsg, error) { var msg ComposedMsg msg.ParentHeader = parent.Header msg.Header.Session = parent.Header.Session msg.Header.Username = parent.Header.Username msg.Header.MsgType = msgType msg.Header.ProtocolVersion = ProtocolVersion msg.Header.Timestamp = time.Now().UTC().Format(time.RFC3339) u, err := uuid.NewV4() if err != nil { return msg, err } msg.Header.MsgID = u.String() return msg, nil } // Publish creates a new ComposedMsg and sends it back to the return identities over the // IOPub channel. func (receipt *msgReceipt) Publish(msgType string, content interface{}) error { msg, err := NewMsg(msgType, receipt.Msg) if err != nil { return err } msg.Content = content return receipt.Sockets.IOPubSocket.RunWithSocket(func(iopub zmq4.Socket) error { return receipt.SendResponse(iopub, msg) }) } // Reply creates a new ComposedMsg and sends it back to the return identities over the // Shell channel. func (receipt *msgReceipt) Reply(msgType string, content interface{}) error { msg, err := NewMsg(msgType, receipt.Msg) if err != nil { return err } msg.Content = content return receipt.Sockets.ShellSocket.RunWithSocket(func(shell zmq4.Socket) error { return receipt.SendResponse(shell, msg) }) } // PublishKernelStatus publishes a status message notifying front-ends of the state the kernel is in. Supports // states "starting", "busy", and "idle". func (receipt *msgReceipt) PublishKernelStatus(status string) error { return receipt.Publish("status", struct { ExecutionState string `json:"execution_state"` }{ ExecutionState: status, }, ) } // PublishExecutionInput publishes a status message notifying front-ends of what code is // currently being executed. func (receipt *msgReceipt) PublishExecutionInput(execCount int, code string) error { return receipt.Publish("execute_input", struct { ExecCount int `json:"execution_count"` Code string `json:"code"` }{ ExecCount: execCount, Code: code, }, ) } func ensure(bundle MIMEMap) MIMEMap { if bundle == nil { bundle = make(MIMEMap) } return bundle } func merge(a MIMEMap, b MIMEMap) MIMEMap { if len(b) == 0 { return a } if a == nil { a = make(MIMEMap) } for k, v := range b { a[k] = v } return a } // PublishExecuteResult publishes the result of the `execCount` execution as a string. func (receipt *msgReceipt) PublishExecutionResult(execCount int, data Data) error { return receipt.Publish("execute_result", struct { ExecCount int `json:"execution_count"` Data MIMEMap `json:"data"` Metadata MIMEMap `json:"metadata"` }{ ExecCount: execCount, Data: data.Data, Metadata: ensure(data.Metadata), }) } // PublishExecuteResult publishes a serialized error that was encountered during execution. func (receipt *msgReceipt) PublishExecutionError(err string, trace []string) error { return receipt.Publish("error", struct { Name string `json:"ename"` Value string `json:"evalue"` Trace []string `json:"traceback"` }{ Name: "ERROR", Value: err, Trace: trace, }, ) } // PublishDisplayData publishes a single image. func (receipt *msgReceipt) PublishDisplayData(data Data) error { // copy Data in a struct with appropriate json tags return receipt.Publish("display_data", struct { Data MIMEMap `json:"data"` Metadata MIMEMap `json:"metadata"` Transient MIMEMap `json:"transient"` }{ Data: data.Data, Metadata: ensure(data.Metadata), Transient: ensure(data.Transient), }) } const ( // StreamStdout defines the stream name for standard out on the front-end. It // is used in `PublishWriteStream` to specify the stream to write to. StreamStdout = "stdout" // StreamStderr defines the stream name for standard error on the front-end. It // is used in `PublishWriteStream` to specify the stream to write to. StreamStderr = "stderr" ) // PublishWriteStream prints the data string to a stream on the front-end. This is // either `StreamStdout` or `StreamStderr`. func (receipt *msgReceipt) PublishWriteStream(stream string, data string) error { return receipt.Publish("stream", struct { Stream string `json:"name"` Data string `json:"text"` }{ Stream: stream, Data: data, }, ) } // JupyterStreamWriter is an `io.Writer` implementation that writes the data to the notebook // front-end. type JupyterStreamWriter struct { stream string receipt *msgReceipt } // Write implements `io.Writer.Write` by publishing the data via `PublishWriteStream` func (writer *JupyterStreamWriter) Write(p []byte) (int, error) { data := string(p) n := len(p) if err := writer.receipt.PublishWriteStream(writer.stream, data); err != nil { return 0, err } return n, nil } type OutErr struct { out io.Writer err io.Writer } gophernotes/paper/000077500000000000000000000000001452123260300145205ustar00rootroot00000000000000gophernotes/paper/paper.bib000066400000000000000000000030721452123260300163070ustar00rootroot00000000000000@Software{Gophernotes, title = "Gophernotes", booktitle = "Gophernotes", author = "Whitenack, Daniel L.", howpublished = "\url{https://github.com/gopherdata/gophernotes}", note = "Accessed: 2017-12-13" } @BOOK{Jupyter_Notebooks, title = "Jupyter Notebooks – a publishing format for reproducible computational workflows", author = "Thomas Kluyver and Benjamin Ragan-Kelley and Fernando Pérez and Brian Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Damián Avila and Safia Abdalla and Carol Willing and Jupyter Development Team", publisher = "IOS Press Ebooks", series = "Positioning and Power in Academic Publishing: Players, Agents and Agendas", year = 2016 } @Software{Nteract, title = "nteract", booktitle = "nteract", author = "nteract", howpublished = "\url{https://github.com/nteract/nteract}", note = "Accessed: 2017-12-13" } @Software{Go, title = "Go", booktitle = "Go", author = "golang.org", howpublished = "\url{https://golang.org/}", note = "Accessed: 2017-12-13" } @Software{Gomacro, title = "gomacro", booktitle = "gomacro", author = "Ghilardi, Massimiliano", howpublished = "\url{https://github.com/cosmos72/gomacro}", note = "Accessed: 2017-12-13" } @Software{ZeroMQ, title = "ZeroMQ", booktitle = "ZeroMQ", howpublished = "\url{http://zeromq.org/}", note = "Accessed: 2017-12-13" } gophernotes/paper/paper.md000066400000000000000000000044231452123260300161540ustar00rootroot00000000000000--- title: 'Gophernotes: the Go kernel for Jupyter and nteract' tags: - go - golang - jupyter - nteract - data science authors: - name: Daniel L Whitenack orcid: 0000-0003-1155-8889 affiliation: "1" # (Multiple affiliations must be quoted) affiliations: - name: Pachyderm index: 1 date: 13 December 2017 bibliography: paper.bib --- # Summary Gophernotes [@Gophernotes] is a Go [@Go] kernel for Jupyter notebooks [@Jupyter_Notebooks] and nteract [@Nteract]. It lets you use Go interactively in a browser-based notebook or desktop application. Use gophernotes to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the Jupyter Notebook Viewer. ## Functionality To enable the use of Go in notebooks, one needs to build the gophernotes binary and ensure that this is available in the user's local PATH. A JSON configuration file is then placed in the user's local Jupyter configuration directory. This configuration file references the gophernotes binary and names the kernel `Go`. Assuming the above steps have been completed, the `Go` kernel should be recognized by Jupyter or nteract when a Jupyter server or the nteract destop application, respectively, is launched. When a new `Go` notebook is generated in one of those systems, Jupyter or nteract will communicate with gophernotes via ZeroMQ [@ZeroMQ]. In particular, these systems will pass cells of code to the gophernotes kernel. Gophernotes will evaluate these and then pass the result(s) back, which is then displayed on the frontend. ## Ongoing Research and Use Gophernotes is currently being used by the author to teach concurrency, streaming data analysis, and machine learning techniques with O'Reilly media. Gophernotes has also been made available to students and researchers at Purdue University through JupyterHub. # Acknowledgements This project utilizes a Go interpreter called gomacro [@Gomacro] under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. The author thanks Spencer Park for all his very helpful input and contributions. # References gophernotes/x_package.go000066400000000000000000000157131452123260300156710ustar00rootroot00000000000000package main import ( "image" "image/color" r "reflect" "github.com/cosmos72/gomacro/imports" ) // prepare imports.Package for interpreted code var display = imports.Package{ Binds: map[string]r.Value{ "Any": r.ValueOf(Any), "Auto": r.ValueOf(Auto), "File": r.ValueOf(File), "HTML": r.ValueOf(HTML), "Image": r.ValueOf(Image), "JPEG": r.ValueOf(JPEG), "JSON": r.ValueOf(JSON), "JavaScript": r.ValueOf(JavaScript), "Latex": r.ValueOf(Latex), "MakeData": r.ValueOf(MakeData), "MakeData3": r.ValueOf(MakeData3), "Markdown": r.ValueOf(Markdown), "Math": r.ValueOf(Math), "MIME": r.ValueOf(MIME), "MIMETypeHTML": r.ValueOf(MIMETypeHTML), "MIMETypeJavaScript": r.ValueOf(MIMETypeJavaScript), "MIMETypeJPEG": r.ValueOf(MIMETypeJPEG), "MIMETypeJSON": r.ValueOf(MIMETypeJSON), "MIMETypeLatex": r.ValueOf(MIMETypeLatex), "MIMETypeMarkdown": r.ValueOf(MIMETypeMarkdown), "MIMETypePDF": r.ValueOf(MIMETypePDF), "MIMETypePNG": r.ValueOf(MIMETypePNG), "MIMETypeSVG": r.ValueOf(MIMETypeSVG), "PDF": r.ValueOf(PDF), "PNG": r.ValueOf(PNG), "SVG": r.ValueOf(SVG), }, Types: map[string]r.Type{ "Data": r.TypeOf((*Data)(nil)).Elem(), "HTMLer": r.TypeOf((*HTMLer)(nil)).Elem(), "JavaScripter": r.TypeOf((*JavaScripter)(nil)).Elem(), "Image": r.TypeOf((*image.Image)(nil)).Elem(), "JPEGer": r.TypeOf((*JPEGer)(nil)).Elem(), "JSONer": r.TypeOf((*JSONer)(nil)).Elem(), "Latexer": r.TypeOf((*Latexer)(nil)).Elem(), "Markdowner": r.TypeOf((*Markdowner)(nil)).Elem(), "MIMEMap": r.TypeOf((*MIMEMap)(nil)).Elem(), "PNGer": r.TypeOf((*PNGer)(nil)).Elem(), "PDFer": r.TypeOf((*PDFer)(nil)).Elem(), "Renderer": r.TypeOf((*Renderer)(nil)).Elem(), "SimpleRenderer": r.TypeOf((*SimpleRenderer)(nil)).Elem(), "SVGer": r.TypeOf((*SVGer)(nil)).Elem(), }, Proxies: map[string]r.Type{ // these are needed to allow interpreted types // to implement the corresponding interfaces "HTMLer": r.TypeOf((*proxy_HTMLer)(nil)).Elem(), "Image": r.TypeOf((*proxy_image_Image)(nil)).Elem(), "JPEGer": r.TypeOf((*proxy_JPEGer)(nil)).Elem(), "JSONer": r.TypeOf((*proxy_JSONer)(nil)).Elem(), "Latexer": r.TypeOf((*proxy_Latexer)(nil)).Elem(), "Markdowner": r.TypeOf((*proxy_Markdowner)(nil)).Elem(), "PNGer": r.TypeOf((*proxy_PNGer)(nil)).Elem(), "PDFer": r.TypeOf((*proxy_PDFer)(nil)).Elem(), "Renderer": r.TypeOf((*proxy_Renderer)(nil)).Elem(), "SimpleRenderer": r.TypeOf((*proxy_SimpleRenderer)(nil)).Elem(), "SVGer": r.TypeOf((*proxy_SVGer)(nil)).Elem(), }, } // --------------- proxy for display.HTMLer --------------- type proxy_HTMLer struct { Object interface{} HTML_ func(interface{}) string } func (P *proxy_HTMLer) HTML() string { return P.HTML_(P.Object) } // compile-time check that *proxy_HTMLer implements HTMLer var _ HTMLer = (*proxy_HTMLer)(nil) // --------------- proxy for display.JPEGer --------------- type proxy_JPEGer struct { Object interface{} JPEG_ func(interface{}) []byte } func (P *proxy_JPEGer) JPEG() []byte { return P.JPEG_(P.Object) } // compile-time check that *proxy_JPEGer implements JPEGer var _ JPEGer = (*proxy_JPEGer)(nil) // --------------- proxy for display.JSONer --------------- type proxy_JSONer struct { Object interface{} JSON_ func(interface{}) map[string]interface{} } func (P *proxy_JSONer) JSON() map[string]interface{} { return P.JSON_(P.Object) } // compile-time check that *proxy_JSONer implements JSONer var _ JSONer = (*proxy_JSONer)(nil) // --------------- proxy for display.Latexer --------------- type proxy_Latexer struct { Object interface{} Latex_ func(interface{}) string } func (P *proxy_Latexer) Latex() string { return P.Latex_(P.Object) } // compile-time check that *proxy_Latexer implements Latexer var _ Latexer = (*proxy_Latexer)(nil) // --------------- proxy for display.Markdowner --------------- type proxy_Markdowner struct { Object interface{} Markdown_ func(interface{}) string } func (P *proxy_Markdowner) Markdown() string { return P.Markdown_(P.Object) } // compile-time check that *proxy_Markdowner implements Markdowner var _ Markdowner = (*proxy_Markdowner)(nil) // --------------- proxy for display.PNGer --------------- type proxy_PNGer struct { Object interface{} PNG_ func(interface{}) []byte } func (P *proxy_PNGer) PNG() []byte { return P.PNG_(P.Object) } // compile-time check that *proxy_PNGer implements PNGer var _ PNGer = (*proxy_PNGer)(nil) // --------------- proxy for display.PDFer --------------- type proxy_PDFer struct { Object interface{} PDF_ func(interface{}) []byte } func (P *proxy_PDFer) PDF() []byte { return P.PDF_(P.Object) } // compile-time check that *proxy_PNGer implements PNGer var _ PNGer = (*proxy_PNGer)(nil) // --------------- proxy for display.Renderer --------------- type proxy_Renderer struct { Object interface{} Render_ func(interface{}) Data } func (P *proxy_Renderer) Render() Data { return P.Render_(P.Object) } // compile-time check that *proxy_Renderer implements Renderer var _ Renderer = (*proxy_Renderer)(nil) // --------------- proxy for display.SimpleRenderer --------------- type proxy_SimpleRenderer struct { Object interface{} SimpleRender_ func(interface{}) MIMEMap } func (P *proxy_SimpleRenderer) SimpleRender() MIMEMap { return P.SimpleRender_(P.Object) } // compile-time check that *proxy_SimpleRenderer implements SimpleRenderer var _ SimpleRenderer = (*proxy_SimpleRenderer)(nil) // --------------- proxy for display.SVGer --------------- type proxy_SVGer struct { Object interface{} SVG_ func(interface{}) string } func (P *proxy_SVGer) SVG() string { return P.SVG_(P.Object) } // compile-time check that *proxy_SVGer implements SVGer var _ SVGer = (*proxy_SVGer)(nil) // --------------- proxy for image.Image --------------- type proxy_image_Image struct { Object interface{} At_ func(_proxy_obj_ interface{}, x int, y int) color.Color Bounds_ func(interface{}) image.Rectangle ColorModel_ func(interface{}) color.Model } func (P *proxy_image_Image) At(x int, y int) color.Color { return P.At_(P.Object, x, y) } func (P *proxy_image_Image) Bounds() image.Rectangle { return P.Bounds_(P.Object) } func (P *proxy_image_Image) ColorModel() color.Model { return P.ColorModel_(P.Object) } // compile-time check that *proxy_image_Image implements image.Image var _ image.Image = (*proxy_image_Image)(nil) // -------------------------------------------------------- // allow importing "display" and "github.com/gopherdata/gophernotes" packages func init() { imports.Packages["display"] = display imports.Packages["github.com/gopherdata/gophernotes"] = display }