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rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/compare_commits_with_basehead.ts
|
/*
* Copyright (c) 2022, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
type RespParams = {
behind_by?: number;
};
export const makeResponse = ({ behind_by = 0 }: RespParams = {}) => ({
data: {
behind_by: behind_by,
},
});
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/context_repo.json
|
{
"owner": "rapidsai",
"repo": "cudf"
}
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/get_by_username.ts
|
/*
* Copyright (c) 2022, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
type RespParams = {
name?: string;
login?: string;
};
const makeResponse = ({ name = "", login = "VibhuJawa" }: RespParams = {}) => ({
data: {
name,
login,
html_url: `https://github.com/${login}`,
},
});
export const user = makeResponse({ name: "Keith Kraus", login: "kkraus14" });
export const userNoName = makeResponse();
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/pulls_get.ts
|
/*
* Copyright (c) 2022, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
type RespParams = {
body?: string;
};
export const makeResponse = ({ body = "" }: RespParams = {}) => ({
data: {
url: "https://api.github.com/repos/rapidsai/cudf/pulls/6775",
id: 521774755,
node_id: "MDExOlB1bGxSZXF1ZXN0NTIxNzc0NzU1",
html_url: "https://github.com/rapidsai/cudf/pull/6775",
diff_url: "https://github.com/rapidsai/cudf/pull/6775.diff",
patch_url: "https://github.com/rapidsai/cudf/pull/6775.patch",
issue_url: "https://api.github.com/repos/rapidsai/cudf/issues/6775",
number: 1234,
state: "closed",
locked: false,
title: "[REVIEW] Implement cudf.DateOffset for months ",
user: {
login: "VibhuJawa",
id: 53796099,
node_id: "MDQ6VXNlcjUzNzk2MDk5",
avatar_url: "https://avatars1.githubusercontent.com/u/53796099?v=4",
gravatar_id: "",
url: "https://api.github.com/users/VibhuJawa",
html_url: "https://github.com/VibhuJawa",
followers_url: "https://api.github.com/users/VibhuJawa/followers",
following_url:
"https://api.github.com/users/VibhuJawa/following{/other_user}",
gists_url: "https://api.github.com/users/VibhuJawa/gists{/gist_id}",
starred_url:
"https://api.github.com/users/VibhuJawa/starred{/owner}{/repo}",
subscriptions_url: "https://api.github.com/users/VibhuJawa/subscriptions",
organizations_url: "https://api.github.com/users/VibhuJawa/orgs",
repos_url: "https://api.github.com/users/VibhuJawa/repos",
events_url: "https://api.github.com/users/VibhuJawa/events{/privacy}",
received_events_url:
"https://api.github.com/users/VibhuJawa/received_events",
type: "User",
site_admin: false,
},
body:
body ||
"Implements `cudf.DateOffset` - an object used for calendrical arithmetic, similar to pandas.DateOffset - for month units only.\n\nCloses https://github.com/rapidsai/cudf/issues/6754",
created_at: "2020-11-16T16:25:22Z",
updated_at: "2020-12-15T23:37:28Z",
closed_at: "2020-12-15T23:37:28Z",
merged_at: "2020-12-15T23:37:28Z",
merge_commit_sha: "1963111b0010e339b0a0d5368c2e96b6d48e6244",
assignee: {
login: "VibhuJawa",
id: 53796099,
node_id: "MDQ6VXNlcjUzNzk2MDk5",
avatar_url: "https://avatars1.githubusercontent.com/u/53796099?v=4",
gravatar_id: "",
url: "https://api.github.com/users/VibhuJawa",
html_url: "https://github.com/VibhuJawa",
followers_url: "https://api.github.com/users/VibhuJawa/followers",
following_url:
"https://api.github.com/users/VibhuJawa/following{/other_user}",
gists_url: "https://api.github.com/users/VibhuJawa/gists{/gist_id}",
starred_url:
"https://api.github.com/users/VibhuJawa/starred{/owner}{/repo}",
subscriptions_url: "https://api.github.com/users/VibhuJawa/subscriptions",
organizations_url: "https://api.github.com/users/VibhuJawa/orgs",
repos_url: "https://api.github.com/users/VibhuJawa/repos",
events_url: "https://api.github.com/users/VibhuJawa/events{/privacy}",
received_events_url:
"https://api.github.com/users/VibhuJawa/received_events",
type: "User",
site_admin: false,
},
assignees: [
{
login: "VibhuJawa",
id: 53796099,
node_id: "MDQ6VXNlcjUzNzk2MDk5",
avatar_url: "https://avatars1.githubusercontent.com/u/53796099?v=4",
gravatar_id: "",
url: "https://api.github.com/users/VibhuJawa",
html_url: "https://github.com/VibhuJawa",
followers_url: "https://api.github.com/users/VibhuJawa/followers",
following_url:
"https://api.github.com/users/VibhuJawa/following{/other_user}",
gists_url: "https://api.github.com/users/VibhuJawa/gists{/gist_id}",
starred_url:
"https://api.github.com/users/VibhuJawa/starred{/owner}{/repo}",
subscriptions_url:
"https://api.github.com/users/VibhuJawa/subscriptions",
organizations_url: "https://api.github.com/users/VibhuJawa/orgs",
repos_url: "https://api.github.com/users/VibhuJawa/repos",
events_url: "https://api.github.com/users/VibhuJawa/events{/privacy}",
received_events_url:
"https://api.github.com/users/VibhuJawa/received_events",
type: "User",
site_admin: false,
},
],
requested_reviewers: [],
requested_teams: [],
labels: [
{
id: 1162013166,
node_id: "MDU6TGFiZWwxMTYyMDEzMTY2",
url: "https://api.github.com/repos/rapidsai/cudf/labels/5%20-%20Ready%20to%20Merge",
name: "5 - Ready to Merge",
color: "a6e54d",
default: false,
description: "Testing and reviews complete, ready to merge",
},
{
id: 1221761547,
node_id: "MDU6TGFiZWwxMjIxNzYxNTQ3",
url: "https://api.github.com/repos/rapidsai/cudf/labels/Cython",
name: "Cython",
color: "f4e85f",
default: false,
description: "",
},
{
id: 1139741213,
node_id: "MDU6TGFiZWwxMTM5NzQxMjEz",
url: "https://api.github.com/repos/rapidsai/cudf/labels/cuDF%20(Python)",
name: "cuDF (Python)",
color: "1d76db",
default: false,
description: "Affects Python cuDF API.",
},
{
id: 599626561,
node_id: "MDU6TGFiZWw1OTk2MjY1NjE=",
url: "https://api.github.com/repos/rapidsai/cudf/labels/feature%20request",
name: "feature request",
color: "a2eeef",
default: false,
description: "New feature or request",
},
{
id: 2546521068,
node_id: "MDU6TGFiZWwyNTQ2NTIxMDY4",
url: "https://api.github.com/repos/rapidsai/cudf/labels/non-breaking",
name: "non-breaking",
color: "0e8a16",
default: false,
description: "Non-breaking change",
},
],
milestone: null,
draft: false,
commits_url:
"https://api.github.com/repos/rapidsai/cudf/pulls/6775/commits",
review_comments_url:
"https://api.github.com/repos/rapidsai/cudf/pulls/6775/comments",
review_comment_url:
"https://api.github.com/repos/rapidsai/cudf/pulls/comments{/number}",
comments_url:
"https://api.github.com/repos/rapidsai/cudf/issues/6775/comments",
statuses_url:
"https://api.github.com/repos/rapidsai/cudf/statuses/3dfbd68fe975eb04ae7b2ffa6369856ae183963d",
head: {
label: "VibhuJawa:fea-month-sub-and-add",
ref: "fea-month-sub-and-add",
sha: "3dfbd68fe975eb04ae7b2ffa6369856ae183963d",
user: {
login: "VibhuJawa",
id: 53796099,
node_id: "MDQ6VXNlcjUzNzk2MDk5",
avatar_url: "https://avatars1.githubusercontent.com/u/53796099?v=4",
gravatar_id: "",
url: "https://api.github.com/users/VibhuJawa",
html_url: "https://github.com/VibhuJawa",
followers_url: "https://api.github.com/users/VibhuJawa/followers",
following_url:
"https://api.github.com/users/VibhuJawa/following{/other_user}",
gists_url: "https://api.github.com/users/VibhuJawa/gists{/gist_id}",
starred_url:
"https://api.github.com/users/VibhuJawa/starred{/owner}{/repo}",
subscriptions_url:
"https://api.github.com/users/VibhuJawa/subscriptions",
organizations_url: "https://api.github.com/users/VibhuJawa/orgs",
repos_url: "https://api.github.com/users/VibhuJawa/repos",
events_url: "https://api.github.com/users/VibhuJawa/events{/privacy}",
received_events_url:
"https://api.github.com/users/VibhuJawa/received_events",
type: "User",
site_admin: false,
},
repo: {
id: 200880888,
node_id: "MDEwOlJlcG9zaXRvcnkyMDA4ODA4ODg=",
name: "cudf",
full_name: "VibhuJawa/cudf",
private: false,
owner: {
login: "VibhuJawa",
id: 53796099,
node_id: "MDQ6VXNlcjUzNzk2MDk5",
avatar_url: "https://avatars1.githubusercontent.com/u/53796099?v=4",
gravatar_id: "",
url: "https://api.github.com/users/VibhuJawa",
html_url: "https://github.com/VibhuJawa",
followers_url: "https://api.github.com/users/VibhuJawa/followers",
following_url:
"https://api.github.com/users/VibhuJawa/following{/other_user}",
gists_url: "https://api.github.com/users/VibhuJawa/gists{/gist_id}",
starred_url:
"https://api.github.com/users/VibhuJawa/starred{/owner}{/repo}",
subscriptions_url:
"https://api.github.com/users/VibhuJawa/subscriptions",
organizations_url: "https://api.github.com/users/VibhuJawa/orgs",
repos_url: "https://api.github.com/users/VibhuJawa/repos",
events_url: "https://api.github.com/users/VibhuJawa/events{/privacy}",
received_events_url:
"https://api.github.com/users/VibhuJawa/received_events",
type: "User",
site_admin: false,
},
html_url: "https://github.com/VibhuJawa/cudf",
description: "cuDF - GPU DataFrame Library",
fork: true,
url: "https://api.github.com/repos/VibhuJawa/cudf",
forks_url: "https://api.github.com/repos/VibhuJawa/cudf/forks",
keys_url: "https://api.github.com/repos/VibhuJawa/cudf/keys{/key_id}",
collaborators_url:
"https://api.github.com/repos/VibhuJawa/cudf/collaborators{/collaborator}",
teams_url: "https://api.github.com/repos/VibhuJawa/cudf/teams",
hooks_url: "https://api.github.com/repos/VibhuJawa/cudf/hooks",
issue_events_url:
"https://api.github.com/repos/VibhuJawa/cudf/issues/events{/number}",
events_url: "https://api.github.com/repos/VibhuJawa/cudf/events",
assignees_url:
"https://api.github.com/repos/VibhuJawa/cudf/assignees{/user}",
branches_url:
"https://api.github.com/repos/VibhuJawa/cudf/branches{/branch}",
tags_url: "https://api.github.com/repos/VibhuJawa/cudf/tags",
blobs_url:
"https://api.github.com/repos/VibhuJawa/cudf/git/blobs{/sha}",
git_tags_url:
"https://api.github.com/repos/VibhuJawa/cudf/git/tags{/sha}",
git_refs_url:
"https://api.github.com/repos/VibhuJawa/cudf/git/refs{/sha}",
trees_url:
"https://api.github.com/repos/VibhuJawa/cudf/git/trees{/sha}",
statuses_url:
"https://api.github.com/repos/VibhuJawa/cudf/statuses/{sha}",
languages_url: "https://api.github.com/repos/VibhuJawa/cudf/languages",
stargazers_url:
"https://api.github.com/repos/VibhuJawa/cudf/stargazers",
contributors_url:
"https://api.github.com/repos/VibhuJawa/cudf/contributors",
subscribers_url:
"https://api.github.com/repos/VibhuJawa/cudf/subscribers",
subscription_url:
"https://api.github.com/repos/VibhuJawa/cudf/subscription",
commits_url:
"https://api.github.com/repos/VibhuJawa/cudf/commits{/sha}",
git_commits_url:
"https://api.github.com/repos/VibhuJawa/cudf/git/commits{/sha}",
comments_url:
"https://api.github.com/repos/VibhuJawa/cudf/comments{/number}",
issue_comment_url:
"https://api.github.com/repos/VibhuJawa/cudf/issues/comments{/number}",
contents_url:
"https://api.github.com/repos/VibhuJawa/cudf/contents/{+path}",
compare_url:
"https://api.github.com/repos/VibhuJawa/cudf/compare/{base}...{head}",
merges_url: "https://api.github.com/repos/VibhuJawa/cudf/merges",
archive_url:
"https://api.github.com/repos/VibhuJawa/cudf/{archive_format}{/ref}",
downloads_url: "https://api.github.com/repos/VibhuJawa/cudf/downloads",
issues_url:
"https://api.github.com/repos/VibhuJawa/cudf/issues{/number}",
pulls_url: "https://api.github.com/repos/VibhuJawa/cudf/pulls{/number}",
milestones_url:
"https://api.github.com/repos/VibhuJawa/cudf/milestones{/number}",
notifications_url:
"https://api.github.com/repos/VibhuJawa/cudf/notifications{?since,all,participating}",
labels_url: "https://api.github.com/repos/VibhuJawa/cudf/labels{/name}",
releases_url:
"https://api.github.com/repos/VibhuJawa/cudf/releases{/id}",
deployments_url:
"https://api.github.com/repos/VibhuJawa/cudf/deployments",
created_at: "2019-08-06T15:44:50Z",
updated_at: "2019-11-13T14:08:59Z",
pushed_at: "2020-12-15T16:32:53Z",
git_url: "git://github.com/VibhuJawa/cudf.git",
ssh_url: "[email protected]:VibhuJawa/cudf.git",
clone_url: "https://github.com/VibhuJawa/cudf.git",
svn_url: "https://github.com/VibhuJawa/cudf",
homepage: "http://rapids.ai",
size: 80749,
stargazers_count: 0,
watchers_count: 0,
language: "Cuda",
has_issues: false,
has_projects: true,
has_downloads: true,
has_wiki: true,
has_pages: false,
forks_count: 0,
mirror_url: null,
archived: false,
disabled: false,
open_issues_count: 0,
license: {
key: "apache-2.0",
name: "Apache License 2.0",
spdx_id: "Apache-2.0",
url: "https://api.github.com/licenses/apache-2.0",
node_id: "MDc6TGljZW5zZTI=",
},
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gravatar_id: "",
url: "https://api.github.com/users/rapidsai",
html_url: "https://github.com/rapidsai",
followers_url: "https://api.github.com/users/rapidsai/followers",
following_url:
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gists_url: "https://api.github.com/users/rapidsai/gists{/gist_id}",
starred_url:
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organizations_url: "https://api.github.com/users/rapidsai/orgs",
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events_url: "https://api.github.com/users/rapidsai/events{/privacy}",
received_events_url:
"https://api.github.com/users/rapidsai/received_events",
type: "Organization",
site_admin: false,
},
repo: {
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full_name: "rapidsai/cudf",
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owner: {
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gravatar_id: "",
url: "https://api.github.com/users/rapidsai",
html_url: "https://github.com/rapidsai",
followers_url: "https://api.github.com/users/rapidsai/followers",
following_url:
"https://api.github.com/users/rapidsai/following{/other_user}",
gists_url: "https://api.github.com/users/rapidsai/gists{/gist_id}",
starred_url:
"https://api.github.com/users/rapidsai/starred{/owner}{/repo}",
subscriptions_url:
"https://api.github.com/users/rapidsai/subscriptions",
organizations_url: "https://api.github.com/users/rapidsai/orgs",
repos_url: "https://api.github.com/users/rapidsai/repos",
events_url: "https://api.github.com/users/rapidsai/events{/privacy}",
received_events_url:
"https://api.github.com/users/rapidsai/received_events",
type: "Organization",
site_admin: false,
},
html_url: "https://github.com/rapidsai/cudf",
description: "cuDF - GPU DataFrame Library",
fork: false,
url: "https://api.github.com/repos/rapidsai/cudf",
forks_url: "https://api.github.com/repos/rapidsai/cudf/forks",
keys_url: "https://api.github.com/repos/rapidsai/cudf/keys{/key_id}",
collaborators_url:
"https://api.github.com/repos/rapidsai/cudf/collaborators{/collaborator}",
teams_url: "https://api.github.com/repos/rapidsai/cudf/teams",
hooks_url: "https://api.github.com/repos/rapidsai/cudf/hooks",
issue_events_url:
"https://api.github.com/repos/rapidsai/cudf/issues/events{/number}",
events_url: "https://api.github.com/repos/rapidsai/cudf/events",
assignees_url:
"https://api.github.com/repos/rapidsai/cudf/assignees{/user}",
branches_url:
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tags_url: "https://api.github.com/repos/rapidsai/cudf/tags",
blobs_url: "https://api.github.com/repos/rapidsai/cudf/git/blobs{/sha}",
git_tags_url:
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git_refs_url:
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trees_url: "https://api.github.com/repos/rapidsai/cudf/git/trees{/sha}",
statuses_url:
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languages_url: "https://api.github.com/repos/rapidsai/cudf/languages",
stargazers_url: "https://api.github.com/repos/rapidsai/cudf/stargazers",
contributors_url:
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subscribers_url:
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subscription_url:
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commits_url: "https://api.github.com/repos/rapidsai/cudf/commits{/sha}",
git_commits_url:
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comments_url:
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issue_comment_url:
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contents_url:
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compare_url:
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merges_url: "https://api.github.com/repos/rapidsai/cudf/merges",
archive_url:
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downloads_url: "https://api.github.com/repos/rapidsai/cudf/downloads",
issues_url:
"https://api.github.com/repos/rapidsai/cudf/issues{/number}",
pulls_url: "https://api.github.com/repos/rapidsai/cudf/pulls{/number}",
milestones_url:
"https://api.github.com/repos/rapidsai/cudf/milestones{/number}",
notifications_url:
"https://api.github.com/repos/rapidsai/cudf/notifications{?since,all,participating}",
labels_url: "https://api.github.com/repos/rapidsai/cudf/labels{/name}",
releases_url:
"https://api.github.com/repos/rapidsai/cudf/releases{/id}",
deployments_url:
"https://api.github.com/repos/rapidsai/cudf/deployments",
created_at: "2017-05-07T03:43:37Z",
updated_at: "2020-12-17T18:37:37Z",
pushed_at: "2020-12-17T18:37:33Z",
git_url: "git://github.com/rapidsai/cudf.git",
ssh_url: "[email protected]:rapidsai/cudf.git",
clone_url: "https://github.com/rapidsai/cudf.git",
svn_url: "https://github.com/rapidsai/cudf",
homepage: "http://rapids.ai",
size: 79812,
stargazers_count: 3543,
watchers_count: 3543,
language: "C++",
has_issues: true,
has_projects: true,
has_downloads: true,
has_wiki: true,
has_pages: false,
forks_count: 470,
mirror_url: null,
archived: false,
disabled: false,
open_issues_count: 571,
license: {
key: "apache-2.0",
name: "Apache License 2.0",
spdx_id: "Apache-2.0",
url: "https://api.github.com/licenses/apache-2.0",
node_id: "MDc6TGljZW5zZTI=",
},
forks: 470,
open_issues: 571,
watchers: 3543,
default_branch: "branch-0.18",
},
},
_links: {
self: {
href: "https://api.github.com/repos/rapidsai/cudf/pulls/6775",
},
html: {
href: "https://github.com/rapidsai/cudf/pull/6775",
},
issue: {
href: "https://api.github.com/repos/rapidsai/cudf/issues/6775",
},
comments: {
href: "https://api.github.com/repos/rapidsai/cudf/issues/6775/comments",
},
review_comments: {
href: "https://api.github.com/repos/rapidsai/cudf/pulls/6775/comments",
},
review_comment: {
href: "https://api.github.com/repos/rapidsai/cudf/pulls/comments{/number}",
},
commits: {
href: "https://api.github.com/repos/rapidsai/cudf/pulls/6775/commits",
},
statuses: {
href: "https://api.github.com/repos/rapidsai/cudf/statuses/3dfbd68fe975eb04ae7b2ffa6369856ae183963d",
},
},
author_association: "MEMBER",
active_lock_reason: null,
merged: true,
mergeable: true,
rebaseable: null,
mergeable_state: "clean",
merged_by: {
login: "rapids-ops-bot[bot]",
id: 73299758,
node_id: "MDM6Qm90NzMyOTk3NTg=",
avatar_url: "https://avatars2.githubusercontent.com/u/43887749?v=4",
gravatar_id: "",
url: "https://api.github.com/users/rapids-ops-bot%5Bbot%5D",
html_url: "https://github.com/apps/rapids-ops-bot",
followers_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/followers",
following_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/following{/other_user}",
gists_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/gists{/gist_id}",
starred_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/starred{/owner}{/repo}",
subscriptions_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/subscriptions",
organizations_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/orgs",
repos_url: "https://api.github.com/users/rapids-ops-bot%5Bbot%5D/repos",
events_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/events{/privacy}",
received_events_url:
"https://api.github.com/users/rapids-ops-bot%5Bbot%5D/received_events",
type: "Bot",
site_admin: false,
},
comments: 6,
review_comments: 24,
maintainer_can_modify: false,
commits: 27,
additions: 222,
deletions: 3,
changed_files: 9,
},
});
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/releases.json
|
{
"legacy": {
"version": "0.18",
"date": "25 February 2021"
},
"stable": {
"version": "0.19",
"date": "15 April 2021"
},
"nightly": {
"version": "21.06",
"cudf_dev": {
"start": "2021-03-25",
"end": "2021-05-18",
"days": 39
},
"other_dev": {
"start": "2021-04-01",
"end": "2021-05-25",
"days": 39
},
"cudf_burndown": {
"start": "2021-05-19",
"end": "2021-05-25",
"days": 5
},
"other_burndown": {
"start": "2021-05-26",
"end": "2021-06-02",
"days": 5
},
"cudf_codefreeze": {
"start": "2021-05-26",
"end": "2021-06-02",
"days": 4
},
"other_codefreeze": {
"start": "2021-06-03",
"end": "2021-06-08",
"days": 4
},
"release": {
"start": "2021-06-09",
"end": "2021-06-10",
"days": 2
}
},
"next_nightly": {
"version": "21.08",
"cudf_dev": {
"start": "2021-05-19",
"end": "2021-07-14",
"days": 39
},
"other_dev": {
"start": "2021-05-26",
"end": "2021-07-21",
"days": 39
},
"cudf_burndown": {
"start": "2021-07-14",
"end": "2021-07-20",
"days": 5
},
"other_burndown": {
"start": "2021-07-21",
"end": "2021-07-28",
"days": 6
},
"cudf_codefreeze": {
"start": "2021-07-21",
"end": "2021-07-28",
"days": 6
},
"other_codefreeze": {
"start": "2021-07-29",
"end": "2021-08-03",
"days": 4
},
"release": {
"start": "2021-08-04",
"end": "2021-08-05",
"days": 2
}
}
}
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/list_pulls.json
|
{
"branch_checker": [
{
"number": 1234,
"title": "[RELEASE] cuml v0.18",
"user": {
"login": "GPUtester"
},
"labels": [{ "name": "breaking" }, { "name": "bug" }],
"merged_at": "2021-01-27T13:38:56Z",
"base": {
"repo": {
"name": "cudf",
"default_branch": "branch-0.18",
"owner": {
"login": "rapidsai"
}
}
},
"head": {
"sha": "randomsha1234"
}
}
],
"recently_updated": [
{
"number": 1234,
"title": "[RELEASE] cuml v0.18",
"user": {
"login": "GPUtester"
},
"labels": [{ "name": "breaking" }, { "name": "bug" }],
"merged_at": "2021-01-27T13:38:56Z",
"base": {
"repo": {
"name": "cudf",
"default_branch": "branch-0.18",
"owner": {
"login": "rapidsai"
}
}
},
"head": {
"sha": "randomsha1234"
}
}
],
"release_drafter": [
{
"number": 1234,
"title": "[WIP] [skip-ci] Some PR title",
"user": {
"login": "octokit"
},
"labels": [{ "name": "breaking" }, { "name": "bug" }],
"merged_at": "2021-01-27T13:38:56Z"
},
{
"number": 456,
"title": "Some Doc PR",
"user": {
"login": "ajschmidt8"
},
"labels": [{ "name": "doc" }],
"merged_at": "2021-01-27T13:38:56Z"
},
{
"number": 4567,
"title": "Another Title",
"user": {
"login": "octokit"
},
"labels": [],
"merged_at": "2021-01-27T13:38:56Z"
},
{
"number": 6432,
"title": "Another Title",
"user": {
"login": "octokit"
},
"labels": [{ "name": "doc" }],
"merged_at": null
},
{
"number": 9134,
"title": "[gpuCI] Forward-merge branch-0.17 to branch-0.18 [skip ci]",
"user": {
"login": "octokit"
},
"labels": [{ "name": "doc" }],
"merged_at": "2021-01-27T13:38:56Z"
}
]
}
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/list_reviews.json
|
[
{
"user": {
"login": "kkraus14"
},
"state": "CHANGES_REQUESTED"
},
{
"user": {
"login": "VibhuJawa"
},
"state": "APPROVED"
},
{
"user": {
"login": "VibhuJawa"
},
"state": "APPROVED"
}
]
| 0 |
rapidsai_public_repos/ops-bot/test/fixtures
|
rapidsai_public_repos/ops-bot/test/fixtures/responses/list_commits.json
|
{
"data": [
{
"sha": "1771a8fc18ac647c9172209fa3b39b40437ac85c",
"author": {
"login": "VibhuJawa"
}
},
{
"sha": "f0f53c76aacd04769a568453fc138e124af7bfdf",
"author": {
"login": "VibhuJawa"
}
},
{
"sha": "f3b0e064eb748a343f78941ecb30ddcfb92f7644",
"author": {
"login": "VibhuJawa"
}
},
{
"sha": "47c917220b748a343f78941ecb30ddcfb923b0e0",
"author": {
"login": "VibhuJawa"
}
},
{
"sha": "0ddcf220b748a343f78941ecb30ddcfb92f76441",
"author": {
"login": "VibhuJawa"
}
},
{
"sha": "stophere",
"author": null
},
{
"sha": "45bd967c09564f40af65cce9f3b88ddb87614c0a",
"author": null
}
]
}
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/shared-actions/README.md
|
# shared-actions
Contains all of the shared composite actions used by RAPIDS.
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/rapids-github-info/action.yml
|
name: "RAPIDS GitHub info"
inputs:
repo:
type: string
branch:
type: string
date:
type: string
sha:
type: string
outputs:
rapids_repository:
value: ${{ steps.standardize-repo-info.outputs.RAPIDS_REPOSITORY }}
rapids_sha:
value: ${{ steps.standardize-repo-info.outputs.RAPIDS_SHA }}
rapids_ref_name:
value: ${{ steps.standardize-repo-info.outputs.RAPIDS_REF_NAME }}
rapids_nightly_date:
value: ${{ steps.standardize-repo-info.outputs.RAPIDS_NIGHTLY_DATE }}
runs:
using: composite
steps:
- name: Standardize repository information
id: standardize-repo-info
shell: bash
run: |
echo "RAPIDS_REPOSITORY=${{ inputs.repo || github.repository }}" | tee --append "${GITHUB_ENV}" "${GITHUB_OUTPUT}"
sha="${{ inputs.sha }}"
if [[ "${sha}" == "" ]]; then
sha=$(git rev-parse HEAD)
fi
echo "RAPIDS_SHA=${sha}" | tee --append "${GITHUB_ENV}" "${GITHUB_OUTPUT}"
echo "RAPIDS_REF_NAME=${{ inputs.branch || github.ref_name }}" | tee --append "${GITHUB_ENV}" "${GITHUB_OUTPUT}"
echo "RAPIDS_NIGHTLY_DATE=${{ inputs.date }}" | tee --append "${GITHUB_ENV}" "${GITHUB_OUTPUT}"
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/rapids-github-info/README.md
|
# rapids-github-info
This composite action is intended to be used in workflows that are run from either the source repository (e.g., cudf, rmm) or an external repository (e.g., actions).
It will take the input repo/branch/date/sha and pass it through if they are set (by an external repo) or use `github.` variables if they are unset (running from the source repo itself).
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/get-pr-info/action.yml
|
name: "Get PR Info"
outputs:
pr-info:
description: "String encoded JSON object containing pull-request information"
value: ${{ steps.get-pull-request.outputs.result }}
runs:
using: "composite"
steps:
- uses: actions/github-script@v6
id: get-pull-request
with:
retries: 3
script: |
const branch = process.env.GITHUB_REF_NAME;
const prBranchPattern = new RegExp("^pull-request/[0-9]+$");
if (!branch.match(prBranchPattern)) {
throw new Error(`${branch} does not match PR branch pattern.`);
}
const prNumber = branch.split("/")[1];
const {data: pullRequest} = await github.rest.pulls.get({
pull_number: prNumber,
owner: context.repo.owner,
repo: context.repo.repo,
});
return pullRequest;
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/get-pr-info/README.md
|
# shared-actions# get-pr-info
This composite action is intended to be used in workflows that run on `pull-request/<PR_NUMBER>` branches.
It will extract the pull-request number from the branch name and use it to fetch information about the corresponding pull-request from GitHub's API.
The JSON object returned by this composite action is described here: [https://docs.github.com/en/rest/pulls/pulls#get-a-pull-request](https://docs.github.com/en/rest/pulls/pulls#get-a-pull-request).
## Example Usage
```yaml
name: Get PR Info Demo
on:
push:
branches:
- "pull-request/[0-9]+"
jobs:
main_job:
runs-on: ubuntu-latest
steps:
- id: get-pr-info
uses: rapidsai/shared-actions/get-pr-info@main
- run: echo "${RAPIDS_BASE_BRANCH}"
env:
RAPIDS_BASE_BRANCH: ${{ fromJSON(steps.get-pr-info.outputs.pr-info).base.ref }}
```
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/dockerhub-script/action.yml
|
name: Run DockerHub Script
description: |
This composite action generates a DockerHub token and runs a script with it. The token is revoked when the composite action exits.
inputs:
DOCKERHUB_USER:
description: DockerHub username
required: true
DOCKERHUB_TOKEN:
description: DockerHub password
required: true
script:
description: Script to run
required: true
runs:
using: composite
steps:
- name: Generate DockerHub Token
shell: bash
run: |
logout() {
curl -X POST \
-H "Authorization: JWT $HUB_TOKEN" \
"https://hub.docker.com/v2/logout/"
}
trap logout EXIT
HUB_TOKEN=$(
curl -s -H "Content-Type: application/json" \
-X POST \
-d "{\"username\": \"${DOCKERHUB_USER}\", \"password\": \"${DOCKERHUB_TOKEN}\"}" \
https://hub.docker.com/v2/users/login/ | jq -r .token \
)
echo "::add-mask::${HUB_TOKEN}"
. ${{ inputs.script }}
env:
DOCKERHUB_USER: ${{ inputs.DOCKERHUB_USER }}
DOCKERHUB_TOKEN: ${{ inputs.DOCKERHUB_TOKEN }}
| 0 |
rapidsai_public_repos/shared-actions
|
rapidsai_public_repos/shared-actions/dockerhub-script/README.md
|
# Get DockerHub Token
This composite action generates a DockerHub token and runs a script with it. The token is revoked when the composite action exits.
## Inputs
- `DOCKERHUB_USER` (required): DockerHub username
- `DOCKERHUB_TOKEN` (required): DockerHub password
- `script` (required): Script to run
## Inner variables
These are variables pre-computed by this action which become usable by the scripts that you pass to be run in this action.
- `HUB_TOKEN`: The generated DockerHub token required for authorization against the DockerHub API.
## Usage
```yaml
jobs:
example:
steps:
- name: Run With DockerHub Token
uses: rapidsai/shared-actions/dockerhub-script@main
with:
DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USERNAME }}
DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_PASSWORD }}
script: ci/do_something.sh
env: #(if needed)
IMAGE_NAME: rapidsai/base:some-random-tag
ARCHES: ${{ toJSON(matrix.ARCHES) }}
#...etc
```
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/node-rapids.code-workspace
|
{
"folders": [
{
"name": "node-rapids",
"path": "."
},
{
"name": "core",
"path": "modules/core"
},
{
"name": "cuda",
"path": "modules/cuda"
},
{
"name": "rmm",
"path": "modules/rmm"
},
{
"name": "cudf",
"path": "modules/cudf"
},
{
"name": "cuml",
"path": "modules/cuml"
},
{
"name": "cugraph",
"path": "modules/cugraph"
},
{
"name": "cuspatial",
"path": "modules/cuspatial"
},
{
"name": "io",
"path": "modules/io"
},
{
"name": "sql",
"path": "modules/sql"
},
{
"name": "deck.gl",
"path": "modules/deck.gl"
},
{
"name": "demo",
"path": "modules/demo"
},
{
"name": "glfw",
"path": "modules/glfw"
},
{
"name": "jsdom",
"path": "modules/jsdom"
},
{
"name": "webgl",
"path": "modules/webgl"
}
],
"extensions": {
"recommendations": [
"twxs.cmake",
"slevesque.shader",
"ms-vscode.cpptools",
"seaube.clangformat",
"cschlosser.doxdocgen",
"dbaeumer.vscode-eslint",
"davidanson.vscode-markdownlint",
"augustocdias.tasks-shell-input",
"dotiful.dotfiles-syntax-highlighting",
"llvm-vs-code-extensions.vscode-clangd"
]
},
"settings": {
"C_Cpp.formatting": "disabled",
"C_Cpp.autocomplete": "disabled",
"C_Cpp.errorSquiggles": "disabled",
"C_Cpp.intelliSenseEngine": "disabled",
"C_Cpp.configurationWarnings": "disabled",
"C_Cpp.autoAddFileAssociations": false,
"C_Cpp.vcpkg.enabled": false,
"clangd.path": "/usr/bin/clangd",
"clangd.arguments": [
"--log=info",
"--pch-storage=disk",
"--cross-file-rename",
"--completion-parse=auto",
"--fallback-style=Google",
"--compile-commands-dir=",
"--background-index=true",
"--all-scopes-completion",
"--header-insertion=never",
"--completion-style=detailed",
"--header-insertion-decorators"
],
// Configure the seaube.clangformat plugin
"clang-format.fallbackStyle": "Google",
"clang-format.executable": "clang-format-17",
"clang-format.language.typescript.enable": true,
"debug.terminal.clearBeforeReusing": true,
// Configure ESLint plugin
"eslint.debug": true,
"eslint.nodeEnv": "production",
"eslint.lintTask.enable": true,
// "eslint.trace.server": "verbose",
"eslint.run": "onSave",
"eslint.probe": [
// "javascript",
// "javascriptreact",
"typescript"
// "typescriptreact",
// "html",
// "vue",
// "markdown"
],
"eslint.workingDirectories": [
"./node-rapids",
"./node-rapids/modules/core",
"./node-rapids/modules/cuda",
"./node-rapids/modules/cudf",
"./node-rapids/modules/cugraph",
"./node-rapids/modules/cuspatial",
"./node-rapids/modules/deck.gl",
"./node-rapids/modules/demo",
"./node-rapids/modules/glfw",
"./node-rapids/modules/webgl",
"./node-rapids/modules/sql",
"./node-rapids/modules/io"
],
"eslint.options": {
"configFile": ".eslintrc.js"
},
"git.ignoreLimitWarning": true,
"markdownlint.config": {
"default": true,
"MD033": false
},
"files.trimFinalNewlines": true,
"files.insertFinalNewline": true,
"files.trimTrailingWhitespace": true,
"files.associations": {
".creds": "dotenv",
"*.cu": "cuda-cpp",
"*.cuh": "cuda-cpp",
"*.glsl.js": "glsl",
"*.glsl.ts": "glsl"
},
"files.exclude": {
"**/.git": true,
"**/.DS_Store": true,
},
"files.watcherExclude": {
"**/.git/objects/**": true,
"**/.git/subtree-cache/**": true,
"**/build/**": true,
"**/.next/**": true,
"**/.cache/**": true,
"**/.cmake-js/**": true,
"**/node_modules/**": true
},
"search.exclude": {
"**/napi.h": false,
"**/napi-inl.h": false,
"**/napi-inl.deprecated.h": false,
"**/node_api.h": false,
"**/build/**": true,
"**/.next/**": true,
"**/.cache/**": true,
"**/.cmake-js/**": true,
"**/node_modules/**": true
},
"terminal.integrated.cwd": "${workspaceFolder:node-rapids}",
"terminal.integrated.enableFileLinks": "on",
"terminal.integrated.profiles.linux": {
"bash": {
"path": "bash",
"icon": "terminal-bash",
"args": [
"-li"
]
}
},
"terminal.integrated.defaultProfile.linux": "bash",
// Fix to make "<ctrl>+<shift>+<B>" tasks list launch instantly
"typescript.tsc.autoDetect": "off",
"typescript.tsdk": "node-rapids/node_modules/typescript/lib",
// Set the formatters and formatting options for each language
"[c]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[cpp]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[cuda]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[cuda-cpp]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[json]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "vscode.json-language-features"
},
"[javascript]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[typescript]": {
"editor.tabSize": 2,
"editor.formatOnSave": true,
"editor.formatOnSaveMode": "file",
"editor.defaultFormatter": "seaube.clangformat"
},
"[javascriptreact]": {
"editor.tabSize": 2,
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| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/package.json
|
{
"private": true,
"name": "node-rapids",
"version": "22.12.2",
"license": "Apache-2.0",
"author": "NVIDIA, Inc. (https://nvidia.com/)",
"maintainers": [
"Paul Taylor <[email protected]>"
],
"homepage": "https://github.com/rapidsai/node#readme",
"bugs": {
"url": "https://github.com/rapidsai/node/issues"
},
"repository": {
"type": "git",
"url": "git+https://github.com/rapidsai/node.git"
},
"scripts": {
"build": "scripts/exec.js run build --concurrency 1",
"build:debug": "scripts/exec.js run build:debug --concurrency 1",
"clean": "scripts/exec.js clean",
"clean:build": "npx rimraf .cache/source .cache/binary && yarn tsc:clean && yarn cpp:clean && yarn build",
"compile": "scripts/exec.js run compile --concurrency 1",
"compile:debug": "scripts/exec.js run compile:debug --concurrency 1",
"rebuild": "scripts/exec.js run rebuild --concurrency 1",
"rebuild:debug": "scripts/exec.js run rebuild:debug --concurrency 1",
"cpp:clean": "scripts/exec.js run cpp:clean --parallel",
"cpp:clean:debug": "scripts/exec.js run cpp:clean:debug --parallel",
"cpp:build": "scripts/exec.js run cpp:build --concurrency 1",
"cpp:build:debug": "scripts/exec.js run cpp:build:debug --concurrency 1",
"cpp:compile": "scripts/exec.js run cpp:compile --concurrency 1",
"cpp:compile:debug": "scripts/exec.js run cpp:compile:debug --concurrency 1",
"cpp:configure": "scripts/exec.js run cpp:configure --concurrency 1",
"cpp:configure:debug": "scripts/exec.js run cpp:configure:debug --concurrency 1",
"cpp:rebuild": "scripts/exec.js run cpp:rebuild --concurrency 1",
"cpp:rebuild:debug": "scripts/exec.js run cpp:rebuild:debug --concurrency 1",
"cpp:reconfigure": "scripts/exec.js run cpp:reconfigure --concurrency 1",
"cpp:reconfigure:debug": "scripts/exec.js run cpp:reconfigure:debug --concurrency 1",
"demo": "scripts/exec.js demo",
"dev:install-cpp-dependencies": "modules/core/bin/exec_install_deps.js",
"dev:npm:pack": "scripts/exec.js npm-pack",
"dev:relink-bin-dirs": "scripts/exec.js relink-bin-dirs",
"doc:packages": "scripts/exec.js run doc",
"doc": "rimraf doc && typedoc --options typedoc.js && yarn doc:packages",
"docker:build:devel": "yarn docker:build:devel:main && yarn docker:build:devel:notebook",
"docker:build:devel:main": "touch .env .creds && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.devel.yml build --pull --force-rm main",
"docker:build:devel:packages": "touch .env .creds && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.devel.yml build --force-rm packages",
"docker:build:devel:notebook": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.devel.yml build --force-rm notebook",
"docker:build:runtime": "yarn docker:build:runtime:base && yarn docker:build:devel:packages && yarn docker:build:runtime:libs && yarn docker:build:runtime:notebook",
"docker:build:runtime:base": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm base",
"docker:build:runtime:libs": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm --parallel main demo glfw cudf sql cuml cugraph cuspatial",
"docker:build:runtime:main": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm main",
"docker:build:runtime:demo": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm demo",
"docker:build:runtime:glfw": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm glfw",
"docker:build:runtime:cudf": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm cudf",
"docker:build:runtime:sql": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm sql",
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"docker:build:runtime:notebook": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml build --force-rm notebook",
"docker:run:devel:notebook": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.devel.yml run --rm -u $(id -u):$(id -g) notebook",
"docker:run:devel": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.devel.yml run --rm -u $(id -u):$(id -g) main",
"docker:run:runtime:cudf": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm cudf",
"docker:run:runtime:cugraph": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm cugraph",
"docker:run:runtime:cuml": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm cuml",
"docker:run:runtime:cuspatial": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm cuspatial",
"docker:run:runtime:demo": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm demo",
"docker:run:runtime:glfw": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm glfw",
"docker:run:runtime:io": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm io",
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"docker:run:runtime:sql": "touch .env && npx cross-env DOCKER_BUILDKIT=1 DOCKER_SCAN_SUGGEST=false docker-compose -f docker-compose.runtime.yml run --rm sql",
"lint": "scripts/exec.js lint --fix",
"lint:ci": "scripts/exec.js lint",
"nuke:from:orbit": "yarn cache clean && yarn clean && yarn && yarn build",
"postinstall": "yarn dev:relink-bin-dirs",
"test": "scripts/exec.js test",
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"tsc:clean": "scripts/exec.js run tsc:clean",
"tsc:watch": "scripts/exec.js run tsc:watch --parallel",
"postversion": "scripts/exec.js postversion"
},
"workspaces": [
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"modules/demo/xterm",
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],
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},
"config": {
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}
}
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/yarn.lock
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string-width "^4.1.0"
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write-file-atomic@^2.0.0, write-file-atomic@^2.3.0, write-file-atomic@^2.4.2:
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dependencies:
graceful-fs "^4.1.11"
imurmurhash "^0.1.4"
signal-exit "^3.0.2"
write-file-atomic@^3.0.0:
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dependencies:
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is-typedarray "^1.0.0"
signal-exit "^3.0.2"
typedarray-to-buffer "^3.1.5"
write-json-file@^2.2.0:
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dependencies:
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graceful-fs "^4.1.2"
make-dir "^1.0.0"
pify "^3.0.0"
sort-keys "^2.0.0"
write-file-atomic "^2.0.0"
write-json-file@^3.2.0:
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dependencies:
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graceful-fs "^4.1.15"
make-dir "^2.1.0"
pify "^4.0.1"
sort-keys "^2.0.0"
write-file-atomic "^2.4.2"
write-pkg@^3.1.0:
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dependencies:
sort-keys "^2.0.0"
write-json-file "^2.2.0"
ws@^7, ws@^7.4.5, ws@^7.5.5:
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ws@~7.4.2:
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ws@~8.11.0:
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xml-name-validator@^3.0.0:
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xmlchars@^2.2.0:
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xmlhttprequest-ssl@~2.0.0:
version "2.0.0"
resolved "https://registry.npmjs.org/xmlhttprequest-ssl/-/xmlhttprequest-ssl-2.0.0.tgz#91360c86b914e67f44dce769180027c0da618c67"
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xmlhttprequest@1:
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resolved "https://registry.npmjs.org/xmlhttprequest/-/xmlhttprequest-1.8.0.tgz#67fe075c5c24fef39f9d65f5f7b7fe75171968fc"
integrity sha512-58Im/U0mlVBLM38NdZjHyhuMtCqa61469k2YP/AaPbvCoV9aQGUpbJBj1QRm2ytRiVQBD/fsw7L2bJGDVQswBA==
xtend@^4.0.0, xtend@^4.0.2, xtend@~4.0.1:
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resolved "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
[email protected]:
version "0.4.1"
resolved "https://registry.npmjs.org/xterm-addon-webgl/-/xterm-addon-webgl-0.4.1.tgz#c245f3310e7dbb0759e5eabaf125bbc9c9ab3955"
integrity sha512-z/YRmAji7y3ngjvSgoNCQxqYioarNplNqWv/KgtF4nRkBTWkhLHAg1QFNcH7XxUV/O8VuZoGAfp4LPX2hVz/yQ==
[email protected]:
version "4.3.0"
resolved "https://registry.npmjs.org/xterm/-/xterm-4.3.0.tgz#9a302efefe75172d4f7ea3afc20f9bd983f05027"
integrity sha512-6dnrC4nxgnRKQzIWwC5HA0mnT9/rpDPZflUIr24gdcdSMTKM1QQcor4qQ/xz4Zerz6AIL/CuuBPypFfzsB63dQ==
y18n@^4.0.0:
version "4.0.3"
resolved "https://registry.npmjs.org/y18n/-/y18n-4.0.3.tgz#b5f259c82cd6e336921efd7bfd8bf560de9eeedf"
integrity sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ==
y18n@^5.0.5:
version "5.0.8"
resolved "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
yallist@^3.0.0, yallist@^3.0.2, yallist@^3.1.1:
version "3.1.1"
resolved "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
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yallist@^4.0.0:
version "4.0.0"
resolved "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
yaml@^1.10.0, yaml@^1.10.2, yaml@^1.7.2:
version "1.10.2"
resolved "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b"
integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
[email protected], yargs-parser@^20.0.0, yargs-parser@^20.2.2, yargs-parser@^20.2.3:
version "20.2.9"
resolved "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz#2eb7dc3b0289718fc295f362753845c41a0c94ee"
integrity sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==
yargs-parser@^15.0.1:
version "15.0.3"
resolved "https://registry.npmjs.org/yargs-parser/-/yargs-parser-15.0.3.tgz#316e263d5febe8b38eef61ac092b33dfcc9b1115"
integrity sha512-/MVEVjTXy/cGAjdtQf8dW3V9b97bPN7rNn8ETj6BmAQL7ibC7O1Q9SPJbGjgh3SlwoBNXMzj/ZGIj8mBgl12YA==
dependencies:
camelcase "^5.0.0"
decamelize "^1.2.0"
yargs-parser@^18.1.2:
version "18.1.3"
resolved "https://registry.npmjs.org/yargs-parser/-/yargs-parser-18.1.3.tgz#be68c4975c6b2abf469236b0c870362fab09a7b0"
integrity sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==
dependencies:
camelcase "^5.0.0"
decamelize "^1.2.0"
yargs-parser@^21.1.1:
version "21.1.1"
resolved "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
integrity sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==
yargs@^14.2.2:
version "14.2.3"
resolved "https://registry.npmjs.org/yargs/-/yargs-14.2.3.tgz#1a1c3edced1afb2a2fea33604bc6d1d8d688a414"
integrity sha512-ZbotRWhF+lkjijC/VhmOT9wSgyBQ7+zr13+YLkhfsSiTriYsMzkTUFP18pFhWwBeMa5gUc1MzbhrO6/VB7c9Xg==
dependencies:
cliui "^5.0.0"
decamelize "^1.2.0"
find-up "^3.0.0"
get-caller-file "^2.0.1"
require-directory "^2.1.1"
require-main-filename "^2.0.0"
set-blocking "^2.0.0"
string-width "^3.0.0"
which-module "^2.0.0"
y18n "^4.0.0"
yargs-parser "^15.0.1"
yargs@^15.0.2, yargs@^15.4.1:
version "15.4.1"
resolved "https://registry.npmjs.org/yargs/-/yargs-15.4.1.tgz#0d87a16de01aee9d8bec2bfbf74f67851730f4f8"
integrity sha512-aePbxDmcYW++PaqBsJ+HYUFwCdv4LVvdnhBy78E57PIor8/OVvhMrADFFEDh8DHDFRv/O9i3lPhsENjO7QX0+A==
dependencies:
cliui "^6.0.0"
decamelize "^1.2.0"
find-up "^4.1.0"
get-caller-file "^2.0.1"
require-directory "^2.1.1"
require-main-filename "^2.0.0"
set-blocking "^2.0.0"
string-width "^4.2.0"
which-module "^2.0.0"
y18n "^4.0.0"
yargs-parser "^18.1.2"
yargs@^16.0.3:
version "16.2.0"
resolved "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
dependencies:
cliui "^7.0.2"
escalade "^3.1.1"
get-caller-file "^2.0.5"
require-directory "^2.1.1"
string-width "^4.2.0"
y18n "^5.0.5"
yargs-parser "^20.2.2"
yargs@^17.6.0:
version "17.7.2"
resolved "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz#991df39aca675a192b816e1e0363f9d75d2aa269"
integrity sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==
dependencies:
cliui "^8.0.1"
escalade "^3.1.1"
get-caller-file "^2.0.5"
require-directory "^2.1.1"
string-width "^4.2.3"
y18n "^5.0.5"
yargs-parser "^21.1.1"
[email protected]:
version "0.1.2"
resolved "https://registry.npmjs.org/yeast/-/yeast-0.1.2.tgz#008e06d8094320c372dbc2f8ed76a0ca6c8ac419"
integrity sha512-8HFIh676uyGYP6wP13R/j6OJ/1HwJ46snpvzE7aHAN3Ryqh2yX6Xox2B4CUmTwwOIzlG3Bs7ocsP5dZH/R1Qbg==
yocto-queue@^0.1.0:
version "0.1.0"
resolved "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
yoga-layout-prebuilt@^1.9.6:
version "1.10.0"
resolved "https://registry.npmjs.org/yoga-layout-prebuilt/-/yoga-layout-prebuilt-1.10.0.tgz#2936fbaf4b3628ee0b3e3b1df44936d6c146faa6"
integrity sha512-YnOmtSbv4MTf7RGJMK0FvZ+KD8OEe/J5BNnR0GHhD8J/XcG/Qvxgszm0Un6FTHWW4uHlTgP0IztiXQnGyIR45g==
dependencies:
"@types/yoga-layout" "1.9.2"
[email protected]:
version "6.0.0-beta.6"
resolved "https://registry.npmjs.org/zeromq/-/zeromq-6.0.0-beta.6.tgz#2b8934cafce735ea9007fd3074ec8aca9bf11204"
integrity sha512-wLf6M7pBHijl+BRltUL2VoDpgbQcOZetiX8UzycHL8CcYFxYnRrpoG5fi3UX3+Umavz1lk4/dGaQez8qiDgr/Q==
dependencies:
node-gyp-build "^4.1.0"
[email protected]:
version "5.0.4"
resolved "https://registry.npmjs.org/zrender/-/zrender-5.0.4.tgz#89c355af908b9f64a301b38f751b7951f2c8a95a"
integrity sha512-DJpy0yrHYY5CuH6vhb9IINWbjvBUe/56J8aH86Jb7O8rRPAYZ3M2E469Qf5B3EOIfM3o3aUrO5edRQfLJ+l1Qw==
dependencies:
tslib "2.0.3"
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/.clangd
|
# https://clangd.llvm.org/config
# Apply a config conditionally to all C files
If:
PathMatch: .*\.(c|h)$
---
# Apply a config conditionally to all C++ files
If:
PathMatch: .*\.(c|h)pp
---
# Apply a config conditionally to all CUDA files
If:
PathMatch: .*\.cuh?
Diagnostics:
Suppress:
- "variadic_device_fn"
- "attributes_not_allowed"
CompileFlags:
Add:
- "-x"
- "cuda"
# No error on unknown CUDA versions
- "-Wno-unknown-cuda-version"
# Allow variadic CUDA functions
- "-Xclang=-fcuda-allow-variadic-functions"
---
# Tweak the clangd parse settings for all files
Hover:
ShowAKA: Yes
InlayHints:
Enabled: No
CompileFlags:
Add:
# report all errors
- "-ferror-limit=0"
- "-fmacro-backtrace-limit=0"
- "-ftemplate-backtrace-limit=0"
# Skip the CUDA version check
- "--no-cuda-version-check"
Remove:
# remove gcc's -fcoroutines
- -fcoroutines
# remove nvc++ flags unknown to clang
- "-gpu=*"
- "-stdpar*"
# remove nvcc flags unknown to clang
- "-arch*"
- "-gencode*"
- "--generate-code*"
- "-ccbin*"
- "-t=*"
- "--threads*"
- "-Xptxas*"
- "-Xcudafe*"
- "-Xfatbin*"
- "-Xcompiler*"
- "--diag-suppress*"
- "--diag_suppress*"
- "--compiler-options*"
- "--expt-extended-lambda"
- "--expt-relaxed-constexpr"
- "-forward-unknown-to-host-compiler"
- "-Werror=cross-execution-space-call"
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/docker-compose.runtime.yml
|
version: "3.9"
x-base-settings: &base_service_settings
tty: true
env_file: .env
network_mode: host
build: &base_build_settings
context: .
environment: &base_environment_settings
# Colorize the terminal in the container if possible
TERM: "${TERM:-}"
deploy:
resources:
reservations:
devices:
- capabilities:
- gpu
x-main-service-settings: &main_service_settings
<<: *base_service_settings
build: &main_build_settings
<<: *base_build_settings
args: &main_build_args
DEVEL_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
BUILD_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-packages
environment: &main_environment_settings
<<: *base_environment_settings
# Use the host's X11 display
DISPLAY: "${DISPLAY:-}"
XAUTHORITY: "${XAUTHORITY:-}"
XDG_SESSION_TYPE: "${XDG_SESSION_TYPE:-}"
services:
base:
<<: *base_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
build:
<<: *base_build_settings
dockerfile: dev/dockerfiles/runtime/base.Dockerfile
args:
UID: ${UID:-1000}
AMD64_BASE: nvidia/cuda:${CUDA_VERSION:-11.6.2}-base-${LINUX_VERSION:-ubuntu20.04}
ARM64_BASE: nvidia/cuda:${CUDA_VERSION:-11.6.2}-base-${LINUX_VERSION:-ubuntu20.04}
DEVEL_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
main:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/main.Dockerfile
args:
<<: *main_build_args
UCX_VERSION: ${UCX_VERSION:-1.12.1}
LINUX_VERSION: ${LINUX_VERSION:-ubuntu20.04}
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
demo:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-demo
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/demo.Dockerfile
args:
<<: *main_build_args
UCX_VERSION: ${UCX_VERSION:-1.12.1}
LINUX_VERSION: ${LINUX_VERSION:-ubuntu20.04}
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
glfw:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-glfw
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/glfw.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
cudf:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-cudf
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/cudf.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
sql:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-sql
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/sql.Dockerfile
args:
<<: *main_build_args
UCX_VERSION: ${UCX_VERSION:-1.12.1}
LINUX_VERSION: ${LINUX_VERSION:-ubuntu20.04}
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
cuml:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-cuml
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/cuml.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
cugraph:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-cugraph
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/cugraph.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
cuspatial:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-cuspatial
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/cuspatial.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-base
notebook:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-notebook
build:
<<: *main_build_settings
dockerfile: dev/dockerfiles/runtime/notebook.Dockerfile
args:
<<: *main_build_args
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-runtime-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-demo
volumes:
- "/etc/fonts:/etc/fonts:ro"
- "/tmp/.X11-unix:/tmp/.X11-unix:rw"
- "/usr/share/fonts:/usr/share/fonts:ro"
- "/usr/share/icons:/usr/share/icons:ro"
- "/etc/timezone:/etc/timezone:ro"
- "/etc/localtime:/etc/localtime:ro"
- "/run/dbus/system_bus_socket:/run/dbus/system_bus_socket"
- "${XDG_RUNTIME_DIR:-/run/user/$UID}:${XDG_RUNTIME_DIR:-/run/user/$UID}"
environment:
<<: *main_environment_settings
NVIDIA_DRIVER_CAPABILITIES: all
# Use the host's X11 display
XDG_RUNTIME_DIR: "${XDG_RUNTIME_DIR:-/run/user/$UID}"
DBUS_SESSION_BUS_ADDRESS: "${DBUS_SESSION_BUS_ADDRESS:-unix:path=/run/user/$UID/bus}"
cap_add:
- SYS_ADMIN
security_opt:
- apparmor=unconfined
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/.yarnrc
|
fund false
audit false
optional false
save-prefix ""
save-exact true
strict-semver true
ignore-engines true
ignore-optional true
update-notifier false
no-node-version-check true
disable-self-update-check true
scripts-prepend-node-path true
registry "https://registry.npmjs.org/"
--add.silent true
--run.silent true
--add.strict-semver true
--install.strict-semver true
--add.ignore-engines true
--install.ignore-engines true
--add.ignore-optional true
--install.ignore-optional true
--add.scripts-prepend-node-path true
--install.scripts-prepend-node-path true
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/lerna.json
|
{
"version": "22.12.2",
"npmClient": "yarn",
"skipGit": true,
"useWorkspaces": true,
"packages": [
"modules/*",
"modules/demo/graph",
"modules/demo/umap",
"modules/demo/luma",
"modules/demo/spatial",
"modules/demo/xterm",
"modules/demo/deck/*",
"modules/demo/ipc/*",
"modules/demo/ssr/*",
"modules/demo/tfjs/*",
"modules/demo/client-server",
"modules/demo/api-server",
"modules/demo/viz-app",
"modules/demo/sql/*"
]
}
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/BINDINGS.md
|
# RAPIDS Bindings
The tables below show the bindings that have been implemented in `node-rapids`:
- [cuDF](#cuDF)
- [cuGraph](#cuGraph)
- [cuSpatial](#cuSpatial)
- [cuML](#cuML)
## cuDF
### Series
#### Numeric Series
| cuDF | node-rapids |
| -------------------- | :-------------------: |
| `abs` | ✅ |
| `acos` | ✅ |
| `add` | ✅ |
| `all` | ✅ |
| `any` | ✅ |
| `append` | ✅ (`concat`) |
| `applymap` | |
| `argsort` | |
| `as_index` | |
| `as_mask` | |
| `asin` | ✅ |
| `astype` | ✅ (`cast`) |
| `atan` | ✅ |
| `ceil` | ✅ |
| `clip` | |
| `copy` | ✅ |
| `corr` | |
| `cos` | ✅ |
| `count` | ✅ (`countNonNulls`) |
| `cov` | |
| `cummax` | ✅ (`cumulativeMax`) |
| `cummin` | ✅ (`cumulativeMin`) |
| `cumprod` | ✅ (`cumulativeProduct`) |
| `cumsum` | ✅ (`cumulativeSum`) |
| `describe` | |
| `diff` | |
| `digitize` | |
| `drop_duplicates` | ✅ (`dropDuplicates`) |
| `dropna` | ✅ (`dropNulls`) |
| `eq` | ✅ |
| `equals` | |
| `exp` | ✅ |
| `factorize` | |
| `fillna` | ✅ (`replaceNulls`) |
| `floor` | ✅ |
| `floordiv` | ✅ |
| `from_arrow` | ✅ |
| `from_categorical` | |
| `from_masked_array` | |
| `from_pandas` | |
| `ge` | ✅ |
| `groupby` | |
| `gt` | ✅ |
| `hash_encode` | |
| `hash_values` | |
| `head` | ✅ |
| `interleave_columns` | |
| `isin` | |
| `isna` | ✅ (`isNull`) |
| `isnull` | ✅ |
| `keys` | |
| `kurt` | ✅ (`kurtosis`) |
| `kurtosis` | ✅ |
| `label_encoding` | |
| `le` | ✅ |
| `log` | ✅ |
| `lt` | ✅ |
| `map` | |
| `mask` | |
| `max` | ✅ |
| `mean` | ✅ |
| `median` | ✅ |
| `memory_usage` | |
| `min` | ✅ |
| `mod` | ✅ |
| `mode` | |
| `mul` | ✅ |
| `nans_to_nulls` | ✅ (`nansToNulls`) |
| `ne` | ✅ |
| `nlargest` | ✅ (`nLargest`) |
| `notna` | ✅ (`isNotNull`) |
| `notnull` | ✅ (`isNotNull`) |
| `nsmallest` | ✅ (`nSmallest`) |
| `nunique` | ✅ |
| `one_hot_encoding` | |
| `pipe` | |
| `pow` | ✅ |
| `prod` | |
| `product` | ✅ |
| `quantile` | ✅ |
| `radd` | |
| `rank` | |
| `reindex` | |
| `rename` | |
| `repeat` | |
| `replace` | |
| `reset_index` | |
| `reverse` | ✅ |
| `rfloordiv` | |
| `rmod` | |
| `rmul` | |
| `rolling` | |
| `round` | |
| `rpow` | |
| `rsub` | |
| `rtruediv` | |
| `sample` | |
| `scale` | |
| `scatter_by_map` | |
| `searchsorted` | |
| `set_index` | |
| `set_mask` | |
| `shift` | |
| `sin` | ✅ |
| `skew` | ✅ |
| `sort_index` | |
| `sort_values` | ✅ (`sortValues`) |
| `sqrt` | ✅ |
| `std` | ✅ |
| `sub` | ✅ |
| `sum` | ✅ |
| `tail` | ✅ |
| `take` | ✅ (`gather`) |
| `tan` | ✅ |
| `tile` | |
| `to_array` | |
| `to_arrow` | |
| `to_dlpack` | |
| `to_frame` | |
| `to_gpu_array` | |
| `to_hdf` | |
| `to_json` | |
| `to_pandas` | |
| `to_string` | |
| `truediv` | ✅ |
| `unique` | ✅ |
| `value_counts` | ✅ |
| `values_to_string` | |
| `var` | ✅ |
| `where` | |
#### List, String, Struct Series
| cuDF | List Series | String Series | Struct Series |
| -------------------- | :-------------------: | :-------------------: | :-------------------: |
| `add` | | | |
| `all` | ✅ | ✅ | ✅ |
| `any` | ✅ | ✅ | ✅ |
| `append` | ✅ (`concat`) | ✅ (`concat`) | ✅ (`concat`) |
| `applymap` | | | |
| `argsort` | | | |
| `as_index` | | | |
| `as_mask` | | | |
| `astype` | ✅ (`cast`) | ✅ (`cast`) | ✅ (`cast`) |
| `byte_count` | N/A | ✅ (`byteCount`) | N/A |
| `clip` | | | |
| `copy` | ✅ | ✅ | ✅ |
| `count` | ✅ (`countNonNulls`) | ✅ (`countNonNulls`) | ✅ (`countNonNulls`) |
| `describe` | | | |
| `diff` | | | |
| `digitize` | | | |
| `drop_duplicates` | ✅ (`dropDuplicates`) | ✅ (`dropDuplicates`) | ✅ (`dropDuplicates`) |
| `dropna` | ✅ (`dropNulls`) | ✅ (`dropNulls`) | ✅ (`dropNulls`) |
| `equals` | | | |
| `factorize` | | | |
| `fillna` | ✅ (`replaceNulls`) | ✅ (`replaceNulls`) | ✅ (`replaceNulls`) |
| `from_arrow` | | | |
| `from_categorical` | | | |
| `from_masked_array` | | | |
| `from_pandas` | | | |
| `get_json_object` | | ✅ (`getJSONObject`) | |
| `groupby` | | | |
| `hash_encode` | | | |
| `hash_values` | | | |
| `head` | ✅ | ✅ | ✅ |
| `interleave_columns` | | | |
| `isin` | | | |
| `isna` | ✅ (`isNull`) | ✅ (`isNull`) | ✅ (`isNull`) |
| `isnull` | ✅ | ✅ | ✅ |
| `keys` | | | |
| `kurt` | ✅ (`kurtosis`) | ✅ (`kurtosis`) | ✅ (`kurtosis`) |
| `kurtosis` | ✅ | ✅ | ✅ |
| `label_encoding` | | | |
| `len` | | ✅ | N/A |
| `map` | | | |
| `mask` | | | |
| `memory_usage` | | | |
| `nans_to_nulls` | ✅ (`nansToNulls`) | ✅ (`nansToNulls`) | ✅ (`nansToNulls`) |
| `notna` | ✅ (`isNotNull`) | ✅ (`isNotNull`) | ✅ (`isNotNull`) |
| `notnull` | ✅ (`isNotNull`) | ✅ (`isNotNull`) | ✅ (`isNotNull`) |
| `nunique` | | | |
| `one_hot_encoding` | | | |
| `pad` | N/A | ✅ | N/A |
| `pipe` | | | |
| `quantile` | | | |
| `rank` | | | |
| `reindex` | | | |
| `rename` | | | |
| `repeat` | | | |
| `replace` | | | |
| `reset_index` | | | |
| `reverse` | ✅ | ✅ | ✅ |
| `rolling` | | | |
| `sample` | | | |
| `scatter_by_map` | | | |
| `searchsorted` | | | |
| `set_index` | | | |
| `set_mask` | | | |
| `shift` | | | |
| `skew` | ✅ | ✅ | ✅ |
| `sort_index` | | | |
| `sort_values` | ✅ (`sortValues`) | ✅ (`sortValues`) | ✅ (`sortValues`) |
| `tail` | ✅ | ✅ | ✅ |
| `take` | ✅ (`gather`) | ✅ (`gather`) | ✅ (`gather`) |
| `tile` | | | |
| `to_array` | | | |
| `to_arrow` | | | |
| `to_dlpack` | | | |
| `to_frame` | | | |
| `to_gpu_array` | | | |
| `to_hdf` | | | |
| `to_json` | | | |
| `to_pandas` | | | |
| `to_string` | | | |
| `unique` | ✅ | ✅ | ✅ |
| `value_counts` | ✅ | ✅ | ✅ |
| `values_to_string` | | | |
| `where` | | | |
| `zfill` | N/A | ✅ | N/A |
### DataFrame
| cuDF | node-rapids |
| -------------------- | :-------------------: |
| `acos` | ✅ |
| `add` | |
| `agg` | |
| `all` | |
| `any` | |
| `append` | ✅ (`concat`) |
| `apply_chunks` | |
| `apply_rows` | |
| `argsort` | |
| `as_gpu_matrix` | |
| `as_matrix` | |
| `asin` | ✅ |
| `assign` | ✅ |
| `astype` | ✅ (`cast`) |
| `atan` | ✅ |
| `clip` | |
| `copy` | |
| `corr` | |
| `cos` | ✅ |
| `count` | |
| `cov` | |
| `cummax` | |
| `cummin` | |
| `cumprod` | |
| `cumsum` | |
| `describe` | |
| `div` | |
| `drop` | ✅ |
| `drop_duplicates` | ✅ (`dropDuplicates`) |
| `dropna` | ✅ (`dropNulls`) |
| `equals` | |
| `exp` | ✅ |
| `fillna` | ✅ (`replaceNulls`) |
| `floordiv` | |
| `from_arrow` | |
| `from_pandas` | |
| `from_records` | |
| `hash_columns` | |
| `head` | ✅ |
| `info` | |
| `insert` | |
| `interleave_columns` | ✅ |
| `isin` | |
| `isna` | ✅ (`isNaN`) |
| `isnull` | ✅ |
| `iteritems` | |
| `join` | |
| `keys` | |
| `kurt` | ✅ (`kurtosis`) |
| `kurtosis` | ✅ |
| `label_encoding` | |
| `log` | ✅ |
| `mask` | |
| `max` | |
| `mean` | |
| `melt` | |
| `memory_usage` | |
| `merge` | |
| `min` | |
| `mod` | |
| `mode` | |
| `mul` | |
| `nans_to_nulls` | ✅ (`nansToNulls`) |
| `nlargest` | |
| `notna` | ✅ (`isNotNaN`) |
| `notnull` | ✅ (`isNotNull`) |
| `nsmallest` | |
| `one_hot_encoding` | |
| `partition_by_hash` | |
| `pipe` | |
| `pivot` | |
| `pop` | |
| `pow` | |
| `prod` | |
| `product` | |
| `quantile` | |
| `quantiles` | |
| `query` | |
| `radd` | |
| `rank` | |
| `rdiv` | |
| `reindex` | |
| `rename` | |
| `repeat` | |
| `replace` | |
| `reset_index` | |
| `rfloordiv` | |
| `rmod` | |
| `rmul` | |
| `round` | |
| `rpow` | |
| `rsub` | |
| `rtruediv` | |
| `sample` | |
| `scatter_by_map` | |
| `searchsorted` | |
| `select_dtypes` | |
| `set_index` | |
| `shift` | |
| `sin` | ✅ |
| `skew` | ✅ |
| `sort_index` | |
| `sort_values` | ✅ (`sortValues`) |
| `sqrt` | ✅ |
| `stack` | |
| `std` | |
| `sub` | |
| `sum` | |
| `tail` | ✅ |
| `take` | ✅ (`gather`) |
| `tan` | ✅ |
| `tile` | |
| `to_arrow` | ✅ (`toArrow`) |
| `to_csv` | ✅ (`toCSV`) |
| `to_dlpack` | |
| `to_feather` | |
| `to_hdf` | |
| `to_json` | |
| `to_orc` | ✅ (`toORC`) |
| `to_pandas` | |
| `to_parquet` | ✅ (`toParquet`) |
| `to_records` | |
| `to_string` | |
| `transpose` | |
| `truediv` | |
| `unstack` | |
| `update` | |
| `var` | |
| `where` | |
### GroupBy
| cuDF | node-rapids |
| --------------- | :---------: |
| `agg` | |
| `aggregate` | |
| `apply` | |
| `apply_grouped` | |
| `nth` | ✅ |
| `pipe` | |
| `rolling` | |
| `size` | |
## cuGraph
| cuGraph | node-rapids |
| ---------------------------- | :-------------: |
| `add_internal_vertex_id` | |
| `add_nodes_from` | |
| `clear` | |
| `compute_renumber_edge_list` | |
| `degree` | |
| `degrees` | |
| `delete_adj_list` | |
| `delete_edge_list` | |
| `edges` | |
| `from_cudf_adjlist` | |
| `from_cudf_edgelist` | |
| `from_dask_cudf_edgelist` | |
| `from_numpy_array` | |
| `from_numpy_matrix` | |
| `from_pandas_adjacency` | |
| `from_pandas_edgelist` | |
| `get_two_hop_neighbors` | |
| `has_edge` | |
| `has_node` | |
| `in_degree` | |
| `is_bipartite` | |
| `is_multigraph` | |
| `is_multipartite` | |
| `lookup_internal_vertex_id` | |
| `nodes` | |
| `number_of_edges` | ✅ (`numEdges`) |
| `number_of_nodes` | ✅ (`numNodes`) |
| `number_of_vertices` | |
| `out_degree` | |
| `sets` | |
| `to_directed` | |
| `to_numpy_array` | |
| `to_numpy_matrix` | |
| `to_pandas_adjacency` | |
| `to_pandas_edgelist` | |
| `to_undirected` | |
| `unrenumber` | |
| `view_adj_list` | |
| `view_edge_list` | |
| `view_transposed_adj_list` | |
## cuSpatial
| cuSpatial | node-rapids |
| ------------------------------------ | :--------------------------------------------: |
| `directed_hausdorff_distance` | |
| `haversine_distance` | |
| `lonlat_to_cartesian` | |
| `point_in_polygon` | |
| `polygon_bounding_boxes` | ✅ (`computePolygonBoundingBoxes`) |
| `polyline_bounding_boxes` | ✅ (`computePolylineBoundingBoxes`) |
| `quadtree_on_points` | ✅ (`createQuadtree`) |
| `points_in_spatial_window` | |
| `join_quadtree_and_bounding_boxes` | ✅ (`findQuadtreeAndBoundingBoxIntersections`) |
| `quadtree_point_in_polygon` | ✅ (`findPointsInPolygons`) |
| `quadtree_point_to_nearest_polyline` | ✅ (`findPolylineNearestToEachPoint`) |
| `derive_trajectories` | |
| `trajectory_bounding_boxes` | |
| `trajectory_distances_and_speeds` | |
| `read_polygon_shapefile` | |
## cuML
#### Clustering
| cuML | node-rapids |
| ------------------------------------ | :--------------------------------------------: |
| `AC` | |
| `DBSCAN` | |
| `K-Means` | |
#### Dimensionality Reduction and Manifold Learning
| cuML | node-rapids |
| ------------------------------------ | :--------------------------------------------: |
| `K-Means` | |
| `PCA` | |
| `Random Projection` | |
| `tSVD` | |
| `TSNE` | |
#### Dimensionality Reduction and Manifold Learning - UMAP
| cuML | node-rapids |
| ------------------------------------ | :--------------------------------------------: |
| `fit` | ✅ (`fitSeries`, `fitDataFrame`,`fitArray`) |
| `transform` | ✅ (`transformSeries`, `transformDataFrame`, |
| | `transformArray`) |
| `refine` | ✅ (`refineSeries`, `refineDataFrame`, |
| | `refineformArray`) |
#### Linear / Nonlinear Models for Regression or Classification
| cuML | node-rapids |
| ------------------------------------ | :--------------------------------------------: |
| `ElasticNet Regression` | |
| `KNN` | |
| `LARS Regression` | |
| `Linear Regression (OLS)` | |
| `Linear Regression Lasso` | |
| `Linear Regression Ridge` | |
| `Logistic Regression` | |
| `Naive Bayes` | |
| `SGD` | |
| `CD` | |
| `QN` | |
| `Random Forest` | |
| `SVC` | |
| `SVR` | |
#### Misc
| cuML | node-rapids |
| ------------------------------------------ | :--------------------------------------------: |
| `Preprocessing Misc` | |
| `Holt-Winters Exponential Smoothing` | |
| `Auto-regressive Integrated Moving Average`| |
| `SHAP Kernel Explainer` | |
| `SHAP Permutation Explainer` | |
| `trustworthiness_score` | ✅ (`trustworthinessSeries`, |
| | `trustworthinessDataFrame`, `trustworthiness`) |
#### EOF
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/.creds.sample
|
# Optional AWS access id for writing to the shared sccache S3 bucket
AWS_ACCESS_KEY_ID=<AWS Access Key Id>
# Optional AWS secret key for writing to the shared sccache S3 bucket
AWS_SECRET_ACCESS_KEY=<AWS Secret Key>
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/docker-compose.devel.yml
|
version: "3.9"
x-base-settings: &base_service_settings
tty: true
env_file:
- .env
- .creds
network_mode: host
build: &base_build_settings
context: .
x-bake:
platforms:
- linux/arm64
output: type=docker
environment: &base_environment_settings
# Colorize the terminal in the container if possible
TERM: "${TERM:-}"
NVIDIA_DRIVER_CAPABILITIES: all
deploy:
resources:
reservations:
devices:
- capabilities:
- gpu
x-main-service-settings: &main_service_settings
<<: *base_service_settings
environment: &main_environment_settings
<<: *base_environment_settings
# Use the host's X11 display
DISPLAY: "${DISPLAY:-}"
XAUTHORITY: "${XAUTHORITY:-}"
XDG_SESSION_TYPE: "${XDG_SESSION_TYPE:-}"
XDG_RUNTIME_DIR: "${XDG_RUNTIME_DIR:-/run/user/$UID}"
LIBCUDF_KERNEL_CACHE_PATH: "/opt/rapids/node/.cache/jit"
RAPIDS_VERSION: "${npm_package_config_rapids_version:-22.12.20}"
DBUS_SESSION_BUS_ADDRESS: "${DBUS_SESSION_BUS_ADDRESS:-unix:path=/run/user/$UID/bus}"
cap_add:
- SYS_ADMIN
- SYS_PTRACE
security_opt:
- apparmor=unconfined
services:
main:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
secrets:
- sccache_credentials
build:
<<: *base_build_settings
dockerfile: dev/dockerfiles/devel/main.Dockerfile
args:
UCX_VERSION: ${UCX_VERSION:-1.12.1}
NODE_VERSION: ${NODE_VERSION:-16.15.1}
LINUX_VERSION: ${LINUX_VERSION:-ubuntu20.04}
AMD64_BASE: nvidia/cuda:${CUDA_VERSION:-11.6.2}-devel-${LINUX_VERSION:-ubuntu20.04}
ARM64_BASE: nvidia/cuda:${CUDA_VERSION:-11.6.2}-devel-${LINUX_VERSION:-ubuntu20.04}
environment:
<<: *main_environment_settings
SSH_AUTH_SOCK: "/ssh-agent"
GCC_COLORS: "${GCC_COLORS:-error=01;31:warning=01;35:note=01;36:caret=01;32:locus=01:quote=01}"
volumes:
- ".:/opt/rapids/node:rw"
- "/etc/fonts:/etc/fonts:ro"
- "/etc/timezone:/etc/timezone"
- "/etc/localtime:/etc/localtime"
- "/tmp/.X11-unix:/tmp/.X11-unix:rw"
- "/usr/share/fonts:/usr/share/fonts:ro"
- "/usr/share/icons:/usr/share/icons:ro"
- "/run/dbus/system_bus_socket:/run/dbus/system_bus_socket"
- "${SSH_AUTH_SOCK:-/run/user/$UID/keyring/ssh}:/ssh-agent"
- "${XDG_RUNTIME_DIR:-/run/user/$UID}:${XDG_RUNTIME_DIR:-/run/user/$UID}"
notebook:
<<: *main_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-notebook
build:
<<: *base_build_settings
dockerfile: dev/dockerfiles/devel/notebook.Dockerfile
args:
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
environment:
<<: *main_environment_settings
QT_AUTO_SCREEN_SCALE_FACTOR: 0
volumes:
- ".:/opt/rapids/node:rw"
- "/etc/fonts:/etc/fonts:ro"
- "/etc/timezone:/etc/timezone"
- "/etc/localtime:/etc/localtime"
- "/tmp/.X11-unix:/tmp/.X11-unix:rw"
- "/usr/share/fonts:/usr/share/fonts:ro"
- "/usr/share/icons:/usr/share/icons:ro"
- "/run/dbus/system_bus_socket:/run/dbus/system_bus_socket"
- "${XDG_RUNTIME_DIR:-/run/user/$UID}:${XDG_RUNTIME_DIR:-/run/user/$UID}"
packages:
<<: *base_service_settings
image: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-packages
build:
<<: *base_build_settings
dockerfile: dev/dockerfiles/devel/package.Dockerfile
ssh:
- default
secrets:
- sccache_credentials
args:
CUDAARCHS: "${CUDAARCHS:-ALL}"
PARALLEL_LEVEL: "${PARALLEL_LEVEL:-4}"
NVCC_APPEND_FLAGS: "${NVCC_APPEND_FLAGS:-}"
SCCACHE_REGION: "${SCCACHE_REGION:-us-west-2}"
SCCACHE_BUCKET: "${SCCACHE_BUCKET:-node-rapids-sccache}"
SCCACHE_IDLE_TIMEOUT: "${SCCACHE_IDLE_TIMEOUT:-32768}"
RAPIDS_VERSION: "${npm_package_config_rapids_version:-22.12.20}"
FROM_IMAGE: ${REPOSITORY:-ghcr.io/rapidsai/node}:${npm_package_version:-22.12.2}-devel-node${NODE_VERSION:-16.15.1}-cuda${CUDA_VERSION_MAJOR:-11}-${LINUX_VERSION:-ubuntu20.04}-main
secrets:
sccache_credentials:
file: .creds
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/README.md
|
# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> node-rapids</div>
[`node-rapids`](https://github.com/rapidsai/node) is a collection of Node.js native addons for the [NVIDIA RAPIDS](https://rapids.ai/) suite of GPU-accelerated data-science and ETL libraries on Linux and WSL2.
* [`@rapidsai/rmm`](https://github.com/rapidsai/node/tree/main/modules/rmm) - [RAPIDS Memory Manager](https://github.com/rapidsai/rmm)
* [`@rapidsai/cudf`](https://github.com/rapidsai/node/tree/main/modules/cudf) - [RAPIDS GPU DataFrame](https://github.com/rapidsai/cudf)
* [`@rapidsai/cuml`](https://github.com/rapidsai/node/tree/main/modules/cuml) - [RAPIDS Machine Learning Library](https://github.com/rapidsai/cuml)
* [`@rapidsai/cugraph`](https://github.com/rapidsai/node/tree/main/modules/cugraph) - [RAPIDS Graph Analytics Library](https://github.com/rapidsai/cugraph)
* [`@rapidsai/cuspatial`](https://github.com/rapidsai/node/tree/main/modules/cuspatial) - [RAPIDS Accelerated GIS Library](https://github.com/rapidsai/cuspatial)
* [`@rapidsai/sql`](https://github.com/rapidsai/node/tree/main/modules/sql) - Multi-node/multi-GPU accelerated SQL execution engine
`node-rapids` includes limited bindings to other necessary native APIs:
* [`@rapidsai/cuda`](https://github.com/rapidsai/node/tree/main/modules/cuda) - Interact with GPUs via the [CUDA Runtime APIs](https://developer.nvidia.com/cuda-toolkit)
* [`@rapidsai/glfw`](https://github.com/rapidsai/node/tree/main/modules/glfw) - Create platform-agnostic native windows with OpenGL contexts via [GLFW](https://github.com/glfw/glfw)
* [`@rapidsai/webgl`](https://github.com/rapidsai/node/tree/main/modules/webgl) - Provides a [`WebGL2RenderingContext`](https://developer.mozilla.org/en-US/docs/Web/API/WebGL2RenderingContext) via [OpenGL ES](https://www.khronos.org/opengles)
`node-rapids` uses the ABI-stable [`N-API`](https://nodejs.org/api/n-api.html) via [`node-addon-api`](https://github.com/nodejs/node-addon-api), so the libraries work in node and Electron without recompiling.
See the [API docs](https://rapidsai.github.io/node/) for detailed information about each module.
## Getting started
Due to native dependency distribution complexity, pre-packaged builds of the `node-rapids` modules are presently only available via our [public docker images](https://github.com/orgs/rapidsai/packages/container/package/node). See [USAGE.md](https://github.com/rapidsai/node/tree/main/USAGE.md) for more details.
## Getting involved
See [DEVELOP.md](https://github.com/rapidsai/node/blob/main/DEVELOP.md) for details on setting up a local dev environment and building the code.
We want your input! Join us in the [#node-rapids channel](https://rapids-goai.slack.com/archives/C0237JMVBRS) in the [RAPIDS-GoAI Slack workspace](https://rapids-goai.slack.com).
## Tracking Progress
You can review [BINDINGS.md](https://github.com/rapidsai/node/blob/main/BINDINGS.md) to see which bindings have been completed for each of the RAPIDS libraries.
## Demos
Check out our [demos](https://github.com/rapidsai/node/tree/main/modules/demo) to see various visualization and compute capabilities:
* [Library of deck.gl demos in OpenGL](https://github.com/rapidsai/node/tree/main/modules/demo/deck/)
* [Cross filtering millions of rows with cuDF](https://github.com/rapidsai/node/tree/main/modules/demo/client-server)
* [Simulating & rendering with cuGraph](https://github.com/rapidsai/node/tree/main/modules/demo/graph/)
* [Querying millions of points with cuSpatial](https://github.com/rapidsai/node/tree/main/modules/demo/spatial/)
* [Multi-GPU SQL queries on GBs of CSVs](https://github.com/rapidsai/node/tree/main/modules/demo/sql/sql-cluster-server/)
Check out our [Jupyter Lab Notebook Demos](https://github.com/rapidsai/node/tree/main/modules/cudf/notebooks) to see how to use Node.js for GPU accelerated data science.
## License
This work is licensed under the [Apache-2.0 license](https://github.com/rapidsai/node/tree/main/LICENSE).
---
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/tsconfig.json
|
{
"include": [
"modules/**/*/src/**/*.ts",
"modules/**/*/test/**/*.ts",
],
"exclude": ["node_modules", "build/js"],
"compilerOptions": {
"baseUrl": "./",
"paths": {
"@rapidsai/core": ["modules/core/src/index"],
"@rapidsai/core/*": ["modules/core/src/*"],
"@rapidsai/cuda": ["modules/cuda/src/index"],
"@rapidsai/cuda/*": ["modules/cuda/src/*"],
"@rapidsai/rmm": ["modules/rmm/src/index"],
"@rapidsai/rmm/*": ["modules/rmm/src/*"],
"@rapidsai/cudf": ["modules/cudf/src/index"],
"@rapidsai/cudf/*": ["modules/cudf/src/*"],
"@rapidsai/cugraph": ["modules/cugraph/src/index"],
"@rapidsai/cugraph/*": ["modules/cugraph/src/*"],
"@rapidsai/cuspatial": ["modules/cuspatial/src/index"],
"@rapidsai/cuspatial/*": ["modules/cuspatial/src/*"],
"@rapidsai/deck.gl": ["modules/deck.gl/src/index"],
"@rapidsai/deck.gl/*": ["modules/deck.gl/src/*"],
"@rapidsai/jsdom": ["modules/jsdom/src/index"],
"@rapidsai/jsdom/*": ["modules/jsdom/src/*"],
"@rapidsai/glfw": ["modules/glfw/src/index"],
"@rapidsai/glfw/*": ["modules/glfw/src/*"],
"@rapidsai/io": ["modules/io/src/index"],
"@rapidsai/io/*": ["modules/io/src/*"],
"@rapidsai/webgl": ["modules/webgl/src/index"],
"@rapidsai/webgl/*": ["modules/webgl/src/*"],
"@rapidsai/sql": ["modules/sql/src/index"],
"@rapidsai/sql/*": ["modules/sql/src/*"],
"deck.gl": ["node_modules/deck.gl/src/index"],
"deck.gl/dist/es5/*": ["node_modules/deck.gl/src/*"],
"deck.gl/dist/es6/*": ["node_modules/deck.gl/src/*"],
"deck.gl/dist/esm/*": ["node_modules/deck.gl/src/*"],
"@deck.gl/arcgis": ["node_modules/@deck.gl/arcgis/src/index"],
"@deck.gl/arcgis/dist/es5/*": ["node_modules/@deck.gl/arcgis/src/*"],
"@deck.gl/arcgis/dist/es6/*": ["node_modules/@deck.gl/arcgis/src/*"],
"@deck.gl/arcgis/dist/esm/*": ["node_modules/@deck.gl/arcgis/src/*"],
"@deck.gl/aggregation-layers": ["node_modules/@deck.gl/aggregation-layers/src/index"],
"@deck.gl/aggregation-layers/dist/es5/*": ["node_modules/@deck.gl/aggregation-layers/src/*"],
"@deck.gl/aggregation-layers/dist/es6/*": ["node_modules/@deck.gl/aggregation-layers/src/*"],
"@deck.gl/aggregation-layers/dist/esm/*": ["node_modules/@deck.gl/aggregation-layers/src/*"],
"@deck.gl/core": ["node_modules/@deck.gl/core/src/index"],
"@deck.gl/core/dist/es5/*": ["node_modules/@deck.gl/core/src/*"],
"@deck.gl/core/dist/es6/*": ["node_modules/@deck.gl/core/src/*"],
"@deck.gl/core/dist/esm/*": ["node_modules/@deck.gl/core/src/*"],
"@deck.gl/extensions": ["node_modules/@deck.gl/extensions/src/index"],
"@deck.gl/extensions/dist/es5/*": ["node_modules/@deck.gl/extensions/src/*"],
"@deck.gl/extensions/dist/es6/*": ["node_modules/@deck.gl/extensions/src/*"],
"@deck.gl/extensions/dist/esm/*": ["node_modules/@deck.gl/extensions/src/*"],
"@deck.gl/geo-layers": ["node_modules/@deck.gl/geo-layers/src/index"],
"@deck.gl/geo-layers/dist/es5/*": ["node_modules/@deck.gl/geo-layers/src/*"],
"@deck.gl/geo-layers/dist/es6/*": ["node_modules/@deck.gl/geo-layers/src/*"],
"@deck.gl/geo-layers/dist/esm/*": ["node_modules/@deck.gl/geo-layers/src/*"],
"@deck.gl/google-maps": ["node_modules/@deck.gl/google-maps/src/index"],
"@deck.gl/google-maps/dist/es5/*": ["node_modules/@deck.gl/google-maps/src/*"],
"@deck.gl/google-maps/dist/es6/*": ["node_modules/@deck.gl/google-maps/src/*"],
"@deck.gl/google-maps/dist/esm/*": ["node_modules/@deck.gl/google-maps/src/*"],
"@deck.gl/json": ["node_modules/@deck.gl/json/src/index"],
"@deck.gl/json/dist/es5/*": ["node_modules/@deck.gl/json/src/*"],
"@deck.gl/json/dist/es6/*": ["node_modules/@deck.gl/json/src/*"],
"@deck.gl/json/dist/esm/*": ["node_modules/@deck.gl/json/src/*"],
"@deck.gl/jupyter-widget": ["node_modules/@deck.gl/jupyter-widget/src/index"],
"@deck.gl/jupyter-widget/dist/es5/*": ["node_modules/@deck.gl/jupyter-widget/src/*"],
"@deck.gl/jupyter-widget/dist/es6/*": ["node_modules/@deck.gl/jupyter-widget/src/*"],
"@deck.gl/jupyter-widget/dist/esm/*": ["node_modules/@deck.gl/jupyter-widget/src/*"],
"@deck.gl/layers": ["node_modules/@deck.gl/layers/src/index"],
"@deck.gl/layers/dist/es5/*": ["node_modules/@deck.gl/layers/src/*"],
"@deck.gl/layers/dist/es6/*": ["node_modules/@deck.gl/layers/src/*"],
"@deck.gl/layers/dist/esm/*": ["node_modules/@deck.gl/layers/src/*"],
"@deck.gl/mapbox": ["node_modules/@deck.gl/mapbox/src/index"],
"@deck.gl/mapbox/dist/es5/*": ["node_modules/@deck.gl/mapbox/src/*"],
"@deck.gl/mapbox/dist/es6/*": ["node_modules/@deck.gl/mapbox/src/*"],
"@deck.gl/mapbox/dist/esm/*": ["node_modules/@deck.gl/mapbox/src/*"],
"@deck.gl/mesh-layers": ["node_modules/@deck.gl/mesh-layers/src/index"],
"@deck.gl/mesh-layers/dist/es5/*": ["node_modules/@deck.gl/mesh-layers/src/*"],
"@deck.gl/mesh-layers/dist/es6/*": ["node_modules/@deck.gl/mesh-layers/src/*"],
"@deck.gl/mesh-layers/dist/esm/*": ["node_modules/@deck.gl/mesh-layers/src/*"],
"@deck.gl/react": ["node_modules/@deck.gl/react/src/index"],
"@deck.gl/react/dist/es5/*": ["node_modules/@deck.gl/react/src/*"],
"@deck.gl/react/dist/es6/*": ["node_modules/@deck.gl/react/src/*"],
"@deck.gl/react/dist/esm/*": ["node_modules/@deck.gl/react/src/*"],
"@deck.gl/test-utils": ["node_modules/@deck.gl/test-utils/src/index"],
"@deck.gl/test-utils/dist/es5/*": ["node_modules/@deck.gl/test-utils/src/*"],
"@deck.gl/test-utils/dist/es6/*": ["node_modules/@deck.gl/test-utils/src/*"],
"@deck.gl/test-utils/dist/esm/*": ["node_modules/@deck.gl/test-utils/src/*"],
"@luma.gl/constants": ["node_modules/@luma.gl/constants/src/index"],
"@luma.gl/constants/dist/es5/*": ["node_modules/@luma.gl/constants/src/*"],
"@luma.gl/constants/dist/es6/*": ["node_modules/@luma.gl/constants/src/*"],
"@luma.gl/constants/dist/esm/*": ["node_modules/@luma.gl/constants/src/*"],
"@luma.gl/core": ["node_modules/@luma.gl/core/src/index"],
"@luma.gl/core/dist/es5/*": ["node_modules/@luma.gl/core/src/*"],
"@luma.gl/core/dist/es6/*": ["node_modules/@luma.gl/core/src/*"],
"@luma.gl/core/dist/esm/*": ["node_modules/@luma.gl/core/src/*"],
"@luma.gl/debug": ["node_modules/@luma.gl/debug/src/index"],
"@luma.gl/debug/dist/es5/*": ["node_modules/@luma.gl/debug/src/*"],
"@luma.gl/debug/dist/es6/*": ["node_modules/@luma.gl/debug/src/*"],
"@luma.gl/debug/dist/esm/*": ["node_modules/@luma.gl/debug/src/*"],
"@luma.gl/engine": ["node_modules/@luma.gl/engine/src/index"],
"@luma.gl/engine/dist/es5/*": ["node_modules/@luma.gl/engine/src/*"],
"@luma.gl/engine/dist/es6/*": ["node_modules/@luma.gl/engine/src/*"],
"@luma.gl/engine/dist/esm/*": ["node_modules/@luma.gl/engine/src/*"],
"@luma.gl/experimental": ["node_modules/@luma.gl/experimental/src/index"],
"@luma.gl/experimental/dist/es5/*": ["node_modules/@luma.gl/experimental/src/*"],
"@luma.gl/experimental/dist/es6/*": ["node_modules/@luma.gl/experimental/src/*"],
"@luma.gl/experimental/dist/esm/*": ["node_modules/@luma.gl/experimental/src/*"],
"@luma.gl/gltools": ["node_modules/@luma.gl/gltools/src/index"],
"@luma.gl/gltools/dist/es5/*": ["node_modules/@luma.gl/gltools/src/*"],
"@luma.gl/gltools/dist/es6/*": ["node_modules/@luma.gl/gltools/src/*"],
"@luma.gl/gltools/dist/esm/*": ["node_modules/@luma.gl/gltools/src/*"],
"@luma.gl/shadertools": ["node_modules/@luma.gl/shadertools/src/index"],
"@luma.gl/shadertools/dist/es5/*": ["node_modules/@luma.gl/shadertools/src/*"],
"@luma.gl/shadertools/dist/es6/*": ["node_modules/@luma.gl/shadertools/src/*"],
"@luma.gl/shadertools/dist/esm/*": ["node_modules/@luma.gl/shadertools/src/*"],
"@luma.gl/test-utils": ["node_modules/@luma.gl/test-utils/src/index"],
"@luma.gl/test-utils/dist/es5/*": ["node_modules/@luma.gl/test-utils/src/*"],
"@luma.gl/test-utils/dist/es6/*": ["node_modules/@luma.gl/test-utils/src/*"],
"@luma.gl/test-utils/dist/esm/*": ["node_modules/@luma.gl/test-utils/src/*"],
"@luma.gl/webgl": ["node_modules/@luma.gl/webgl/src/index"],
"@luma.gl/webgl/dist/es5/*": ["node_modules/@luma.gl/webgl/src/*"],
"@luma.gl/webgl/dist/es6/*": ["node_modules/@luma.gl/webgl/src/*"],
"@luma.gl/webgl/dist/esm/*": ["node_modules/@luma.gl/webgl/src/*"],
},
"target": "ESNEXT",
"module": "commonjs",
"outDir": "./build/js",
/* Decorators */
"experimentalDecorators": true,
/* Basic stuff */
"moduleResolution": "node",
"skipLibCheck": true,
"skipDefaultLibCheck": true,
"lib": ["dom", "esnext", "esnext.asynciterable"],
/* Control what is emitted */
"declaration": true,
"noEmitOnError": true,
"removeComments": false,
"downlevelIteration": true,
/* Create inline sourcemaps with sources */
"sourceMap": false,
"inlineSources": true,
"inlineSourceMap": true,
/* The most restrictive settings possible */
"strict": true,
"importHelpers": true,
"noEmitHelpers": true,
"noImplicitAny": true,
"noUnusedLocals": true,
"noImplicitReturns": true,
"allowUnusedLabels": false,
"noUnusedParameters": true,
"allowUnreachableCode": false,
"noFallthroughCasesInSwitch": true,
"forceConsistentCasingInFileNames": true
}
}
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/USAGE.md
|
# Using the `node-rapids` docker images
## Quick links
* [About our docker images](#develop-with-docker-and-the-vscode-remote-containers-extension)
* [Retrieving the runtime images](#retrieving-the-runtime-images)
* [Running code in the runtime images](#running-code-in-the-runtime-images)
* [Extracting the packaged artifacts](#extracting-the-packaged-artifacts)
## About our docker images
We publish standalone docker images [to the `rapidsai/node` repository](https://github.com/orgs/rapidsai/packages/container/package/node) to the GitHub container registry on each PR merge.
These images are based on the [`nvidia/cuda`](https://hub.docker.com/r/nvidia/cuda) runtime image. They are intended to be used directly, extended with app code, or to serve as examples for building your own deployment images. The sources for each image are in [dev/dockerfiles/runtime](https://github.com/rapidsai/node/tree/main/dev/dockerfiles/runtime).
Our tag template scheme is as follows:
```txt
ghcr.io/rapidsai/node:{{ RAPIDS_VERSION }}-runtime-node{{ NODE_VERSION }}-cuda{{ CUDA_VERSION }}-ubuntu{{ UBUNTU_VERSION }}-{{ LIBRARY_NAME }}
```
The latest manifest is available at the [GitHub container registry page](https://github.com/orgs/rapidsai/packages/container/package/node).
## Retrieving the runtime images
The following will retrieve the docker image with each library (+ its native and JS dependencies) installed into the image's `/home/node/node_modules`:
```bash
REPO=ghcr.io/rapidsai/node
VERSIONS="22.12.2-runtime-node16.15.1-cuda11-ubuntu20.04"
docker pull $REPO:$VERSIONS-cudf
docker pull $REPO:$VERSIONS-cuml
docker pull $REPO:$VERSIONS-cugraph
docker pull $REPO:$VERSIONS-cuspatial
VERSIONS="22.12.2-runtime-node16.15.1-cuda11-ubuntu20.04"
docker pull $REPO:$VERSIONS-glfw
# Includes all the above RAPIDS libraries in a single image
docker pull $REPO:$VERSIONS-main
# Includes all the above RAPIDS libraries + demos in a single image
docker pull $REPO:$VERSIONS-demo
```
## Running code in the runtime images
Like the official node images, the default command in the runtime images is `node`. You can run the following test to validate the GPU is available to your containers:
```bash
REPO=ghcr.io/rapidsai/node
VERSIONS="22.12.2-runtime-node16.15.1-cuda11-ubuntu20.04"
# Be sure to pass either the `--runtime=nvidia` or `--gpus` flag!
docker run --rm --gpus=0 $REPO:$VERSIONS-cudf \
-p "const {Series, DataFrame} = require('@rapidsai/cudf');\
new DataFrame({ a: Series.new([0, 1, 2]) }).toString()"
> a
> 0.0
> 1.0
> 2.0
```
You can mount your host's X11 socket and `$DISPLAY` envvar, then launch demos that render via OpenGL:
```bash
REPO=ghcr.io/rapidsai/node
VERSIONS="22.12.2-runtime-node16.15.1-cuda11-ubuntu20.04"
# Be sure to pass either the `--runtime=nvidia` or `--gpus` flag!
docker run --rm \
--runtime=nvidia \
-e "DISPLAY=$DISPLAY" \
-v "/etc/fonts:/etc/fonts:ro" \
-v "/tmp/.X11-unix:/tmp/.X11-unix:rw" \
-v "/usr/share/fonts:/usr/share/fonts:ro" \
-v "/usr/share/icons:/usr/share/icons:ro" \
$REPO:$VERSIONS-demo \
npx @rapidsai/demo-graph
```
<details>
<summary>Click here to see a demo of the above command:</summary>
<img src="docs/images/docker-x11-socket-forwarding.gif"/>
</details>
## Extracting the packaged artifacts
We also publish the container of npm-packed `.tgz` artifacts installed into each runtime image.
You can use the following technique to install the npm-packed modules into another container or bare-metal cloud instance, provided you also install any runtime dependencies (a gcc-9-compatible `libstdc++`, matching CUDA Toolkit, etc.) needed by the module.
```bash
REPO=ghcr.io/rapidsai/node
VERSIONS="22.12.2-devel-node16.15.1-cuda11-ubuntu20.04"
# Pull the latest image of the packaged .tgz artifacts
docker pull $REPO:$VERSIONS-packages
# Copy the packaged .tgz artifacts, including any of its `@rapidsai/*` dependencies
docker run --rm -v "$PWD:/out" \
$REPO:$VERSIONS-packages \
bash -c "cp \
/opt/rapids/rapidsai-core-*.tgz \
/opt/rapids/rapidsai-cuda-*.tgz \
/opt/rapids/rapidsai-rmm-*.tgz \
/opt/rapids/rapidsai-cudf-*.tgz \
/out/"
# Install the npm-packed .tgz (+ npm dependencies) into `./node_modules`
npm install --force --production --legacy-peer-deps \
--omit dev --omit peer --omit optional \
*.tgz
```
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|
**/*.arrow
**/*.csv
**/*.geojson
**/*.gif
**/*.git
**/*.gitattributes
**/*.jpg
**/*.las
**/*.laz
**/*.o
**/*.parquet
**/*.png
**/*.so
**/*.tar.gz
**/*.tar.xz
**/*.zip
**/*.pyc
**/lerna-debug.log
**/docker-compose*.yml
**/compile_commands.json
**/*.code-workspace
**/z-*
**/out/
**/.logs/
**/.next/
**/_next/
**/build/
**/.cache/
**/.ccache/
**/.clangd/
**/.github/
**/.vscode/
**/.cmake-js/
**/node_modules/
**/modules/**/doc/*
**/modules/**/lib/*
**/modules/**/data/*
**/modules/*/include/napi.h
**/modules/*/include/napi-inl.h
**/modules/*/include/napi-inl.deprecated.h
**/modules/*/include/node_api.h
README.md
BINDINGS.md
doc
docs
internal
dev/dockerfiles
.env.sample
.gitignore
.clang-format
.dockerignore
!**/modules/demo/**/data/*.png
!**/modules/demo/**/data/*.jpg
!**/modules/demo/**/data/*.gif
!**/modules/demo/**/data/*.las
!**/modules/demo/**/data/*.laz
!**/modules/demo/**/data/*.arrow
!**/modules/demo/**/public/*.png
!**/modules/demo/**/public/*.jpg
!**/modules/demo/**/public/*.gif
!**/modules/demo/**/public/*.las
!**/modules/demo/**/public/*.laz
!**/modules/demo/**/public/*.arrow
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rapidsai_public_repos/node/DEVELOP.md
|
# Development
This document is intended for anyone who wants to clone and build the `node-rapids` modules from source. If you're interested in using `node-rapids` packages in your app, see our docs on [using the public `node-rapids` docker images](https://github.com/rapidsai/node/tree/main/USAGE.md).
## Quick links
* [Develop with docker and VSCode Remote Containers](#develop-with-docker-and-the-vscode-remote-containers-extension)
* [Setup yarn workspace](#setup-yarn-workspace)
* [Build the modules](#build-the-modules)
* [Build modules individually](#build-modules-individually)
* [Running the demos](#running-the-demos)
* [Running notebooks](#running-notebooks)
* [Troubleshooting](#troubleshooting)
* [Develop on bare-metal](#develop-on-bare-metal)
## System/GPU requirements
* Ubuntu 18.04+ (other Linuxes should work, but untested)
* [CUDA Toolkit 11.0+](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements)
* NVIDIA driver 465+
* \>= Pascal architecture (Compute Capability >=7.0)
* `docker-ce` 19.03+ (optional)
* `docker-compose` v1.28.5+ (optional)
## Develop with docker and [VSCode Remote Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
You can build and run the C++ and TypeScript in a [docker](https://docker.com/) container with the [nvidia-container-runtime](https://github.com/NVIDIA/nvidia-docker) and [docker-compose](https://github.com/docker/compose/).
This eliminates potential incompatibilities between the tools and environment in your base OS vs. the tools needed to build, format, lint, debug, and provide intellisense for the C++ native modules.
### Install docker and VSCode extensions
See our `docker`, `docker-compose`, and `nvidia-container-runtime` [installation and setup instructions](https://github.com/rapidsai/node/tree/main/docs/docker/installation.md).
Install the [VSCode Remote Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers).
We recommend installing the following extensions in the development container:
* [CMake](https://marketplace.visualstudio.com/items?itemName=twxs.cmake)
* [Microsoft C/C++ tools](https://marketplace.visualstudio.com/items?itemName=ms-vscode.cpptools)
* [Clang Format](https://marketplace.visualstudio.com/items?itemName=xaver.clang-format)
* [Clangd](https://marketplace.visualstudio.com/items?itemName=llvm-vs-code-extensions.vscode-clangd)
* [Tasks Shell Input](https://marketplace.visualstudio.com/items?itemName=augustocdias.tasks-shell-input)
* [ESLint](https://marketplace.visualstudio.com/items?itemName=dbaeumer.vscode-eslint)
### Clone the repository
```bash
git clone https://github.com/rapidsai/node.git
```
### Create a `.env` file for local development
Create a local [`.env` file](https://docs.docker.com/compose/environment-variables/#the-env-file) for `docker-compose`. This file controls high-level features outside the container, like CUDA and Ubuntu version, and more fine-grained features inside the container like the user, number of parallel jobs, and compiler caching.
```bash
# Make a local .env file of envvar overrides for the container
cp .env.sample .env
# Modify the .env file to match your system's parameters
nano .env
```
### Starting and connecting to the development container
To pull and run the development container, issue the following commands:
```bash
# Pull the main development container built in node-rapids CI (fastest)
docker-compose -f docker-compose.devel.yml pull main
# Or build the development containers locally (e.g. after making changes)
yarn docker:build:devel:main
# Start the main development container
yarn docker:run:devel
```
After starting the container, you should see it in the VSCode Remote Containers extension.
<details>
<summary>Click the "Attach to Container" icon to open a new VSCode window in the context of the development container:</summary>
<img src="docs/images/vscode-attach-to-container.png"/>
</details>
<br/>
## Setup yarn workspace
Install and boostrap the monorepo's yarn workspace inside the development container:
```bash
# Boostrap the yarn workspace, set up monorepo symlinks, and install dev dependencies
yarn
```
## Build the modules
To build the TypeScript and C++ of each module:
```bash
yarn build
```
To build the TypeScript half of each module:
```bash
yarn tsc:build
```
To build the C++ half of each module:
```bash
yarn cpp:build
```
### Build modules individually
The npm scripts in the top-level `package.json` operate on every module (via [yarn workspaces](https://classic.yarnpkg.com/lang/en/docs/workspaces/) and [lerna](https://github.com/lerna/lerna)), but often it's easier to build a single module. Because each module also defines those scripts, we can use `yarn workspace` to run them in isolation:
```bash
# Run the `cpp:build` npm script in the cuDF module only
yarn workspace @rapidsai/cudf cpp:build
```
### Escape hatch
If something goes haywire in the dependency tree or build cache, we include an "escape hatch" command to clean and reinstall/rebuild all dependencies and build directories inside the container. Feel free to run this as often as you need:
```bash
yarn nuke:from:orbit
```
### CMake and Ninja
We use [cmake-js](https://www.npmjs.com/package/cmake-js) to generate the C++ build scripts and follow a similar naming convention in our npm scripts:
```txt
Usage: cmake-js [<command>] [options]
Commands:
configure Configure CMake project
build Build the project (will configure first if required)
reconfigure Clean the project directory then configure the project
rebuild Clean the project directory then build the project
compile Build the project, and if build fails, try a full rebuild
```
These `cmake-js` commands are exposed as npm scripts in the top-level `package.json`:
```txt
yarn cpp:configure Configure CMake project
yarn cpp:build Build the project (will configure first if required)
yarn cpp:reconfigure Clean the project directory then configure the project
yarn cpp:rebuild Clean the project directory then build the project
yarn cpp:compile Build the project, and if build fails, try a full rebuild
```
Adding the `:debug` suffix to the above command will build the C++ projects (and their dependencies!) with `CMAKE_BUILD_TYPE=Debug`:
```txt
yarn cpp:rebuild:debug
```
This is potentially expensive but is required if you need to run in the debugger.
#### CMake
CMake is a "build script generator." It uses a `CMakeLists.txt` as a description of how to generate C++ build scripts.
A CMake build is separated into two phases; a "configuration" phase (run CMake to generate the build scripts), and a "compile" phase (executes commands in the generated build scripts to compile C++ source into object files).
Certain information about how to build C++ source into object files is determined in the "configuration" phase. If this information changes, the configuration phase must be re-run. For example, the C++ `.cpp` file paths are found in the configuration phase, so adding a new `.cpp` file requires regenerating (i.e. "reconfiguring") the build scripts.
#### Ninja
[Ninja](https://ninja-build.org/) is a build tool to compile C++ sources into object files. The CMake configuration phase generates ninja build scripts.
## Running the demos
The demos can be run via the top-level `yarn demo` npm script:
```bash
# Show a list of available demos
yarn demo
# Run a specific demo with extra arguments passed to the selected demo
yarn demo modules/demo/luma 01
```
## Running notebooks
We've included a container for launching [`jupyterlab-desktop`](https://github.com/jupyterlab/jupyterlab-desktop) with access to locally built `node-rapids` modules:
```shell
# Compile the TypeScript and C++ modules inside the development container (only necessary if it hasn't already been built)
yarn docker:run:devel bash -c 'yarn && yarn build'
# Build the notebook container (only necessary if it hasn't already been built)
yarn docker:build:devel:notebook
# Start a containerized `jupyterlab-desktop` instance with the source tree as a docker volume mount
yarn docker:run:devel:notebook
```
## Troubleshooting
Potential error messages you may encounter while building:
* > ninja: error: loading 'build.ninja': No such file or directory
This means the CMake "configure" step did not succeed. You need to execute `yarn rebuild` from the top of the repo (to rebuild everything), or in the specific module that failed to configure.
## Develop on bare-metal
Though not usually advised, follow [these instructions](https://github.com/rapidsai/node/tree/main/docs/develop-on-bare-metal.md) to develop outside docker on properly configured Ubuntu host.
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|
fund=false
audit=false
save-prefix=
--omit=optional
save-exact=true
package-lock=false
loglevel=error
update-notifier=false
scripts-prepend-node-path=true
registry=https://registry.npmjs.org/
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rapidsai_public_repos/node/.eslintrc.js
|
module.exports = {
'env': {
'browser': true,
'es6': true,
'node': true,
},
'plugins': ['@typescript-eslint'],
'parser': '@typescript-eslint/parser',
'parserOptions': {
'project': [
'tsconfig.json'
],
'sourceType': 'module'
},
'extends': [
'eslint:recommended',
'plugin:@typescript-eslint/recommended',
'plugin:@typescript-eslint/recommended-requiring-type-checking'
],
'rules': {
// "prefer-const": "off",
// "prefer-rest-params": "off",
'semi': ['error', 'always'],
'@typescript-eslint/unbound-method': 'off',
'@typescript-eslint/ban-ts-comment': 'off',
'@typescript-eslint/no-unsafe-call': 'off',
'@typescript-eslint/no-var-requires': 'off',
'@typescript-eslint/no-explicit-any': 'off',
'@typescript-eslint/no-unsafe-return': 'off',
'@typescript-eslint/no-empty-function': 'off',
'@typescript-eslint/no-require-imports': 'off',
'@typescript-eslint/no-unsafe-argument': 'off',
'@typescript-eslint/no-unsafe-assignment': 'off',
// "@typescript-eslint/no-non-null-assertion": "off",
'@typescript-eslint/no-unsafe-member-access': 'off',
'@typescript-eslint/explicit-module-boundary-types': 'off',
'@typescript-eslint/no-redeclare': [
'error',
{'ignoreDeclarationMerge': true},
],
'@typescript-eslint/no-use-before-define': [
'error',
{'functions': false, 'classes': false, 'variables': false},
],
},
'overrides': [
{'files': ['test/**/*.ts'], 'rules': {'no-debugger': 'off'}},
]
};
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rapidsai_public_repos/node/LICENSE
|
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include/visit_struct/visit_struct.hpp (modified): BSL 1.0
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rapidsai_public_repos/node/.env.sample
|
# The ID of the non-root user inside the container (can match host user ID)
UID=1000
# Number of C++ object files to build in parallel. This is dependent on the number of CPU
# cores your machine has. Increasing the number will result in faster build times, but use
# more memory.
PARALLEL_LEVEL=4
# `nvidia/cuda` base image CUDA version
# CUDA_VERSION=11.6.2
# `nvidia/cuda` base image Ubuntu version
# LINUX_VERSION=ubuntu20.04
# List of CUDA device architectures to target.
# CUDAARCHS=60-real;70-real;75-real;80-real;86
#
# Or set to `ALL` to compile for all supported.
# CUDAARCHS=ALL
# How long sccache should wait until it considers a compile job timed out.
# This number should be large, because C++ and CUDA can take a long time to compile.
SCCACHE_IDLE_TIMEOUT=32768
# Optional S3 region and bucket for shared sccache.
#
# This bucket is publicly readable, and is continuously populated by the RAPIDS dev team and CI jobs.
#
# Using these values can significantly speed up your compile times.
SCCACHE_REGION=us-west-2
SCCACHE_BUCKET=node-rapids-sccache
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/.clang-format
|
---
# Refer to the following link for the explanation of each params:
# http://releases.llvm.org/8.0.0/tools/clang/docs/ClangFormatStyleOptions.html
Language: Cpp
AccessModifierOffset: -1
AlignAfterOpenBracket: Align
AlignArrayOfStructures: None
AlignConsecutiveAssignments: true
AlignConsecutiveDeclarations: false
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AllowAllParametersOfDeclarationOnNextLine: true
AllowShortBlocksOnASingleLine: true
AllowShortCaseLabelsOnASingleLine: true
AllowShortFunctionsOnASingleLine: All
AllowShortIfStatementsOnASingleLine: true
AllowShortLoopsOnASingleLine: true
# This is deprecated
AlwaysBreakAfterDefinitionReturnType: None
AlwaysBreakAfterReturnType: None
AlwaysBreakBeforeMultilineStrings: true
AlwaysBreakTemplateDeclarations: Yes
BinPackArguments: false
BinPackParameters: false
BitFieldColonSpacing: After
BraceWrapping:
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AfterControlStatement: false
AfterEnum: false
AfterFunction: false
AfterNamespace: false
AfterObjCDeclaration: false
AfterStruct: false
AfterUnion: false
AfterExternBlock: false
BeforeCatch: false
BeforeElse: false
IndentBraces: false
# disabling the below splits, else, they'll just add to the vertical length of source files!
SplitEmptyFunction: false
SplitEmptyRecord: false
SplitEmptyNamespace: false
BreakArrays: false
BreakAfterAttributes: Leave
BreakBeforeBinaryOperators: None
BreakBeforeBraces: Custom
BreakBeforeInheritanceComma: false
BreakInheritanceList: BeforeColon
BreakBeforeTernaryOperators: true
BreakConstructorInitializersBeforeComma: false
BreakConstructorInitializers: BeforeColon
BreakAfterJavaFieldAnnotations: false
BreakStringLiterals: true
ColumnLimit: 100
CommentPragmas: '^ IWYU pragma:'
CompactNamespaces: false
ConstructorInitializerAllOnOneLineOrOnePerLine: true
# Kept the below 2 to be the same as `IndentWidth` to keep everything uniform
ConstructorInitializerIndentWidth: 2
ContinuationIndentWidth: 2
Cpp11BracedListStyle: true
DerivePointerAlignment: false
DisableFormat: false
ExperimentalAutoDetectBinPacking: false
FixNamespaceComments: true
ForEachMacros:
- foreach
- Q_FOREACH
- BOOST_FOREACH
IncludeBlocks: Preserve
IncludeCategories:
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Priority: 2
- Regex: '^<.*\.h>'
Priority: 1
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Priority: 2
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Priority: 3
IncludeIsMainRegex: '([-_](test|unittest))?$'
IndentCaseLabels: true
IndentPPDirectives: None
IndentWidth: 2
IndentWrappedFunctionNames: false
InsertNewlineAtEOF: true
KeepEmptyLinesAtTheStartOfBlocks: false
MacroBlockBegin: ''
MacroBlockEnd: ''
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
ObjCBinPackProtocolList: Never
ObjCBlockIndentWidth: 2
ObjCSpaceAfterProperty: false
ObjCSpaceBeforeProtocolList: true
PenaltyBreakAssignment: 2
PenaltyBreakBeforeFirstCallParameter: 1
PenaltyBreakComment: 300
PenaltyBreakFirstLessLess: 120
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PenaltyBreakTemplateDeclaration: 10
PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 200
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RawStringFormats:
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Delimiters:
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CanonicalDelimiter: ''
- Language: TextProto
Delimiters:
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EnclosingFunctions:
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- PARSE_PARTIAL_TEXT_PROTO
- PARSE_TEST_PROTO
- PARSE_TEXT_PROTO
- ParseTextOrDie
- ParseTextProtoOrDie
CanonicalDelimiter: ''
BasedOnStyle: google
# Enabling comment reflow causes doxygen comments to be messed up in their formats!
ReflowComments: true
RemoveBracesLLVM: false
SortIncludes: true
SortUsingDeclarations: true
SpaceAfterCStyleCast: false
SpaceAfterTemplateKeyword: true
SpaceAroundPointerQualifiers: Default
SpaceBeforeAssignmentOperators: true
SpaceBeforeCaseColon: false
SpaceBeforeCpp11BracedList: false
SpaceBeforeCtorInitializerColon: true
SpaceBeforeInheritanceColon: true
SpaceBeforeParens: ControlStatements
SpaceBeforeRangeBasedForLoopColon: true
SpaceBeforeSquareBrackets: false
SpaceInEmptyBlock: false
SpaceInEmptyParentheses: false
SpacesBeforeTrailingComments: 2
SpacesInAngles: Never
SpacesInParens: Never
SpacesInContainerLiterals: true
SpacesInCStyleCastParentheses: false
SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: c++17
StatementMacros:
- Q_UNUSED
- QT_REQUIRE_VERSION
# Be consistent with indent-width, even for people who use tab for indentation!
TabWidth: 2
UseTab: Never
---
# Refer to the following link for the explanation of each params:
# https://clang.llvm.org/docs/ClangFormatStyleOptions.html
Language: JavaScript
#########################################################
#
# Additions or modifications to the above C++ settings
#
#########################################################
AccessModifierOffset: 0
AlignArrayOfStructures: None
AllowAllArgumentsOnNextLine: true
AlignConsecutiveBitFields: true
AllowShortEnumsOnASingleLine: false
AllowShortLambdasOnASingleLine: All
BreakStringLiterals: false
InsertTrailingCommas: None
IndentCaseBlocks: false
JavaScriptQuotes: Single
JavaScriptWrapImports: true
SpaceAfterCStyleCast: true
SpaceAfterLogicalNot: false
SpaceBeforeCpp11BracedList: true
SpaceBeforeCtorInitializerColon: false
SpaceBeforeJsonColon: false
SpaceBeforeInheritanceColon: false
SpaceBeforeRangeBasedForLoopColon: false
SpacesInConditionalStatement: false
SpacesInContainerLiterals: false
#########################################################
#
# Begin previous C++ settings
#
#########################################################
AlignAfterOpenBracket: Align
AlignConsecutiveAssignments: true
AlignConsecutiveDeclarations: false
AlignEscapedNewlines: Left
AlignOperands: AlignAfterOperator
AlignTrailingComments:
Kind: Always
OverEmptyLines: 2
AllowAllParametersOfDeclarationOnNextLine: true
AllowShortBlocksOnASingleLine: true
AllowShortCaseLabelsOnASingleLine: true
AllowShortFunctionsOnASingleLine: All
AllowShortIfStatementsOnASingleLine: true
AllowShortLoopsOnASingleLine: true
# This is deprecated
AlwaysBreakAfterDefinitionReturnType: None
AlwaysBreakAfterReturnType: None
AlwaysBreakBeforeMultilineStrings: true
AlwaysBreakTemplateDeclarations: Yes
BinPackArguments: false
BinPackParameters: false
BitFieldColonSpacing: After
BraceWrapping:
AfterClass: false
AfterControlStatement: false
AfterEnum: false
AfterFunction: false
AfterNamespace: false
AfterObjCDeclaration: false
AfterStruct: false
AfterUnion: false
AfterExternBlock: false
BeforeCatch: false
BeforeElse: false
IndentBraces: false
# disabling the below splits, else, they'll just add to the vertical length of source files!
SplitEmptyFunction: false
SplitEmptyRecord: false
SplitEmptyNamespace: false
BreakArrays: false
BreakAfterAttributes: Leave
BreakBeforeBinaryOperators: None
BreakBeforeBraces: Custom
BreakBeforeInheritanceComma: false
BreakInheritanceList: BeforeColon
BreakBeforeTernaryOperators: true
BreakConstructorInitializersBeforeComma: false
BreakConstructorInitializers: BeforeColon
BreakAfterJavaFieldAnnotations: false
ColumnLimit: 100
CommentPragmas: '^ IWYU pragma:'
CompactNamespaces: false
ConstructorInitializerAllOnOneLineOrOnePerLine: true
# Kept the below 2 to be the same as `IndentWidth` to keep everything uniform
ConstructorInitializerIndentWidth: 2
ContinuationIndentWidth: 2
Cpp11BracedListStyle: true
DerivePointerAlignment: false
DisableFormat: false
ExperimentalAutoDetectBinPacking: false
FixNamespaceComments: true
ForEachMacros:
- foreach
- Q_FOREACH
- BOOST_FOREACH
IncludeBlocks: Preserve
IncludeCategories:
- Regex: '^<ext/.*\.h>'
Priority: 2
- Regex: '^<.*\.h>'
Priority: 1
- Regex: '^<.*'
Priority: 2
- Regex: '.*'
Priority: 3
IncludeIsMainRegex: '([-_](test|unittest))?$'
IndentCaseLabels: true
IndentPPDirectives: None
IndentWidth: 2
IndentWrappedFunctionNames: false
InsertNewlineAtEOF: true
KeepEmptyLinesAtTheStartOfBlocks: false
MacroBlockBegin: ''
MacroBlockEnd: ''
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
ObjCBinPackProtocolList: Never
ObjCBlockIndentWidth: 2
ObjCSpaceAfterProperty: false
ObjCSpaceBeforeProtocolList: true
PenaltyBreakAssignment: 2
PenaltyBreakBeforeFirstCallParameter: 1
PenaltyBreakComment: 300
PenaltyBreakFirstLessLess: 120
PenaltyBreakString: 1000
PenaltyBreakTemplateDeclaration: 10
PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 200
PointerAlignment: Left
ReferenceAlignment: Left
RawStringFormats:
- Language: JavaScript
Delimiters:
- js
- Js
- JS
- ts
- Ts
- TS
- javascript
- Javascript
- JavaScript
- typescript
- Typescript
- TypeScript
CanonicalDelimiter: ''
- Language: TextProto
Delimiters:
- pb
- PB
- proto
- PROTO
EnclosingFunctions:
- EqualsProto
- EquivToProto
- PARSE_PARTIAL_TEXT_PROTO
- PARSE_TEST_PROTO
- PARSE_TEXT_PROTO
- ParseTextOrDie
- ParseTextProtoOrDie
CanonicalDelimiter: ''
BasedOnStyle: google
# Enabling comment reflow causes doxygen comments to be messed up in their formats!
ReflowComments: true
RemoveBracesLLVM: false
SortIncludes: true
SortUsingDeclarations: true
SpaceAfterTemplateKeyword: true
SpaceAroundPointerQualifiers: Default
SpaceBeforeAssignmentOperators: true
SpaceBeforeCaseColon: false
SpaceBeforeParens: ControlStatements
SpaceBeforeSquareBrackets: false
SpaceInEmptyBlock: false
SpaceInEmptyParentheses: false
SpacesBeforeTrailingComments: 2
SpacesInAngles: Never
SpacesInParens: Never
SpacesInCStyleCastParentheses: false
SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: c++17
StatementMacros:
- Q_UNUSED
- QT_REQUIRE_VERSION
# Be consistent with indent-width, even for people who use tab for indentation!
TabWidth: 2
UseTab: Never
| 0 |
rapidsai_public_repos
|
rapidsai_public_repos/node/typedoc.js
|
module.exports = {
entryPoints: [
'modules/cuda/src/index.ts',
'modules/rmm/src/index.ts',
'modules/cudf/src/index.ts',
'modules/cuml/src/index.ts',
'modules/cugraph/src/index.ts',
'modules/cuspatial/src/index.ts',
'modules/io/src/index.ts',
'modules/deck.gl/src/index.ts',
'modules/glfw/src/index.ts',
'modules/jsdom/src/index.ts',
'modules/webgl/src/index.ts',
'modules/sql/src/index.ts',
],
out: 'doc',
name: 'RAPIDS',
tsconfig: 'tsconfig.json',
hideGenerator: true,
excludePrivate: true,
excludeProtected: true,
excludeExternals: true,
};
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/package.json
|
{
"name": "@rapidsai/deck.gl",
"version": "22.12.2",
"description": "NVIDIA RAPIDS deck.gl components and utilities",
"license": "Apache-2.0",
"main": "index.js",
"types": "build/js",
"author": "NVIDIA, Inc. (https://nvidia.com/)",
"maintainers": [
"Paul Taylor <[email protected]>"
],
"homepage": "https://github.com/rapidsai/node/tree/main/modules/deck.gl#readme",
"bugs": {
"url": "https://github.com/rapidsai/node/issues"
},
"repository": {
"type": "git",
"url": "git+https://github.com/rapidsai/node.git"
},
"scripts": {
"clean": "rimraf build doc compile_commands.json",
"doc": "rimraf doc && typedoc --options typedoc.js",
"test": "node -r dotenv/config node_modules/.bin/jest -c jest.config.js",
"build": "yarn tsc:build",
"compile": "yarn tsc:build",
"rebuild": "yarn tsc:build",
"tsc:clean": "rimraf build/js",
"tsc:build": "yarn tsc:clean && tsc -p ./tsconfig.json",
"tsc:watch": "yarn tsc:clean && tsc -p ./tsconfig.json -w"
},
"dependencies": {
"@deck.gl/core": "8.8.10",
"@deck.gl/layers": "8.8.10",
"@luma.gl/core": "8.5.16",
"@luma.gl/engine": "8.5.16",
"@luma.gl/webgl": "8.5.16",
"@rapidsai/core": "~22.12.2",
"@rapidsai/cuda": "~22.12.2",
"@rapidsai/cudf": "~22.12.2",
"mjolnir.js": "2.7.1"
},
"files": [
"build",
"LICENSE",
"index.js",
"README.md",
"package.json"
]
}
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/index.js
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
module.exports = require('./build/js/index');
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/jest.config.js
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
try {
require('dotenv').config();
} catch (e) { }
module.exports = {
"verbose": true,
"testEnvironment": "node",
"maxWorkers": process.env.PARALLEL_LEVEL || 1,
"globals": {
"ts-jest": {
"diagnostics": false,
"tsconfig": "test/tsconfig.json"
}
},
"rootDir": "./",
"roots": [
"<rootDir>/test/"
],
"moduleFileExtensions": [
"js",
"ts",
"tsx"
],
"coverageReporters": [
"lcov"
],
"coveragePathIgnorePatterns": [
"test\\/.*\\.(ts|tsx|js)$",
"/node_modules/"
],
"transform": {
"^.+\\.jsx?$": "ts-jest",
"^.+\\.tsx?$": "ts-jest"
},
"transformIgnorePatterns": [
"/build/(js|Debug|Release)/*$",
],
"testRegex": "(.*(-|\\.)(test|spec)s?)\\.(ts|tsx|js)$",
"preset": "ts-jest",
"testMatch": null,
"moduleNameMapper": {
"^@rapidsai\/deck\.gl(.*)": "<rootDir>/src/$1",
}
};
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/tsconfig.json
|
{
"include": ["src"],
"exclude": ["node_modules"],
"compilerOptions": {
"baseUrl": "./",
"paths": {
"@rapidsai/deck.gl": ["src/index"],
"@rapidsai/deck.gl/*": ["src/*"]
},
"target": "ESNEXT",
"module": "commonjs",
"outDir": "./build/js",
/* Decorators */
"experimentalDecorators": true,
/* Basic stuff */
"moduleResolution": "node",
"skipLibCheck": true,
"skipDefaultLibCheck": true,
"lib": ["dom", "esnext", "esnext.asynciterable"],
/* Control what is emitted */
"declaration": true,
"declarationMap": true,
"noEmitOnError": true,
"removeComments": false,
"downlevelIteration": true,
/* Create inline sourcemaps with sources */
"sourceMap": false,
"inlineSources": true,
"inlineSourceMap": true,
/* The most restrictive settings possible */
"strict": true,
"importHelpers": true,
"noEmitHelpers": true,
"noImplicitAny": true,
"noUnusedLocals": true,
"noImplicitReturns": true,
"allowUnusedLabels": false,
"noUnusedParameters": true,
"allowUnreachableCode": false,
"noFallthroughCasesInSwitch": true,
"forceConsistentCasingInFileNames": true
}
}
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/LICENSE
|
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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6. Trademarks. This License does not grant permission to use the trade
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whether in tort (including negligence), contract, or otherwise,
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END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
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Copyright 2019 NVIDIA Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/deck.gl/typedoc.js
|
module.exports = {
entryPoints: ['src/index.ts'],
out: 'doc',
name: '@rapidsai/deck.gl',
tsconfig: 'tsconfig.json',
excludePrivate: true,
excludeProtected: true,
excludeExternals: true,
};
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/.vscode/launch.json
|
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Debug Tests",
"type": "node",
"request": "launch",
"cwd": "${workspaceFolder}",
"console": "integratedTerminal",
"internalConsoleOptions": "neverOpen",
"program": "${workspaceFolder}/node_modules/.bin/jest",
"skipFiles": [
"<node_internals>/**",
"${workspaceFolder}/node_modules/**"
],
"env": {
"NODE_NO_WARNINGS": "1",
"NODE_ENV": "production",
"READABLE_STREAM": "disable",
},
"args": [
"--verbose",
"--runInBand",
"-c", "jest.config.js",
"${input:TEST_FILE}"
]
},
],
"inputs": [
{
"type": "command",
"id": "TEST_FILE",
"command": "shellCommand.execute",
"args": {
"cwd": "${workspaceFolder}/modules/deck.gl",
"description": "Select a file to debug",
"command": "./node_modules/.bin/jest --listTests | sed -r \"s@$PWD/test/@@g\"",
}
},
],
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/.vscode/tasks.json
|
{
"version": "2.0.0",
"tasks": [
{
"type": "npm",
"group": "build",
"label": "Recompile @rapidsai/deck.gl TS (fast)",
"script": "tsc:build",
"detail": "yarn tsc:build",
"problemMatcher": ["$tsc"],
},
],
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/src/index.ts
|
// Copyright (c) 2020-2023, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
export * from './buffer';
export * from './layers/graph';
export * from './layers/point-cloud';
export * from './utils/series-color-utils';
export {Deck as DeckSSR} from './ssr/deck';
export {AnimationLoop as AnimationLoopSSR} from './ssr/animation-loop';
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/src/deck.gl.ts
|
// Copyright (c) 2021-2023, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
export interface DeckContext {
gl: WebGL2RenderingContext;
shaderCache: any;
}
export interface PickingInfo {
/** Mouse position x relative to the viewport. */
x: number;
/** Mouse position y relative to the viewport. */
y: number;
/**
Mouse position in world coordinates. Only applies if coordinateSystem is
COORDINATE_SYSTEM.LNGLAT.
*/
coordinate: [number, number];
/**
The color of the pixel that is being picked. It represents a "picking color" that is
encoded by layer.encodePickingColor().
*/
color: [number, number, number, number];
/**
The index of the object that is being picked. It is the returned value of
layer.decodePickingColor().
*/
index: number;
/** true if index is not -1. */
picked: boolean;
[key: string]: any;
}
export interface UpdateStateProps {
oldProps: any;
props: any;
context: DeckContext;
changeFlags: any;
}
export declare class Deck {
constructor(props: any);
[key: string]: any;
}
export declare class DeckLayer {
constructor(props?: any);
props: any;
state: any;
internalState: any;
setState(updatedState: any): void;
initializeState(context: DeckContext): void;
shouldUpdateState(options: UpdateStateProps): boolean;
updateState(options: UpdateStateProps): void;
finalizeState(context?: DeckContext): void;
draw(options: {moduleParameters: any, uniforms: any, context: DeckContext}): void;
getPickingInfo({info, mode}: {info: PickingInfo, mode: 'hover'|'click'}): void;
onHover(info: PickingInfo, pickingEvent: any): void;
getAttributeManager(): any;
getShaders(shaders: any): any;
}
export declare class DeckCompositeLayer extends DeckLayer {
getSubLayerProps(subLayerProps: any): any;
}
export declare class DeckTextLayer extends DeckLayer {
constructor(props?: any);
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/src/buffer.ts
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
const {Buffer: LumaBuffer_} = require('@luma.gl/webgl');
import {BufferProps} from '@luma.gl/webgl';
import {LumaBuffer} from './luma.gl';
import {CUDA, Uint8Buffer} from '@rapidsai/cuda';
export class Buffer extends (LumaBuffer_ as typeof LumaBuffer) {
static mapResources(buffers: any[] = []) {
buffers = buffers.filter((buffer) => buffer && buffer.handle &&
buffer.handle.cudaGraphicsResource !== undefined &&
buffer.handle.cudaGraphicsResourceMapped === false);
CUDA.runtime.cudaGraphicsMapResources(
buffers.map((buffer) => buffer.handle.cudaGraphicsResource));
buffers.forEach((buffer) => buffer.handle.cudaGraphicsResourceMapped = true);
}
static unmapResources(buffers: any[] = []) {
buffers = buffers.filter((buffer) => buffer && buffer.handle &&
buffer.handle.cudaGraphicsResource !== undefined &&
buffer.handle.cudaGraphicsResourceMapped === true);
CUDA.runtime.cudaGraphicsUnmapResources(
buffers.map((buffer) => buffer.handle.cudaGraphicsResource));
buffers.forEach((buffer) => buffer.handle.cudaGraphicsResourceMapped = false);
}
constructor(gl: WebGLRenderingContext, props?: BufferProps);
constructor(gl: WebGLRenderingContext, data: ArrayBufferView|number[]);
constructor(gl: WebGLRenderingContext, byteLength: number);
constructor(gl: WebGLRenderingContext, propsDataOrByteLength?: any) {
super(gl, propsDataOrByteLength);
if (this.byteLength > 0) { this._registerResource(this.handle); }
}
subData(props: any = {}) {
if (!this._handle || !this._handle.cudaGraphicsResourceMapped) { return super.subData(props); }
props = props instanceof ArrayBuffer ? {data: new Uint8Array(props), length: props.byteLength}
: ArrayBuffer.isView(props) ? {data: props, length: props.byteLength}
: {data: new Uint8Array(0), ...props};
const {
data: buffer,
length = buffer.byteLength,
byteLength: srcLength = length,
offset = 0,
srcOffset = 0,
} = props;
this.asCUDABuffer(offset).set({buffer, byteOffset: srcOffset, byteLength: srcLength});
return this;
}
asMappedResource(work: (buffer: this) => any) {
this._mapResource();
work(this);
this._unmapResource();
}
asCUDABuffer(byteOffset = 0, byteLength = this.byteLength - byteOffset) {
if (this._handle.cudaGraphicsResourceMapped) {
return new Uint8Buffer(
CUDA.runtime.cudaGraphicsResourceGetMappedPointer(this._handle.cudaGraphicsResource),
byteOffset,
byteLength);
}
throw new Error(
'OpenGL Buffer must be mapped as a CUDAGraphicsResource to create a CUDA buffer');
}
_deleteHandle() {
this._unregisterResource(this._handle);
return super._deleteHandle();
}
// eslint-disable-next-line @typescript-eslint/restrict-plus-operands
_setData(data: any, offset = 0, byteLength = data.byteLength + offset) {
const mapped = this._handle.cudaGraphicsResourceMapped;
mapped && this._unmapResource(this._handle);
super._setData(data, offset, byteLength);
mapped && this._mapResource(this._handle);
return this;
}
_setByteLength(byteLength: number, usage = this.usage) {
const mapped = this._handle.cudaGraphicsResourceMapped;
mapped && this._unmapResource(this._handle);
super._setByteLength(byteLength, usage);
mapped && this._mapResource(this._handle);
return this;
}
_registerResource(handle = this._handle) {
if (handle && handle.cudaGraphicsResource === undefined) {
this._handle = handle;
handle.cudaGraphicsResourceMapped = false;
handle.cudaGraphicsResource = CUDA.runtime.cudaGraphicsGLRegisterBuffer(handle.ptr, 0);
}
return this;
}
_unregisterResource(handle = this._handle) {
if (handle && handle.cudaGraphicsResource !== undefined) {
this._unmapResource(handle);
CUDA.runtime.cudaGraphicsUnregisterResource(handle.cudaGraphicsResource);
handle.cudaGraphicsResource = undefined;
}
return this;
}
_mapResource(handle = this._handle) {
if (handle && handle.cudaGraphicsResource && !handle.cudaGraphicsResourceMapped) {
CUDA.runtime.cudaGraphicsMapResources([handle.cudaGraphicsResource]);
handle.cudaGraphicsResourceMapped = true;
}
return this;
}
_unmapResource(handle = this._handle) {
if (handle && handle.cudaGraphicsResource && handle.cudaGraphicsResourceMapped) {
CUDA.runtime.cudaGraphicsUnmapResources([handle.cudaGraphicsResource]);
handle.cudaGraphicsResourceMapped = false;
}
return this;
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/src/luma.gl.ts
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {
Buffer as LumaBuffer_,
} from '@luma.gl/webgl';
// Add protected props and methods missing on the luma.gl typings
export declare class LumaResource {
_deleteHandle(handle?: any): void;
}
export declare class LumaBuffer extends LumaBuffer_ {
// _handle isn't mutable in practice, even though it is in the luma.gl typings
_handle: any;
_deleteHandle(): void;
_setData(data: any, offset?: number, byteLength?: number): void;
_setByteLength(byteLength: number, usage?: number): void;
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/utils/series-color-utils.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {IndexType, Series, Uint32, Uint32Series} from '@rapidsai/cudf';
function getSize(col: Series<IndexType>|number) {
return col instanceof Series ? col.length : undefined;
}
function validateSeries(col: Series<IndexType>) {
if (col.min() < 0 || col.max() > 255) {
throw new RangeError('invalid column, contains values outside of the range [0,255]');
}
return col;
}
function hexToInteger(value: string) {
value = value.replace('#', '0x');
return parseInt(`${value}ff`, 16);
}
/**
* Convert 4 individual RGBA Series or values to a RGBA Integer Series to be consumed directly by
* color buffers
* @param r Series<IndexType> or number for the color red (valid values [0,255])
* @param g Series<IndexType> or number for the color red (valid values [0,255])
* @param b Series<IndexType> or number for the color red (valid values [0,255])
* @param a Series<IndexType> or number for the color red (valid values [0,255])
* @param size optional size of the series, if all of the r,g,b,a arguments are numbers. default is
* 1
* @returns Uint32Series containing RGBA integer values
*/
export function RGBASeriestoIntSeries(r: Series<IndexType>|number,
g: Series<IndexType>|number,
b: Series<IndexType>|number,
a: Series<IndexType>|number,
size = 1): Uint32Series {
size = getSize(r) || getSize(g) || getSize(b) || getSize(a) || size;
r = r instanceof Series ? validateSeries(r)
: Series.sequence({type: new Uint32, size: size, init: r, step: 0});
g = g instanceof Series ? validateSeries(g)
: Series.sequence({type: new Uint32, size: size, init: g, step: 0});
b = b instanceof Series ? validateSeries(b)
: Series.sequence({type: new Uint32, size: size, init: b, step: 0});
a = a instanceof Series ? validateSeries(a)
: Series.sequence({type: new Uint32, size: size, init: a, step: 0});
return r.shiftLeft(BigInt(24))
.bitwiseOr(g.shiftLeft(BigInt(16)))
.bitwiseOr(b.shiftLeft(BigInt(8)))
.bitwiseOr(a)
.cast(new Uint32);
}
/**
* Convert rgba to 32 bit signed integer
* @param rgba array of rgba value (a value is optional, default is 255)
* @returns rgba 32 bit signed integer value
*/
function RGBAtoInt(rgba: number[]): number {
rgba.forEach((value) => {
if (value < 0 || value > 255) { throw new RangeError('rgba values expected within [0,255]'); }
});
if (rgba.length < 3 || rgba.length > 4) {
throw new Error('invalid array length, provide values for r,g,b,a(optional)');
}
if (rgba.length == 3) { rgba = rgba.concat(255); } // if only rgb values are provided
return (rgba[0] << 24 >>> 0) + (rgba[1] << 16 >>> 0) + (rgba[2] << 8 >>> 0) + rgba[3];
}
/**
* Helper function for quickly creating a cudf color Series based on color bins provided
* @param values
* @param domain
* @param colors
* possible input values:
* - array of rgba arrays
* - array of rgb arrays (a defaults to 255)
* - array of 32bit rgba integers
* - array of hex color strings
* @param nullColor
*/
export function mapValuesToColorSeries(
values: Series<IndexType>,
domain: number[],
colors: number[][]|number[]|string[],
nullColor: number[]|number = [204, 204, 204, 255]): Uint32Series {
// validate colors and domain lengths
if (colors.length < 1 || domain.length < 1) {
throw new Error('colors and domain must be arrays of length 1 or greater');
}
// convert RGBA values to Integers accepted by deck gpu buffers
const nullColorInteger = Array.isArray(nullColor) ? RGBAtoInt(nullColor) : nullColor;
const colorsInteger: number[] =
colors.map(value => Array.isArray(value) ? RGBAtoInt(value)
: typeof value == 'string' ? hexToInteger(value)
: value);
let colorSeries =
Series.sequence({type: new Uint32, init: colorsInteger[0], step: 0, size: values.length});
const colorIndices = Series.sequence({type: values.type, init: 0, step: 1, size: values.length});
if (domain.length == 1) {
const boolMask = values.ge(domain[0]);
const indices = colorIndices.filter(boolMask);
colorSeries =
colorSeries.scatter(colorsInteger[1] || colorsInteger[colors.length - 1], indices);
} else {
for (let i = 0; i < domain.length - 1; i++) {
const boolMask = values.ge(domain[i]).logicalAnd(values.lt(domain[i + 1]));
const indices = colorIndices.filter(boolMask);
colorSeries =
colorSeries.scatter(colorsInteger[i] || colorsInteger[colors.length - 1], indices);
}
}
// handle nulls
if (values.countNonNulls() !== values.length) { // contains null values
const indices = colorIndices.filter(values.isNull());
colorSeries = colorSeries.scatter(nullColorInteger, indices);
}
return colorSeries;
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/graph.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {
DeckCompositeLayer,
DeckContext,
DeckTextLayer,
PickingInfo,
UpdateStateProps
} from '../deck.gl';
const {TextLayer} = require('@deck.gl/layers') as {TextLayer: typeof DeckTextLayer};
const {CompositeLayer} = require('@deck.gl/core');
import {Accessor} from '@luma.gl/webgl';
import {Buffer} from '../buffer';
import {EdgeLayer} from './edges';
import {EdgeColorBuffer, EdgeComponentBuffer, EdgeListBuffer} from './edges/attributes';
import {ComputeEdgePositionsTransform} from './edges/positions';
import {NodeLayer} from './nodes';
import {
NodeColorBuffer,
NodeElementIndicesBuffer,
NodePositionBuffer,
NodeRadiusBuffer
} from './nodes/attributes';
const edgeBufferNames = [
'edgeList',
'edgeBundles',
'edgeColors',
'edgeControlPoints',
'edgeSourcePositions',
'edgeTargetPositions'
];
const nodeBufferNames = [
'nodeXPositions',
'nodeYPositions',
'nodeRadius',
'nodeFillColors',
'nodeLineColors',
'nodeElementIndices'
];
export class GraphLayer extends (CompositeLayer as typeof DeckCompositeLayer) {
static get layerName() { return 'GraphLayer'; }
static get defaultProps() {
const defaultNodeProps = NodeLayer.defaultProps;
const defaultEdgeProps = EdgeLayer.defaultProps;
return {
// graph props
numNodes: {type: 'number', min: 0, value: 0},
numEdges: {type: 'number', min: 0, value: 0},
nodesVisible: {type: 'boolean', value: true},
edgesVisible: {type: 'boolean', value: true},
// edge props
edgeOpacity: defaultEdgeProps.opacity,
edgeStrokeWidth: defaultEdgeProps.width,
// node props
nodesFilled: defaultNodeProps.filled,
nodesStroked: defaultNodeProps.stroked,
nodeFillOpacity: defaultNodeProps.fillOpacity,
nodeStrokeRatio: defaultNodeProps.strokeRatio,
nodeStrokeOpacity: defaultNodeProps.strokeOpacity,
nodeRadiusScale: defaultNodeProps.radiusScale,
nodeLineWidthScale: defaultNodeProps.lineWidthScale,
nodeRadiusMinPixels: defaultNodeProps.radiusMinPixels,
nodeRadiusMaxPixels: defaultNodeProps.radiusMaxPixels,
nodeLineWidthMinPixels: defaultNodeProps.lineWidthMinPixels,
nodeLineWidthMaxPixels: defaultNodeProps.lineWidthMaxPixels,
};
}
static getAccessors(context: DeckContext) {
return {
...NodeLayer.getAccessors(context),
...EdgeLayer.getAccessors(context),
};
}
initializeState({gl}: DeckContext) {
// GPGPU
this.internalState.computeEdgePositions = new ComputeEdgePositionsTransform(gl);
this.setState({
numNodesLoaded: 0,
numEdgesLoaded: 0,
highlightedEdge: -1,
highlightedNode: -1,
highlightedSourceNode: -1,
highlightedTargetNode: -1,
edgePositionRanges: new Set(),
buffers: {
// edges
edgeList: new EdgeListBuffer(gl),
edgeBundles: new EdgeListBuffer(gl),
edgeColors: new EdgeColorBuffer(gl),
edgeControlPoints: new EdgeComponentBuffer(gl),
edgeSourcePositions: new EdgeComponentBuffer(gl),
edgeTargetPositions: new EdgeComponentBuffer(gl),
// nodes
nodeRadius: new NodeRadiusBuffer(gl),
nodeFillColors: new NodeColorBuffer(gl),
nodeLineColors: new NodeColorBuffer(gl),
nodeXPositions: new NodePositionBuffer(gl),
nodeYPositions: new NodePositionBuffer(gl),
nodeElementIndices: new NodeElementIndicesBuffer(gl),
},
});
}
shouldUpdateState({props, oldProps, changeFlags, ...rest}: UpdateStateProps) {
return changeFlags.viewportChanged ||
super.shouldUpdateState({props, changeFlags, oldProps, ...rest});
}
updateState({props, oldProps, changeFlags, ...rest}: UpdateStateProps) {
// highlight props
this.setState([
'labels',
'highlightedEdge',
'highlightedNode',
'highlightedSourceNode',
'highlightedTargetNode'
].filter((key) => key in props)
.reduce((state, key) => ({...state, [key]: props[key]}), {}));
super.updateState({props, oldProps, changeFlags, ...rest});
changeFlags = {
...changeFlags,
edgesChanged: false,
nodesChanged: false,
graphChanged: false,
numEdgesChanged: false,
numNodesChanged: false,
};
const updates = [];
if (changeFlags.dataChanged && props.data) {
updates[0] = props.data;
changeFlags.edgesChanged = updates.some((x) => !!x.edges && x.edges.length > 0);
changeFlags.nodesChanged = updates.some((x) => !!x.nodes && x.nodes.length > 0);
changeFlags.graphChanged = changeFlags.edgesChanged || changeFlags.nodesChanged;
}
if (changeFlags.propsChanged) {
if (typeof props.numEdges !== 'number') { props.numEdges = 0; }
if (typeof props.numNodes !== 'number') { props.numNodes = 0; }
changeFlags.numEdgesChanged = oldProps.numEdges !== props.numEdges;
changeFlags.numNodesChanged = oldProps.numNodes !== props.numNodes;
}
if (changeFlags.numEdgesChanged) {
resizeBuffers(props.numEdges, edgeBufferNames.map((name) => this.state.buffers[name]));
}
if (changeFlags.numNodesChanged) {
nodeBufferNames.forEach((name) => {
let length = props.numNodes;
switch (name) {
case 'nodeXPositions':
case 'nodeYPositions':
length = this.internalState.computeEdgePositions.roundSizeUpToTextureDimensions(length);
}
resizeBuffer(length, this.state.buffers[name]);
});
this.setState({numNodesLoaded: Math.min(this.state.numNodesLoaded, props.numNodes)});
}
if (changeFlags.graphChanged) {
this.setState(copyUpdatesIntoBuffers({...this.state, updates}));
}
if (props.edgesVisible && props.numEdges > 0) {
this.setState(computePendingEdgePositions({
...this.state,
...props,
...changeFlags,
computeEdgePositions: this.internalState.computeEdgePositions,
edgeBuffers: edgeBufferNames.map((name) => this.state.buffers[name]),
nodeBuffers: nodeBufferNames.map((name) => this.state.buffers[name]),
}));
}
}
serialize() {
const subLayerIdPrefixSize = `${this.props.id}-`.length;
return {
highlightedEdge: this.state.highlightedEdge,
highlightedNode: this.state.highlightedNode,
labels: this.state.labels?.filter((x: any) => x?.text),
highlightedSourceNode: this.state.highlightedSourceNode,
highlightedTargetNode: this.state.highlightedTargetNode,
_subLayerProps: (this.internalState.subLayers || [])
.reduce(
(subLayers: any, layer: any) => {
if (layer && typeof layer.serialize === 'function') {
subLayers[layer.id.slice(subLayerIdPrefixSize)] = layer.serialize();
}
return subLayers;
},
{})
};
}
finalizeState(contex?: DeckContext) {
[...edgeBufferNames, ...nodeBufferNames]
.map((name) => this.state.buffers[name])
.filter(Boolean)
.forEach((b) => b.delete());
if (this.internalState.computeEdgePositions) {
this.internalState.computeEdgePositions.delete();
this.internalState.computeEdgePositions = null;
}
return super.finalizeState(contex);
}
onHover({layer, x, y, coordinate, edgeId = -1, nodeId = -1, sourceNodeId = -1, targetNodeId = -1}:
PickingInfo&
{edgeId: number, nodeId: number, sourceNodeId: number, targetNodeId: number}) {
const nextState = {
labels: [] as any[],
highlightedEdge: edgeId,
highlightedNode: nodeId,
highlightedSourceNode: sourceNodeId,
highlightedTargetNode: targetNodeId,
};
let label = '';
if (nodeId !== -1) {
label = `${nodeId}`;
if (this.props.data.nodes.attributes.nodeName) {
label = this.props.data.nodes.attributes.nodeName.getValue(nodeId);
}
} else if (edgeId !== -1) {
label = `${sourceNodeId} - ${targetNodeId}`;
if (this.props.data.edges.attributes.edgeName) {
label = this.props.data.edges.attributes.edgeName.getValue(edgeId);
}
}
if (nodeId !== -1 && (typeof this.props.getNodeLabels === 'function')) {
nextState.labels = this.props.getNodeLabels(
{x, y, coordinate, nodeId, edgeId, sourceNodeId, targetNodeId, layer, props: this.props});
} else if (edgeId !== -1 && (typeof this.props.getEdgeLabels === 'function')) {
nextState.labels = this.props.getEdgeLabels(
{x, y, coordinate, nodeId, edgeId, sourceNodeId, targetNodeId, layer, props: this.props});
} else {
nextState.labels = [{
text: label,
size: this.props.labelTextSize || 14,
color: this.props.labelTextColor || [255, 255, 255],
position: coordinate || layer.context.viewport.unproject([x, y])
}];
}
this.setState(nextState);
}
renderLayers() {
const layers = [];
const {props, state} = this;
const maxNumElements = 16777215 / 3;
const renderChunks = (numElements: number,
LayerClass: any,
getProps: (index: number, offset: number, length: number) => any) => {
const count = Math.ceil(numElements / maxNumElements);
for (let index = -1; ++index < count;) {
const offset = (index * maxNumElements);
const length = Math.min(maxNumElements, numElements - offset);
layers.push(new LayerClass(this.getSubLayerProps(getProps(index, offset, length))));
}
};
props.edgesVisible &&
renderChunks(
this.state.numEdgesLoaded, EdgeLayer, (index, offset, length) => ({
id: `${EdgeLayer.layerName}-${index}`,
...edgeLayerProps(props, state, offset, length),
}));
// render bezier control points for debugging
// renderChunks(this.state.numEdgesLoaded, ScatterplotLayer, (index, offset, length) => ({
// id: `${ScatterplotLayer.name}-${index}`,
// numInstances: length,
// radiusScale: 2,
// data: {
// attributes: {
// instancePositions: { buffer: state.buffers.edgeControlPoints, offset: offset * 3
// }, instanceFillColors: { buffer: state.buffers.edgeColors, offset: offset * 4,
// stride: 8 },
// }
// },
// }));
props.nodesVisible &&
renderChunks(
this.state.numNodesLoaded, NodeLayer, (index, offset, length) => ({
id: `${NodeLayer.layerName}-${index}`,
...nodeLayerProps(props, state, offset, length),
}));
if (this.state.labels?.length) {
layers.push(new TextLayer(this.getSubLayerProps({
id: `${TextLayer.name}-0`,
...textLayerProps(this.props, this.state),
})));
}
return layers;
}
}
type LumaBuffer = import('@luma.gl/webgl').Buffer;
const resizeBuffer = (length: number, buffer: LumaBuffer) =>
buffer.reallocate(length * (buffer.accessor as Accessor).BYTES_PER_VERTEX);
const resizeBuffers = (length: number, buffers: LumaBuffer[]) =>
buffers.forEach((buffer) => resizeBuffer(length, buffer));
const copyIntoBuffer = (target: LumaBuffer, source: any, offset: number) => target.subData({
data: source,
srcOffset: source.byteOffset,
offset: offset * (target.accessor as Accessor).BYTES_PER_VERTEX
});
const copyUpdatesIntoBuffers = ({
buffers,
updates,
numEdgesLoaded,
numNodesLoaded,
edgePositionRanges,
}: any) => {
const updatedBufferNames = (names: string[], {attributes = {}}: any) =>
names.filter((name) => attributes[name]);
const copyUpdateIntoBuffers = (buffers: any, names: string[], update: any) => {
for (const name of updatedBufferNames(names, update)) {
copyIntoBuffer(buffers[name], update.attributes[name], update.offset);
}
};
const buffersToUpdate = [
...updates.reduce((names: string[], {edges = {}, nodes = {}}: any) => new Set([
...names,
...updatedBufferNames(edgeBufferNames, edges),
...updatedBufferNames(nodeBufferNames, nodes),
]),
new Set())
].map((name) => buffers[name]);
Buffer.mapResources(buffersToUpdate);
updates.forEach(({edges = {}, nodes = {}}: any) => {
edges.offset = Math.max(0, edges.offset || 0);
nodes.offset = Math.max(0, nodes.offset || 0);
edges.length > 0 && edgePositionRanges.add([edges.offset, edges.length]);
edges.length > 0 && copyUpdateIntoBuffers(buffers, edgeBufferNames, edges);
nodes.length > 0 && copyUpdateIntoBuffers(buffers, nodeBufferNames, nodes);
numEdgesLoaded = Math.max(numEdgesLoaded, (<number>edges.offset + <number>edges.length) || 0);
numNodesLoaded = Math.max(numNodesLoaded, (<number>nodes.offset + <number>nodes.length) || 0);
});
Buffer.unmapResources(buffersToUpdate);
return {numEdgesLoaded, numNodesLoaded, edgePositionRanges};
};
const computePendingEdgePositions = ({
buffers,
numEdges,
numNodes,
edgeBuffers,
nodeBuffers,
nodesChanged,
edgesChanged,
numEdgesLoaded,
numNodesLoaded,
edgeStrokeWidth,
edgePositionRanges,
computeEdgePositions,
}: any) => {
const allElementsLoaded = (numTotal: number, numLoaded: number, buffers: LumaBuffer[]) =>
(numTotal > 0 && numLoaded === numTotal &&
buffers.every((buffer) => numTotal === (buffer.byteLength /
(buffer.accessor as Accessor).BYTES_PER_VERTEX)));
const computeEdgePositionRanges = (ranges: any) => {
const args = {
...buffers,
edgeStrokeWidth,
offset: 0,
length: numEdges,
numNodes,
numNodesLoaded,
nodesChanged,
};
ranges.forEach(([offset, length]: [number, number]) => {
args.offset = offset;
args.length = length;
computeEdgePositions.call(args);
});
return {edgePositionRanges: new Set()};
};
// If new node positions load after logical edges, copy all the
// new node positions to the source and target edge positions.
if (nodesChanged && allElementsLoaded(numEdges, numEdgesLoaded, edgeBuffers)) {
return computeEdgePositionRanges(new Set([[0, numEdges]]));
}
if (edgesChanged && allElementsLoaded(numNodes, numNodesLoaded, nodeBuffers)) {
return computeEdgePositionRanges([...edgePositionRanges].sort(([a], [b]) => a - b));
}
return {edgePositionRanges};
};
const sliceLayerAttrib = (multiplier: number, buffer: LumaBuffer, offset = 0) =>
({buffer, offset: (buffer.accessor as Accessor).BYTES_PER_VERTEX * multiplier + offset});
const edgeLayerProps = (props: any, state: any, offset: number, length: number) => ({
pickable: true,
autoHighlight: false,
highlightColor: [225, 225, 225, 100],
numInstances: length,
opacity: props.edgeOpacity,
visible: props.edgesVisible,
width: props.edgeStrokeWidth,
highlightedNode: state.highlightedNode,
highlightedEdge: state.highlightedEdge,
highlightedSourceNode: state.highlightedSourceNode,
highlightedTargetNode: state.highlightedTargetNode,
data: {
attributes: {
instanceEdges: sliceLayerAttrib(offset, state.buffers.edgeList),
instanceSourceColors: sliceLayerAttrib(offset, state.buffers.edgeColors),
instanceTargetColors: sliceLayerAttrib(offset, state.buffers.edgeColors, 4),
instanceControlPoints: sliceLayerAttrib(offset, state.buffers.edgeControlPoints),
instanceSourcePositions: sliceLayerAttrib(offset, state.buffers.edgeSourcePositions),
instanceTargetPositions: sliceLayerAttrib(offset, state.buffers.edgeTargetPositions),
}
}
});
const nodeLayerProps = (props: any, state: any, offset: number, length: number) => ({
pickable: true,
autoHighlight: false,
highlightColor: [225, 225, 225, 100],
numInstances: length,
filled: props.nodesFilled,
stroked: props.nodesStroked,
visible: props.nodesVisible,
fillOpacity: props.nodeFillOpacity,
strokeRatio: props.nodeStrokeRatio,
strokeOpacity: props.nodeStrokeOpacity,
radiusScale: props.nodeRadiusScale,
radiusMinPixels: props.nodeRadiusMinPixels,
radiusMaxPixels: props.nodeRadiusMaxPixels,
lineWidthScale: props.nodeLineWidthScale,
lineWidthMinPixels: props.nodeLineWidthMinPixels,
lineWidthMaxPixels: props.nodeLineWidthMaxPixels,
highlightedNode: state.highlightedNode,
highlightedEdge: state.highlightedEdge,
highlightedSourceNode: state.highlightedSourceNode,
highlightedTargetNode: state.highlightedTargetNode,
data: {
attributes: {
instanceRadius: sliceLayerAttrib(offset, state.buffers.nodeRadius),
instanceFillColors: sliceLayerAttrib(offset, state.buffers.nodeFillColors),
instanceLineColors: sliceLayerAttrib(offset, state.buffers.nodeFillColors),
instanceXPositions: sliceLayerAttrib(offset, state.buffers.nodeXPositions),
instanceYPositions: sliceLayerAttrib(offset, state.buffers.nodeYPositions),
instanceXPositions64Low: sliceLayerAttrib(offset, state.buffers.nodeXPositions),
instanceYPositions64Low: sliceLayerAttrib(offset, state.buffers.nodeYPositions),
instanceNodeIndices: sliceLayerAttrib(offset, state.buffers.nodeElementIndices),
elementIndices: sliceLayerAttrib(offset, state.buffers.nodeElementIndices),
}
},
});
const textLayerProps = (_props: any, state: any) => ({
sizeScale: 1.0,
opacity: 1.0,
maxWidth: -1,
pickable: false,
background: true,
getBackgroundColor: () => [46, 46, 46],
getTextAnchor: 'start',
getAlignmentBaseline: 'top',
fontFamily: 'sans-serif, sans',
getSize: (d: any) => d.size,
getColor: (d: any) => d.color,
getPixelOffset: (d: any) => d.offset || [d.size, 0],
data: state.labels.filter((label: any) => !!label.text),
getPosition: (d: any) => typeof d.position === 'function' ? d.position() : d.position,
});
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/point-cloud.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {DeckCompositeLayer, DeckContext, UpdateStateProps} from '../deck.gl';
const {CompositeLayer} = require('@deck.gl/core');
import {Accessor} from '@luma.gl/webgl';
import {Buffer} from '../buffer';
import {PointCloudGPUBase} from './point-cloud/point-cloud-base';
import {
PointColorBuffer,
PointNormalizeBuffer,
PointPositionBuffer
} from './point-cloud/attributes';
const pointBufferNames =
['pointPositionX', 'pointPositionY', 'pointPositionZ', 'pointColor', 'pointNormal'];
export class PointCloudLayer extends (CompositeLayer as typeof DeckCompositeLayer) {
static get layerName() { return 'PointCloudLayer'; }
static get defaultProps() {
return {
numPoints: {type: 'number', min: 0, value: 0},
...PointCloudGPUBase.defaultProps,
};
}
static getAccessors(context: DeckContext) { return PointCloudGPUBase.getAccessors(context); }
initializeState({gl}: DeckContext) {
this.setState({
numPointsLoaded: 0,
buffers: {
// points
pointPositionX: new PointPositionBuffer(gl),
pointPositionY: new PointPositionBuffer(gl),
pointPositionZ: new PointPositionBuffer(gl),
pointColor: new PointColorBuffer(gl),
pointNormal: new PointNormalizeBuffer(gl)
},
});
}
shouldUpdateState({props, oldProps, changeFlags, ...rest}: UpdateStateProps) {
return changeFlags.viewportChanged ||
super.shouldUpdateState({props, changeFlags, oldProps, ...rest});
}
updateState({props, oldProps, changeFlags, ...rest}: UpdateStateProps) {
super.updateState({props, oldProps, changeFlags, ...rest});
changeFlags = {
...changeFlags,
pointsChanged: false,
numPointsChanged: false,
};
const updates = [];
if (changeFlags.dataChanged && props.data) {
updates[0] = props.data;
changeFlags.pointsChanged = updates.some((x) => !!x.points && x.points.length > 0);
}
if (changeFlags.propsChanged) {
changeFlags.numPointsChanged =
props.numPoints > 0 && (oldProps.numPoints !== props.numPoints);
}
if (changeFlags.numPointsChanged) {
resizeBuffers(props.numPoints, pointBufferNames.map((name) => this.state.buffers[name]));
}
if (changeFlags.pointsChanged) {
this.setState(copyUpdatesIntoBuffers({...this.state, updates}));
}
}
serialize() { return {}; }
finalizeState(context?: DeckContext) {
[...pointBufferNames]
.map((name) => this.state.buffers[name])
.filter(Boolean)
.forEach((b) => b.delete());
return super.finalizeState(context);
}
renderLayers() {
const layers: any = [];
const {props, state} = this;
const maxNumElements = 16777215 / 3;
const renderChunks = (numElements: number,
LayerClass: any,
getProps: (index: number, offset: number, length: number) => any) => {
const count = Math.ceil(numElements / maxNumElements);
for (let index = -1; ++index < count;) {
const offset = (index * maxNumElements);
const length = Math.min(maxNumElements, numElements - offset);
layers.push(new LayerClass(this.getSubLayerProps(getProps(index, offset, length))));
}
};
renderChunks(state.numPointsLoaded,
PointCloudGPUBase,
(index, offset, length) => ({
id: `${PointCloudGPUBase.layerName}-${index}`,
...PointCloudGPUBaseProps(props, state, offset, length),
}));
return layers;
}
}
type LumaBuffer = import('@luma.gl/webgl').Buffer;
const resizeBuffer = (length: number, buffer: LumaBuffer) =>
buffer.reallocate(length * (buffer.accessor as Accessor).BYTES_PER_VERTEX);
const resizeBuffers = (length: number, buffers: LumaBuffer[]) =>
buffers.forEach((buffer) => resizeBuffer(length, buffer));
const copyIntoBuffer = (target: LumaBuffer, source: any, offset: number) => target.subData({
data: source,
srcOffset: source.byteOffset,
offset: offset * (target.accessor as Accessor).BYTES_PER_VERTEX
});
const copyUpdatesIntoBuffers = ({buffers, updates, numPointsLoaded}: any) => {
const updatedBufferNames = (names: string[], {attributes = {}}: any) =>
names.filter((name) => attributes[name]);
const copyUpdateIntoBuffers = (buffers: any, names: string[], update: any) => {
for (const name of updatedBufferNames(names, update)) {
copyIntoBuffer(buffers[name], update.attributes[name], update.offset);
}
};
const buffersToUpdate = [
...updates.reduce((names: string[], {points = {}}: any) => new Set([
...names,
...updatedBufferNames(pointBufferNames, points),
]),
new Set())
].map((name) => buffers[name]);
Buffer.mapResources(buffersToUpdate);
updates.forEach(({points = {}}: any) => {
points.length > 0 && copyUpdateIntoBuffers(buffers, pointBufferNames, points);
numPointsLoaded = Math.max(numPointsLoaded, (<number>points.length) || 0);
});
Buffer.unmapResources(buffersToUpdate);
return {numPointsLoaded};
};
const sliceLayerAttrib = (multiplier: number, buffer: LumaBuffer, offset = 0) =>
({buffer, offset: (buffer.accessor as Accessor).BYTES_PER_VERTEX * multiplier + offset});
const PointCloudGPUBaseProps = (props: any, state: any, offset: number, length: number) => {
const pointCloudProps = {
...props,
numInstances: length,
data: {
attributes: {
instancePositionsX: sliceLayerAttrib(offset, state.buffers.pointPositionX),
instancePositionsY: sliceLayerAttrib(offset, state.buffers.pointPositionY),
instancePositionsZ: sliceLayerAttrib(offset, state.buffers.pointPositionZ),
instancePositionsX64Low: sliceLayerAttrib(offset, state.buffers.pointPositionX),
instancePositionsY64Low: sliceLayerAttrib(offset, state.buffers.pointPositionY),
instancePositionsZ64Low: sliceLayerAttrib(offset, state.buffers.pointPositionZ)
}
},
};
if (props.data.points.attributes.pointColor) {
pointCloudProps.data.attributes.instanceColors =
sliceLayerAttrib(offset, state.buffers.pointColor);
}
if (props.data.points.attributes.pointNormal) {
pointCloudProps.data.attributes.instanceNormals =
sliceLayerAttrib(offset, state.buffers.pointNormal);
}
return pointCloudProps;
};
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/nodes.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {DeckContext, DeckLayer, PickingInfo, UpdateStateProps} from '../deck.gl';
const {Layer, picking, project32} = require('@deck.gl/core');
import {Geometry, Model} from '@luma.gl/engine';
import {
nodeColorAccessor,
nodeElementIndicesAccessor,
nodePositionAccessor,
nodeRadiusAccessor,
} from './nodes/attributes';
import nodeFragmentShader from './nodes/node-fragment.glsl';
import nodeVertexShader from './nodes/node-vertex.glsl';
export class NodeLayer extends (Layer as typeof DeckLayer) {
static get layerName() { return 'NodeLayer'; }
static get defaultProps() {
return {
filled: true,
stroked: true,
strokeRatio: 0.05,
fillOpacity: 1,
strokeOpacity: 1,
radiusScale: 1,
lineWidthScale: 1,
// min point radius in pixels
radiusMinPixels: 0,
// max point radius in pixels
radiusMaxPixels: Number.MAX_SAFE_INTEGER,
lineWidthMinPixels: 0,
lineWidthMaxPixels: Number.MAX_SAFE_INTEGER,
highlightedNode: -1,
highlightedSourceNode: -1,
highlightedTargetNode: -1,
getRadius: {type: 'accessor', value: 5},
getFillColor: {type: 'accessor', value: [255, 255, 255, 255]},
getLineColor: {type: 'accessor', value: [255, 255, 255, 255]},
getXPosition: {type: 'accessor', value: (x: any) => x.positionX},
getYPosition: {type: 'accessor', value: (x: any) => x.positionY},
getNodeIndex: {type: 'accessor', value: (x: any) => x.index},
getElementIndex: {type: 'accessor', value: (x: any) => x.Elementindex}
};
}
static getAccessors({gl}: {gl: WebGLRenderingContext}) {
return {
instanceRadius: {...nodeRadiusAccessor(gl), accessor: 'getRadius'},
instanceFillColors: {...nodeColorAccessor(gl), accessor: 'getFillColor'},
instanceLineColors: {...nodeColorAccessor(gl), accessor: 'getLineColor'},
instanceXPositions: {...nodePositionAccessor(gl), accessor: 'getXPosition'},
instanceYPositions: {...nodePositionAccessor(gl), accessor: 'getYPosition'},
instanceNodeIndices: {...nodeElementIndicesAccessor(gl), accessor: 'getNodeIndex'},
elementIndices:
{...nodeElementIndicesAccessor(gl), accessor: 'getElementIndex', isIndexed: true},
};
}
initializeState(context: DeckContext) {
this.internalState.selectedNodeId = -1;
this.internalState.highlightedNodeId = -1;
this.internalState.selectedNodeIndex = -1;
this.internalState.highlightedNodeIndex = -1;
this.getAttributeManager().addInstanced(NodeLayer.getAccessors(context));
}
updateState({props, oldProps, context, changeFlags}: UpdateStateProps) {
['selectedNodeId', 'highlightedNodeId', 'selectedNodeIndex', 'highlightedNodeIndex']
.filter((key) => typeof props[key] === 'number')
.forEach((key) => this.internalState[key] = props[key]);
// if (this.internalState.highlightedNode && this.internalState.highlightedNode !== -1) {
// props.highlightedObjectIndex = this.internalState.highlightedNode;
// }
super.updateState({props, oldProps, context, changeFlags});
if (changeFlags.extensionsChanged) {
if (this.state.model) { this.state.model.delete(); }
this.setState({model: this._getModel(context)});
this.getAttributeManager().invalidateAll();
}
}
serialize() {
return {
selectedNodeId: this.internalState.selectedNodeId,
selectedNodeIndex: this.internalState.selectedNodeIndex,
highlightedNodeId: this.internalState.highlightedNodeId,
highlightedNodeIndex: this.internalState.highlightedNodeIndex,
};
}
draw({uniforms, ...rest}: {uniforms?: any, context?: DeckContext} = {}) {
this.state.model.draw({
...rest,
uniforms: {
filled: !!this.props.filled,
stroked: +(!!this.props.stroked),
fillOpacity: this.props.fillOpacity,
strokeRatio: this.props.strokeRatio,
strokeOpacity: this.props.strokeOpacity,
radiusScale: this.props.radiusScale,
lineWidthScale: this.props.lineWidthScale,
radiusMinPixels: this.props.radiusMinPixels,
radiusMaxPixels: this.props.radiusMaxPixels,
lineWidthMinPixels: this.props.lineWidthMinPixels,
lineWidthMaxPixels: this.props.lineWidthMaxPixels,
highlightedNode: this.props.highlightedNode,
highlightedSourceNode: this.props.highlightedSourceNode,
highlightedTargetNode: this.props.highlightedTargetNode,
...uniforms,
}
});
}
getPickingInfo({mode, info}: {info: PickingInfo, mode: 'hover'|'click'}) {
if (info.index === -1) {
info.nodeId = info.index;
} else if (this.internalState.highlightedNodeIndex === info.index) {
info.nodeId = this.internalState.highlightedNodeId;
} else {
const {buffer, offset = 0} = this.props.data.attributes.instanceNodeIndices;
([info.nodeId] = buffer.getData({
length: 1,
srcByteOffset: <number>offset + (info.index * buffer.accessor.BYTES_PER_VERTEX),
}));
}
this.internalState.highlightedNodeId = info.nodeId;
this.internalState.highlightedNodeIndex = info.index;
if (mode === 'click') {
this.internalState.selectedNodeId = this.internalState.highlightedNodeId;
this.internalState.selectedNodeIndex = this.internalState.highlightedNodeIndex;
}
info.object = info.index; // deck.gl uses info.object to check if item has already been added
return info;
}
_getModel({gl, shaderCache}: DeckContext) {
return new Model(gl, <any>{
id: this.props.id,
shaderCache,
modules: [project32, picking],
vs: nodeVertexShader,
fs: nodeFragmentShader,
// isIndexed: true,
isInstanced: true,
indexType: gl.UNSIGNED_INT,
geometry: new Geometry({
drawMode: gl.TRIANGLE_FAN,
vertexCount: 4,
attributes:
{positions: {size: 3, value: new Float32Array([-1, -1, 0, -1, 1, 0, 1, 1, 0, 1, -1, 0])}}
}),
});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Geometry, Model} from '@luma.gl/engine';
import {DeckContext, DeckLayer, PickingInfo, UpdateStateProps} from '../deck.gl';
import {
edgeComponentAccessor,
edgeListAccessor,
edgeSourceColorAccessor,
edgeTargetColorAccessor,
} from './edges/attributes';
import edgeFragmentShader from './edges/edge-fragment.glsl';
import edgeVertexShader from './edges/edge-vertex.glsl';
const {Layer, picking, project32} = require('@deck.gl/core');
const NUM_SEGMENTS = 40;
export class EdgeLayer extends (Layer as typeof DeckLayer) {
static get layerName() { return 'EdgeLayer'; }
static get defaultProps() {
return {
width: 1,
opacity: 1,
highlightedNode: -1,
highlightedEdge: -1,
};
}
static getAccessors({gl}: {gl: WebGLRenderingContext}) {
return {
instanceEdges: {...edgeListAccessor(gl), accessor: 'getInstanceEdge'},
instanceSourceColors: {...edgeSourceColorAccessor(gl), accessor: 'getInstanceSourceColors'},
instanceTargetColors: {...edgeTargetColorAccessor(gl), accessor: 'getInstanceTargetColors'},
instanceControlPoints: {...edgeComponentAccessor(gl), accessor: 'getInstanceControlPoints'},
instanceSourcePositions:
{...edgeComponentAccessor(gl), accessor: 'getInstanceSourcePositions'},
instanceTargetPositions:
{...edgeComponentAccessor(gl), accessor: 'getInstanceTargetPositions'},
};
}
initializeState(context: DeckContext) {
this.internalState.selectedEdgeId = -1;
this.internalState.highlightedEdgeId = -1;
this.internalState.selectedEdgeIndex = -1;
this.internalState.highlightedEdgeIndex = -1;
this.internalState.selectedSourceNodeId = -1;
this.internalState.highlightedSourceNodeId = -1;
this.internalState.selectedTargetNodeId = -1;
this.internalState.highlightedTargetNodeId = -1;
this.getAttributeManager().addInstanced(EdgeLayer.getAccessors(context));
}
updateState({props, oldProps, context, changeFlags}: UpdateStateProps) {
['selectedEdgeId',
'highlightedEdgeId',
'selectedEdgeIndex',
'highlightedEdgeIndex',
'selectedSourceNodeId',
'highlightedSourceNodeId',
'selectedTargetNodeId',
'highlightedTargetNodeId']
.filter((key) => typeof props[key] === 'number')
.forEach((key) => this.internalState[key] = props[key]);
// if (this.internalState.highlightedEdgeIndex && this.internalState.highlightedEdgeIndex !==
// -1) {
// props.highlightedObjectIndex = this.internalState.highlightedEdgeIndex;
// }
super.updateState({props, oldProps, context, changeFlags});
if (changeFlags.extensionsChanged) {
if (this.state.model) { this.state.model.delete(); }
this.setState({model: this._getModel(context)});
this.getAttributeManager().invalidateAll();
}
}
serialize() {
return {
selectedEdgeId: this.internalState.selectedEdgeId,
selectedEdgeIndex: this.internalState.selectedEdgeIndex,
highlightedEdgeId: this.internalState.highlightedEdgeId,
highlightedEdgeIndex: this.internalState.highlightedEdgeIndex,
selectedSourceNodeId: this.internalState.selectedSourceNodeId,
selectedTargetNodeId: this.internalState.selectedTargetNodeId,
highlightedSourceNodeId: this.internalState.highlightedSourceNodeId,
highlightedTargetNodeId: this.internalState.highlightedTargetNodeId,
};
}
draw({uniforms, ...rest}: {uniforms?: any, context?: DeckContext} = {}) {
this.state.model.draw({
...rest,
uniforms: {
...uniforms,
highlightedNode: this.props.highlightedNode,
highlightedEdge: this.props.highlightedEdge,
strokeWidth: Math.max(1, this.props.width || 1),
opacity: Math.max(0, Math.min(this.props.opacity, 1)),
}
});
}
getPickingInfo({mode, info}: {info: PickingInfo, mode: 'hover'|'click'}) {
if (info.index === -1) {
info.edgeId = -1;
info.sourceNodeId = -1;
info.targetNodeId = -1;
} else if (info.index === this.internalState.highlightedEdgeIndex) {
info.edgeId = this.internalState.highlightedEdgeIndex;
info.sourceNodeId = this.internalState.highlightedSourceNodeId;
info.targetNodeId = this.internalState.highlightedTargetNodeId;
} else {
info.edgeId = info.index;
const {buffer, offset = 0} = this.props.data.attributes.instanceEdges;
([info.sourceNodeId, info.targetNodeId] = buffer.getData({
length: 2,
srcByteOffset: <number>offset + (info.index * buffer.accessor.BYTES_PER_VERTEX),
}));
}
this.internalState.highlightedEdgeId = info.edgeId;
this.internalState.highlightedEdgeIndex = info.index;
this.internalState.highlightedSourceNodeId = info.sourceNodeId;
this.internalState.highlightedTargetNodeId = info.targetNodeId;
if (mode === 'click') {
this.internalState.selectedEdgeId = this.internalState.highlightedEdgeId;
this.internalState.selectedEdgeIndex = this.internalState.highlightedEdgeIndex;
this.internalState.selectedSourceNodeId = this.internalState.highlightedSourceNodeId;
this.internalState.selectedTargetNodeId = this.internalState.highlightedTargetNodeId;
}
info.object = info.index; // deck.gl uses info.object to check if item has already been added
return info;
}
_getModel({gl, shaderCache}: DeckContext) {
return new Model(gl, <any>{
id: this.props.id,
shaderCache,
modules: [project32, picking],
vs: edgeVertexShader,
fs: edgeFragmentShader,
uniforms: {numSegments: NUM_SEGMENTS},
isInstanced: true,
geometry: new Geometry({
drawMode: gl.TRIANGLE_STRIP,
attributes: {
//
// (0, -1)------------_(1, -1)
// | _,-' |
// o _,-' o
// | _,-' |
// (0, 1)-------------(1, 1)
//
positions: new Float32Array(
Array.from({length: NUM_SEGMENTS}).flatMap((_, i) => [i, -1, 0, i, 1, 0]))
},
}),
});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/point-cloud/attributes.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Buffer} from '../../buffer';
export const pointPositionAccessor = (gl: WebGLRenderingContext) => ({size: 1, type: gl.FLOAT});
export const pointColorAccessor = (gl: WebGLRenderingContext) =>
({size: 4, type: gl.UNSIGNED_BYTE, normalized: true});
export const pointNormalizeAccessor = (gl: WebGLRenderingContext) =>
({size: 3, stride: 4, offset: 0, type: gl.UNSIGNED_BYTE, transition: true});
export class PointColorBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: pointColorAccessor(gl)});
}
}
export class PointPositionBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: pointPositionAccessor(gl)});
}
}
export class PointNormalizeBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: pointNormalizeAccessor(gl)});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/point-cloud/point-cloud-layer-vertex.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME point-cloud-layer-vertex-shader
uniform float opacity;
uniform float radiusPixels;
uniform int sizeUnits;
in vec3 positions;
in vec4 instanceColors;
in vec3 instancePickingColors;
in vec3 instanceNormals;
in float instancePositionsX;
in float instancePositionsY;
in float instancePositionsZ;
in float instancePositionsX64Low;
in float instancePositionsY64Low;
in float instancePositionsZ64Low;
out vec4 vColor;
out vec2 unitPosition;
void main(void) {
geometry.worldPosition = vec3(instancePositionsX, instancePositionsY, instancePositionsZ);
geometry.normal = project_normal(instanceNormals);
// position on the containing square in [-1, 1] space
unitPosition = positions.xy;
geometry.uv = unitPosition;
geometry.pickingColor = instancePickingColors;
// Find the center of the point and add the current vertex
vec3 offset = vec3(positions.xy * project_size_to_pixel(radiusPixels, sizeUnits), 0.0);
DECKGL_FILTER_SIZE(offset, geometry);
gl_Position = project_position_to_clipspace(
vec3(instancePositionsX, instancePositionsY, instancePositionsZ),
vec3(instancePositionsX64Low, instancePositionsY64Low, instancePositionsZ64Low),
vec3(0.), geometry.position);
gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);
DECKGL_FILTER_GL_POSITION(gl_Position, geometry);
// Apply lighting
vec3 lightColor = lighting_getLightColor(instanceColors.rgb, project_uCameraPosition, geometry.position.xyz, geometry.normal);
// Apply opacity to instance color, or return instance picking color
vColor = vec4(lightColor, instanceColors.a * opacity);
DECKGL_FILTER_COLOR(vColor, geometry);
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/point-cloud/point-cloud-layer-fragment.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME point-cloud-layer-fragment-shader
precision highp float;
in vec4 vColor;
in vec2 unitPosition;
out vec4 fragColor;
void main(void) {
geometry.uv = unitPosition;
float distToCenter = length(unitPosition);
if (distToCenter > 1.0) {
discard;
}
fragColor = vColor;
DECKGL_FILTER_COLOR(fragColor, geometry);
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/point-cloud/point-cloud-base.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {DeckContext, DeckLayer, UpdateStateProps} from '../../deck.gl';
const {Layer, project32, gouraudLighting, UNIT} = require('@deck.gl/core');
import GL from '@luma.gl/constants';
import {Geometry, Model} from '@luma.gl/engine';
import {pointColorAccessor, pointNormalizeAccessor, pointPositionAccessor} from './attributes';
import fs from './point-cloud-layer-fragment.glsl';
import vs from './point-cloud-layer-vertex.glsl';
const DEFAULT_COLOR = [0, 0, 0, 255];
const DEFAULT_NORMAL = [0, 0, 1];
function normalizeData(data: any) {
const {header, attributes} = data;
if (!header || !attributes) { return; }
data.length = header.vertexCount;
if (attributes.POSITION) { attributes.instancePositions = attributes.POSITION; }
if (attributes.NORMAL) { attributes.instanceNormals = attributes.NORMAL; }
if (attributes.COLOR_0) { attributes.instanceColors = attributes.COLOR_0; }
}
export class PointCloudGPUBase extends (Layer as typeof DeckLayer) {
static get layerName() { return 'PointCloudGPUBase'; }
getShaders() { return super.getShaders({vs, fs, modules: [project32, gouraudLighting]}); }
static get defaultProps() {
return {
sizeUnits: 'pixels',
opacity: {type: 'number', min: 0, max: 1, value: 0.5},
pointSize: {type: 'number', min: 0, value: 10}, // point radius in pixels
material: true,
getPositionX: {type: 'accessor', value: (x: any) => x.positionX},
getPositionY: {type: 'accessor', value: (x: any) => x.positionY},
getPositionZ: {type: 'accessor', value: (x: any) => x.positionZ},
getColor: {type: 'accessor', value: DEFAULT_COLOR},
getNormal: {type: 'accessor', value: DEFAULT_NORMAL},
// Depreated
radiusPixels: {deprecatedFor: 'pointSize'}
};
}
static getAccessors({gl}: {gl: WebGL2RenderingContext}) {
return {
instancePositionsX: {...pointPositionAccessor(gl), accessor: 'getPositionX'},
instancePositionsY: {...pointPositionAccessor(gl), accessor: 'getPositionY'},
instancePositionsZ: {...pointPositionAccessor(gl), accessor: 'getPositionZ'},
instanceColors: {...pointColorAccessor(gl), accessor: 'getColor'},
instanceNormals: {...pointNormalizeAccessor(gl), accessor: 'getNormal'},
};
}
initializeState(context: DeckContext) {
/* eslint-disable max-len */
this.getAttributeManager().addInstanced(PointCloudGPUBase.getAccessors(context));
/* eslint-enable max-len */
}
updateState({props, oldProps, context, changeFlags}: UpdateStateProps) {
super.updateState({props, oldProps, context, changeFlags});
if (changeFlags.extensionsChanged) {
this.state.model?.delete();
this.state.model = this._getModel(context.gl);
this.getAttributeManager().invalidateAll();
}
if (changeFlags.dataChanged) { normalizeData(props.data); }
}
draw({uniforms, ...rest}: {uniforms?: any, context?: DeckContext} = {}) {
const {pointSize, sizeUnits} = this.props;
this.state.model.draw({
...rest,
uniforms: {
sizeUnits: UNIT[sizeUnits],
radiusPixels: pointSize,
...uniforms,
}
});
}
_getModel(gl: WebGL2RenderingContext) {
// a triangle that minimally cover the unit circle
const positions = [];
for (let i = 0; i < 3; i++) {
const angle = (i / 3) * Math.PI * 2;
positions.push(Math.cos(angle) * 2, Math.sin(angle) * 2, 0);
}
return new Model(gl, {
id: this.props.id,
...this.getShaders(),
isInstanced: true,
geometry: new Geometry(
{drawMode: GL.TRIANGLES, attributes: {positions: new Float32Array(positions)}}),
});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/nodes/node-vertex.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME node-vertex-shader
precision highp float;
uniform bool filled;
uniform float stroked;
uniform float fillOpacity;
uniform float strokeRatio;
uniform float strokeOpacity;
uniform float radiusScale;
uniform float radiusMinPixels;
uniform float radiusMaxPixels;
uniform float lineWidthScale;
uniform float lineWidthMinPixels;
uniform float lineWidthMaxPixels;
uniform uint highlightedNode;
uniform uint highlightedSourceNode;
uniform uint highlightedTargetNode;
in vec3 positions;
in float instanceRadius;
in uint instanceNodeIndices;
// in float instanceLineWidths;
in vec4 instanceFillColors;
in vec4 instanceLineColors;
in float instanceXPositions;
in float instanceYPositions;
in float instanceXPositions64Low;
in float instanceYPositions64Low;
in vec3 instancePickingColors;
out vec4 vFillColor;
out vec4 vLineColor;
out vec2 unitPosition;
out float innerUnitRadius;
out float outerRadiusPixels;
void main(void) {
geometry.worldPosition = vec3(instanceXPositions, instanceYPositions, 0.);
// Multiply out radius and clamp to limits
outerRadiusPixels = project_size_to_pixel(instanceRadius * radiusScale);
outerRadiusPixels = clamp(outerRadiusPixels, radiusMinPixels, radiusMaxPixels);
// Multiply out line width and clamp to limits
float lineWidthPixels = 0.;
lineWidthPixels = outerRadiusPixels * strokeRatio * lineWidthScale;
lineWidthPixels = clamp(lineWidthPixels, lineWidthMinPixels, lineWidthMaxPixels);
// outer radius needs to offset by half stroke width
outerRadiusPixels += stroked * lineWidthPixels / 2.0;
// position on the containing square in [-1, 1] space
unitPosition = positions.xy;
geometry.uv = unitPosition;
geometry.pickingColor = instancePickingColors;
innerUnitRadius = 1.0 - stroked * lineWidthPixels / outerRadiusPixels;
vec3 offset = positions * project_pixel_size(outerRadiusPixels);
DECKGL_FILTER_SIZE(offset, geometry);
gl_Position = project_position_to_clipspace(
vec3(instanceXPositions, instanceYPositions, 0.),
vec3(instanceXPositions64Low, instanceYPositions64Low, 0.),
offset, geometry.position
);
DECKGL_FILTER_GL_POSITION(gl_Position, geometry);
// Apply opacity to instance color, or return instance picking color
vFillColor = vec4(instanceFillColors.bgr, fillOpacity);
DECKGL_FILTER_COLOR(vFillColor, geometry);
vLineColor = vec4(instanceLineColors.bgr, strokeOpacity);
DECKGL_FILTER_COLOR(vLineColor, geometry);
picking_vRGBcolor_Avalid.a = float(
bool(picking_vRGBcolor_Avalid.a) ||
instanceNodeIndices == highlightedNode ||
instanceNodeIndices == highlightedSourceNode ||
instanceNodeIndices == highlightedTargetNode );
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/nodes/attributes.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Buffer} from '../../buffer';
export const nodePositionAccessor = (gl: WebGLRenderingContext) => ({size: 1, type: gl.FLOAT});
export const nodeColorAccessor = (gl: WebGLRenderingContext) =>
({size: 4, type: gl.UNSIGNED_BYTE, normalized: true});
export const nodeRadiusAccessor = (gl: WebGLRenderingContext) =>
({size: 1, type: gl.UNSIGNED_BYTE});
export const nodeElementIndicesAccessor = (gl: WebGLRenderingContext) =>
({size: 1, type: gl.UNSIGNED_INT});
export class NodeColorBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: nodeColorAccessor(gl)});
}
}
export class NodeRadiusBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: nodeRadiusAccessor(gl)});
}
}
export class NodePositionBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: nodePositionAccessor(gl)});
}
}
export class NodeElementIndicesBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: nodeElementIndicesAccessor(gl)});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/nodes/node-fragment.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME node-fragment-shader
precision highp float;
uniform bool filled;
uniform float stroked;
in vec4 vFillColor;
in vec4 vLineColor;
in vec2 unitPosition;
in float innerUnitRadius;
in float outerRadiusPixels;
out vec4 fragColor;
void main(void) {
geometry.uv = unitPosition;
float distToCenter = length(unitPosition) * outerRadiusPixels;
float inCircle = smoothedge(distToCenter, outerRadiusPixels);
if (inCircle == 0.0) {
discard;
}
if (stroked > 0.5) {
float isLine = smoothedge(innerUnitRadius * outerRadiusPixels, distToCenter);
if (filled) {
fragColor = mix(vFillColor, vLineColor, isLine);
} else {
if (isLine == 0.0) {
discard;
}
fragColor = vec4(vLineColor.rgb, vLineColor.a * isLine);
}
} else if (filled) {
fragColor = vFillColor;
} else {
discard;
}
fragColor.a *= inCircle;
DECKGL_FILTER_COLOR(fragColor, geometry);
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges/edge-fragment.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME edge-fragment-shader
precision highp float;
in vec4 vColor;
in float vDiscard;
out vec4 fragColor;
void main(void) {
if (vDiscard > 0.5) {
discard;
}
fragColor = vColor;
// use highlight color if this fragment belongs to the selected object.
fragColor = picking_filterHighlightColor(fragColor);
// use picking color if rendering to picking FBO.
fragColor = picking_filterPickingColor(fragColor);
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges/edge-vertex.glsl.ts
|
// Copyright (c) 2015 - 2017 Uber Technologies, Inc.
//
// 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.
export default `\
#version 300 es
#define SHADER_NAME edge-vertex-shader
precision highp float;
uniform float numSegments;
uniform float strokeWidth;
uniform float opacity;
uniform uint highlightedNode;
uniform uint highlightedEdge;
in vec3 positions;
in uvec2 instanceEdges;
in vec3 instanceSourcePositions;
in vec3 instanceTargetPositions;
in vec3 instanceControlPoints;
in vec4 instanceSourceColors;
in vec4 instanceTargetColors;
in vec3 instancePickingColors;
out vec4 vColor;
out float vDiscard;
// offset vector by strokeWidth pixels
// offset_direction is -1 (left) or 1 (right)
vec2 getExtrusionOffset(vec2 line_clipspace, float offset_direction) {
// normalized direction of the line
vec2 dir_screenspace = normalize(line_clipspace * project_uViewportSize);
// rotate by 90 degrees
dir_screenspace = vec2(-dir_screenspace.y, dir_screenspace.x);
// vec2 offset_screenspace = dir_screenspace * offset_direction * (strokeWidth * project_uScale) * .25;
vec2 offset_screenspace = dir_screenspace * offset_direction * strokeWidth / 2.0;
vec2 offset_clipspace = project_pixel_size_to_clipspace(offset_screenspace).xy;
return offset_clipspace;
}
float getSegmentRatio(float index) {
return smoothstep(0.0, 1.0, index / numSegments);
}
vec4 computeBezierCurve(vec4 source, vec4 target, vec4 controlPoint, float segmentRatio) {
float mt = 1.0 - segmentRatio;
float mt2 = pow(mt, 2.0);
float t2 = pow(segmentRatio, 2.0);
// quadratic curve
float a = mt2;
float b = mt * segmentRatio * 2.0;
float c = t2;
// TODO: if depth is not needed remove z computations.
vec4 ret = vec4(
a * source.x + b * controlPoint.x + c * target.x,
a * source.y + b * controlPoint.y + c * target.y,
a * source.z + b * controlPoint.z + c * target.z,
1.0
);
return ret;
}
void main(void) {
vDiscard = instanceSourcePositions.z + instanceTargetPositions.z;
// Position
vec3 sourcePos = project_position(instanceSourcePositions);
vec3 targetPos = project_position(instanceTargetPositions);
vec3 controlPointPos = project_position(instanceControlPoints);
vec4 source = project_common_position_to_clipspace(vec4(sourcePos, 1.0));
vec4 target = project_common_position_to_clipspace(vec4(targetPos, 1.0));
vec4 controlPoint = project_common_position_to_clipspace(vec4(controlPointPos, 1.0));
// linear interpolation of source & target to pick right coord
float segmentIndex = positions.x;
float segmentRatio = getSegmentRatio(segmentIndex);
vec4 p0 = computeBezierCurve(source, target, controlPoint, segmentRatio);
// next point
float indexDir = mix(-1.0, 1.0, step(segmentIndex, 0.0));
float nextSegmentRatio = getSegmentRatio(segmentIndex + indexDir);
vec4 p2 = computeBezierCurve(source, target, controlPoint, nextSegmentRatio);
// extrude
float direction = positions.y;
direction = mix(-1.0, 1.0, step(segmentIndex, 0.0)) * direction;
vec2 offset = getExtrusionOffset(p2.xy - p0.xy, direction);
gl_Position = p0 + vec4(offset, 0.0, 0.0);
// Color
vColor = mix(instanceSourceColors, instanceTargetColors, segmentRatio);
vColor = vec4(vColor.bgr, vColor.a * opacity) / 255.;
// Set color to be rendered to picking fbo (also used to check for selection highlight).
picking_setPickingColor(instancePickingColors);
picking_vRGBcolor_Avalid.a = float(bool(picking_vRGBcolor_Avalid.a)
// Highlight if this edge originates from the highlighted node
|| instanceEdges.x == highlightedNode
// Highlight if this edge terminates at the highlighted node
// || instanceEdges.y == highlightedNode
// Highlight if this edge is the hovered/selected edge
|| uint(gl_InstanceID) == highlightedEdge
);
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges/attributes.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Texture2D} from '@luma.gl/webgl';
import {Buffer} from '../../buffer';
export const edgeListAccessor = (gl: WebGLRenderingContext) => ({size: 2, type: gl.UNSIGNED_INT});
export const edgeComponentAccessor = (gl: WebGLRenderingContext) => ({size: 3, type: gl.FLOAT});
export const edgeSourceColorAccessor = (gl: WebGLRenderingContext) =>
({size: 4, stride: 8, offset: 0, type: gl.UNSIGNED_BYTE});
export const edgeTargetColorAccessor = (gl: WebGLRenderingContext) =>
({size: 4, stride: 8, offset: 4, type: gl.UNSIGNED_BYTE});
export class EdgeListBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: edgeListAccessor(gl)});
}
}
export class EdgeColorBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: {...edgeSourceColorAccessor(gl), size: 8}});
}
}
export class EdgeComponentBuffer extends Buffer {
constructor(gl: WebGLRenderingContext, byteLength = 0) {
byteLength = Math.max(byteLength || 0, 1);
super(gl, {byteLength, accessor: edgeComponentAccessor(gl)});
}
}
// Transform feedback buffers
export class EdgePositionTexture extends Texture2D {
constructor(gl: WebGL2RenderingContext) {
super(gl, {
format: gl.R32F,
type: gl.FLOAT,
width: 1,
height: 1,
parameters: {
[gl.TEXTURE_MIN_FILTER]: [gl.NEAREST],
[gl.TEXTURE_MAG_FILTER]: [gl.NEAREST],
[gl.TEXTURE_WRAP_S]: [gl.CLAMP_TO_EDGE],
[gl.TEXTURE_WRAP_T]: [gl.CLAMP_TO_EDGE],
},
mipmaps: false
});
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges/compute-position-vertex.glsl.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
export default `\
#version 300 es
#define SHADER_NAME compute-position-vertex
precision highp float;
precision highp sampler2D;
uniform float strokeWidth;
uniform uint textureWidth;
uniform uint numNodesLoaded;
uniform sampler2D nodeXPositions;
uniform sampler2D nodeYPositions;
in uvec2 edge;
in uvec2 bundle;
out vec3 controlPoint;
out vec3 sourcePosition;
out vec3 targetPosition;
ivec2 getTexCoord(uint id) {
uint y = id / textureWidth;
uint x = id - textureWidth * y;
return ivec2(x, y);
}
void main(void) {
uint source = min(edge.x, edge.y);
uint target = max(edge.x, edge.y);
controlPoint = vec3(0., 0., 1.);
sourcePosition = vec3(0., 0., 1.);
targetPosition = vec3(0., 0., 1.);
if (source < numNodesLoaded) {
ivec2 xIdx = getTexCoord(source);
sourcePosition = vec3(
texelFetch(nodeXPositions, xIdx, 0).x,
texelFetch(nodeYPositions, xIdx, 0).x,
0.
);
}
if (target < numNodesLoaded) {
ivec2 yIdx = getTexCoord(target);
targetPosition = vec3(
texelFetch(nodeXPositions, yIdx, 0).x,
texelFetch(nodeYPositions, yIdx, 0).x,
0.
);
}
// Compute the quadratic bezier control point for this edge
if ((sourcePosition.z + targetPosition.z) < 1.0) {
vec3 diff = normalize(targetPosition - sourcePosition);
vec3 midp = (targetPosition + sourcePosition) * .5;
vec3 unit = vec3(-diff.y, diff.x, 1.);
float stroke = strokeWidth;
float maxBundleSize = length(targetPosition - sourcePosition) * 0.25;
float eindex = float(bundle.x);
float bcount = float(bundle.y);
float direction = 1.0; // all edges will bend in the same direction
float size = mix(maxBundleSize * .5, maxBundleSize * 1.5, eindex / bcount);
controlPoint = vec3((midp + (unit * size * direction)).xy, 0.);
}
}
`;
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src/layers
|
rapidsai_public_repos/node/modules/deck.gl/src/layers/edges/positions.ts
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
const {getParameters} = require('@luma.gl/core');
import {Accessor, Buffer} from '@luma.gl/webgl';
import {Transform} from '@luma.gl/engine';
import {Texture2D} from '@luma.gl/webgl';
import {EdgeComponentBuffer, EdgeListBuffer, EdgePositionTexture} from './attributes';
import computePositionVertexShader from './compute-position-vertex.glsl';
interface CallComputeEdgePositionsProps {
offset?: number;
length?: number;
numNodes?: number;
nodesChanged?: boolean;
numNodesLoaded?: number;
strokeWidth?: number;
edgeList: EdgeListBuffer;
edgeBundles: Buffer;
nodeXPositions: Buffer;
nodeYPositions: Buffer;
edgeControlPoints: EdgeComponentBuffer;
edgeSourcePositions: EdgeComponentBuffer;
edgeTargetPositions: EdgeComponentBuffer;
}
export interface ComputeEdgePositionsTransform {
gl: WebGLRenderingContext;
}
export class ComputeEdgePositionsTransform extends Transform {
constructor(gl: WebGL2RenderingContext) {
super(gl, <any>{
elementCount: 0,
isInstanced: false,
vs: computePositionVertexShader,
uniforms: {
textureWidth: 1,
nodeXPositions: new EdgePositionTexture(gl),
nodeYPositions: new EdgePositionTexture(gl),
},
varyings: ['controlPoint', 'sourcePosition', 'targetPosition'],
feedbackBuffers: {
controlPoint: new EdgeComponentBuffer(gl, 1),
sourcePosition: new EdgeComponentBuffer(gl, 1),
targetPosition: new EdgeComponentBuffer(gl, 1),
},
});
this.gl = gl;
}
getTextureWidth(size: number) {
return Math.min((size + 7) & ~7, getParameters(this.gl, this.gl.MAX_TEXTURE_SIZE));
}
roundSizeUpToTextureDimensions(size: number) {
const length = (size + 7) & ~7;
const width = this.getTextureWidth(length);
return width * Math.ceil(length / width); // width * height;
}
call({
offset = 0,
length = 0,
numNodes = 0,
nodesChanged = false,
numNodesLoaded = numNodes,
strokeWidth = 1,
edgeList,
edgeBundles,
nodeXPositions,
nodeYPositions,
edgeControlPoints,
edgeSourcePositions,
edgeTargetPositions,
}: CallComputeEdgePositionsProps) {
if (length <= 0) return;
const textureWidth = this.getTextureWidth(numNodes);
const internalControlPoints = this.getBuffer('controlPoint');
const internalSourcePositions = this.getBuffer('sourcePosition');
const internalTargetPositions = this.getBuffer('targetPosition');
const {
nodeXPositions: nodeXPositionsTexture,
nodeYPositions: nodeYPositionsTexture,
} = this.model.getUniforms();
// resize internal edge component buffers
resizeBuffer(length, internalControlPoints);
resizeBuffer(length, internalSourcePositions);
resizeBuffer(length, internalTargetPositions);
// resize x and y node position textures
resizeTexture(nodeXPositionsTexture, textureWidth, numNodes);
resizeTexture(nodeYPositionsTexture, textureWidth, numNodes);
// copy x and y node positions into place
if (nodesChanged) {
setSubImageData(nodeXPositionsTexture, nodeXPositions, offset);
setSubImageData(nodeYPositionsTexture, nodeYPositions, offset);
}
this.update({elementCount: length, sourceBuffers: {edge: edgeList, bundle: edgeBundles}});
this.run({offset, uniforms: {numNodesLoaded, strokeWidth, textureWidth}});
copyDtoD(edgeControlPoints, internalControlPoints, offset, length);
copyDtoD(edgeSourcePositions, internalSourcePositions, offset, length);
copyDtoD(edgeTargetPositions, internalTargetPositions, offset, length);
}
}
const resizeBuffer = (length: number, buffer: Buffer) =>
buffer.reallocate(length * (buffer.accessor as Accessor).BYTES_PER_VERTEX);
const resizeTexture = (texture: Texture2D, width: number, length: number) => {
const height = Math.ceil(length / width);
if (texture.width !== width || texture.height !== height) { //
texture.resize({width, height});
}
};
const setSubImageData =
(texture: Texture2D, {handle: data, accessor, byteLength}: Buffer, offset = 0) =>
texture.setSubImageData({
data,
offset: offset * (accessor as Accessor).BYTES_PER_VERTEX,
height: Math.ceil((byteLength / (accessor as Accessor).BYTES_PER_VERTEX) / texture.width),
});
const copyDtoD = (target: any, source: any, offset: number, length: number) => target.copyData({
sourceBuffer: source,
size: length * target.accessor.BYTES_PER_VERTEX,
writeOffset: offset * target.accessor.BYTES_PER_VERTEX,
});
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/ssr/animation-loop.ts
|
// Copyright (c) 2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {AnimationLoop as DeckAnimationLoop} from '@luma.gl/engine';
import {setParameters} from '@luma.gl/gltools';
import {cancelAnimationFrame, requestAnimationFrame} from '@luma.gl/webgl';
const {
start,
_getPageLoadPromise,
_initializeCallbackData,
} = <any>DeckAnimationLoop.prototype;
export class AnimationLoop extends DeckAnimationLoop {
declare private _sync: boolean;
declare private _running: boolean;
declare private _initialized: boolean;
declare private _animationProps: any;
declare private _pageLoadPromise: any;
declare private _animationFrameId: any;
constructor(props = {}) {
super(props);
this._sync = (<any>props)._sync || false;
this._animationProps = (<any>props).animationProps;
(<any>this.props)._onBeforeRender = (<any>props)._onBeforeRender;
(<any>this.props)._onAfterRender = (<any>props)._onAfterRender;
}
start(opts = {}): this {
if (this._initialized && !this._running) {
this._running = true;
(<any>this)._startLoop();
return this;
}
return start.call(this, opts);
}
pause() {
if (this._running) {
this._running = false;
if (this._animationFrameId) { cancelAnimationFrame(this._animationFrameId); }
}
return this;
}
onBeforeRender(animationProps: any) {
const {_onBeforeRender} = (<any>this.props);
_onBeforeRender && _onBeforeRender(animationProps);
}
_renderFrame(animationProps: any) {
if (this.framebuffer) { //
setParameters(this.gl, {framebuffer: this.framebuffer});
}
this.onBeforeRender(animationProps);
this.onRender(animationProps);
this.onAfterRender(animationProps);
}
onAfterRender(animationProps: any) {
const {_onAfterRender} = (<any>this.props);
_onAfterRender && _onAfterRender(animationProps);
}
_getPageLoadPromise() {
if (this._sync) {
if (!this._pageLoadPromise) {
this._pageLoadPromise =
new ImmediatePromise(typeof document !== 'undefined' ? document : {});
}
return this._pageLoadPromise;
}
return _getPageLoadPromise.call(this);
}
_requestAnimationFrame(renderFrameCallback: any) {
return this._running ? requestAnimationFrame(renderFrameCallback) : undefined;
}
_initializeCallbackData() {
const animationProps = _initializeCallbackData.call(this) ?? this.animationProps;
if (animationProps && this._animationProps) {
const {
useDevicePixels = animationProps.useDevicePixels,
needsRedraw = animationProps.needsRedraw,
startTime = animationProps.startTime,
engineTime = animationProps.engineTime,
width = animationProps.width,
height = animationProps.height,
aspect = animationProps.aspect,
tick = animationProps.tick,
tock = animationProps.tock,
time = animationProps.time,
_mousePosition = animationProps._mousePosition,
} = this._animationProps;
animationProps.useDevicePixels = useDevicePixels;
animationProps.needsRedraw = needsRedraw;
animationProps.startTime = startTime;
animationProps.engineTime = engineTime;
animationProps.width = width;
animationProps.height = height;
animationProps.aspect = aspect;
animationProps.tick = tick;
animationProps.tock = tock;
animationProps.time = time;
animationProps._mousePosition = _mousePosition;
}
return animationProps;
}
restore(animationProps: any = {}) {
if (animationProps && this.animationProps) { //
Object.assign(this.animationProps, animationProps);
}
return this;
}
serialize() {
const props = this.animationProps as any;
return Object.keys(props).reduce((result: any, key) => {
const value = props[key];
switch (value && typeof value) {
case null:
case undefined:
case 'number':
case 'string':
case 'boolean': {
result[key] = value;
break;
}
default: break;
}
return result;
}, {_mousePosition: props._mousePosition});
}
}
class ImmediatePromise {
declare private _value: any;
declare private _error: any;
constructor(value: any = undefined, error: any = undefined) {
this._value = value;
this._error = error;
}
catch(onError?: (error: any) => any) {
if (this._error) {
try {
if (typeof onError === 'function') {
const x = onError(this._error);
return (typeof x?.then === 'function') ? x : new ImmediatePromise(x);
}
} catch (e1) { return new ImmediatePromise(undefined, e1); }
}
return this;
}
then(onValue?: (value: any) => any, onError?: (error: any) => any) {
try {
if (typeof onValue === 'function') {
const x = onValue(this._value);
return (typeof x?.then === 'function') ? x : new ImmediatePromise(x);
}
} catch (e1) {
if (typeof onError === 'function') {
try {
const x = onError(e1);
return (typeof x?.then === 'function') ? x : new ImmediatePromise(x);
} catch (e2) { return new ImmediatePromise(undefined, e2); }
} else {
return new ImmediatePromise(undefined, e1);
}
}
return this;
}
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl/src
|
rapidsai_public_repos/node/modules/deck.gl/src/ssr/deck.ts
|
// Copyright (c) 2021-2023, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Deck as DeckGL} from '../deck.gl';
const {Deck: BaseDeck} = require('@deck.gl/core');
import {createGLContext} from '@luma.gl/gltools';
import {AnimationLoop} from './animation-loop';
export class Deck extends (BaseDeck as typeof DeckGL) {
constructor(props: any) {
super(props);
const {
Controller,
MapController,
_GlobeController,
FirstPersonController,
OrbitController,
OrthographicController,
} = require('@deck.gl/core');
Controller.prototype._getTransitionProps = () => ({transitionDuration: 0});
MapController.prototype._getTransitionProps = () => ({transitionDuration: 0});
_GlobeController.prototype._getTransitionProps = () => ({transitionDuration: 0});
FirstPersonController.prototype._getTransitionProps = () => ({transitionDuration: 0});
OrbitController.prototype._getTransitionProps = () => ({transitionDuration: 0});
OrthographicController.prototype._getTransitionProps = () => ({transitionDuration: 0});
}
_createAnimationLoop(props: any) {
const {
_sync,
width,
height,
gl,
glOptions,
debug,
onError,
onBeforeAnimationFrameRender,
onAfterAnimationFrameRender,
animationProps,
useDevicePixels,
createFramebuffer,
autoResizeDrawingBuffer
} = props;
const getFramebufferFromLoop = createFramebuffer && !props._framebuffer;
const loop = new AnimationLoop({
_sync,
width,
height,
animationProps,
useDevicePixels,
createFramebuffer,
autoResizeDrawingBuffer,
autoResizeViewport: false,
gl,
onCreateContext: (opts: any) => createGLContext({
...glOptions,
...opts,
canvas: this.canvas,
debug,
onContextLost: () => this._onContextLost()
}),
onInitialize: ({gl}: any) => this._setGLContext(gl),
onRender: this._onRenderFrame.bind(this),
onError,
_onBeforeRender: onBeforeAnimationFrameRender,
_onAfterRender: onAfterAnimationFrameRender,
});
if (getFramebufferFromLoop) {
const {_createFramebuffer} = <any>loop;
(<any>loop)._createFramebuffer = () => {
_createFramebuffer.call(loop);
this.setProps({_framebuffer: loop.framebuffer});
};
}
return loop;
}
_onViewStateChange(params: any) {
this.props.initialViewState = {...this.props.initialViewState, ...params.viewState};
super._onViewStateChange(params);
}
_onInteractionStateChange(interactionState: any) {
return super._onInteractionStateChange(
Object.assign(this._interactionState ||= {}, interactionState));
}
restore(state: any) {
if ('width' in state) { this.width = state.width; }
if ('height' in state) { this.height = state.height; }
if (state.metrics) { this.metrics = {...this.metrics, ...state.metrics}; }
if ('_metricsCounter' in state) { this._metricsCounter = state._metricsCounter; }
if (state._pickRequest) { this._pickRequest = {...this._pickRequest, ...state._pickRequest}; }
if (state.interactionState) {
this._interactionState = {...this._interactionState, ...state.interactionState};
}
if (state._lastPointerDownInfo) {
this._lastPointerDownInfo = {...this._lastPointerDownInfo, ...state._lastPointerDownInfo};
}
if ('animationProps' in state && this.animationLoop) {
this.animationLoop.restore(state.animationProps);
}
if (state.deckPicker && this.deckPicker) {
this.deckPicker.lastPickedInfo = {
...this.deckPicker.lastPickedInfo,
index: state.deckPicker.lastPickedInfo?.index,
layerId: state.deckPicker.lastPickedInfo?.layerId,
info: {
...this.deckPicker.lastPickedInfo?.info,
layer: {
...this.deckPicker.lastPickedInfo?.info?.layer,
...state.deckPicker.lastPickedInfo?.info?.layer,
},
viewport: {
...this.deckPicker.lastPickedInfo?.info?.viewport,
...state.deckPicker.lastPickedInfo?.info?.viewport,
},
}
};
}
if (state.props) {
const {props} = state;
if (props.initialViewState) {
if (props.initialViewState.minZoom === null) { delete props.initialViewState.minZoom; }
if (props.initialViewState.maxZoom === null) { delete props.initialViewState.maxZoom; }
}
this.setProps(props);
}
return this;
}
serialize() {
return {
width: this.width,
height: this.height,
_metricsCounter: this._metricsCounter,
metrics: this.metrics ? {...this.metrics} : undefined,
_pickRequest: this._pickRequest ? {...this._pickRequest} : undefined,
animationProps: this.animationLoop ? this.animationLoop.serialize() : undefined,
interactionState: this._interactionState ? {...this._interactionState} : undefined,
_lastPointerDownInfo: serializePickingInfo(this._lastPointerDownInfo),
deckPicker: this.deckPicker.lastPickedInfo && {
lastPickedInfo: serializeLastPickedInfo(this),
},
props: {
debug: this.props.debug,
_animate: this.props._animate,
_pickable: this.props._pickable,
touchAction: this.props.touchAction,
useDevicePixels: this.props.useDevicePixels,
initialViewState: this.props.initialViewState,
drawPickingColors: this.props.drawPickingColors,
_typedArrayManagerProps: this.props._typedArrayManagerProps,
},
};
}
}
function serializePickingInfo(info: any) {
const {layer, viewport, sourceLayer} = info || {};
return info && {
x: info.x,
y: info.y,
index: info.index,
color: info.color,
picked: info.picked,
coordinate: info.coordinate,
devicePixel: info.devicePixel,
layer: layer && {id: layer.id},
viewport: viewport && {id: viewport.id},
sourceLayer: sourceLayer && {id: sourceLayer.id},
};
}
function serializeLastPickedInfo({deckPicker}: any) {
const {lastPickedInfo} = deckPicker || {};
const {index, layerId, info} = lastPickedInfo || {};
return lastPickedInfo && {
index,
layerId,
info: serializePickingInfo(info),
};
}
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/test/deckgl-tests.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Series, Uint32, Uint8} from '@rapidsai/cudf';
import {mapValuesToColorSeries, RGBASeriestoIntSeries} from '@rapidsai/deck.gl';
describe('DeckGL-color-utils', () => {
test('test RGBASeriesToIntSeries', () => {
const r = Series.new({type: new Uint32, data: [255, 167, 123]});
const g = Series.new({type: new Uint32, data: [255, 0, 123]});
const b = Series.new({type: new Uint32, data: [255, 125, 123]});
const a = Series.new({type: new Uint32, data: [255, 0, 125]});
const badR = Series.new({type: new Uint32, data: [-1, 167, 256]});
const expectedA = Series.new({type: new Uint32, data: [4294967295, 2801827072, 2071690109]});
const expectedB = Series.new({type: new Uint32, data: [4294967295, 2801827327, 2071690239]});
expect([...RGBASeriestoIntSeries(r, g, b, a)]).toEqual([...expectedA]);
expect([...RGBASeriestoIntSeries(r, g, b, 255)]).toEqual([...expectedB]);
expect(() => {[...RGBASeriestoIntSeries(badR, g, b, 255)]}).toThrow(RangeError);
});
test('test mapValuesToColorSeries', () => {
const values = Series.new({type: new Uint8, data: [1, 5, 19, 24, 23, 32, 50, null]});
const domain = [1, 10, 30, 40];
// Integer colors: 4278190335, 4227793151, 1835007, 16712447, 4278243071
const colors = [[255, 0, 0], [251, 255, 0], [0, 27, 255], [0, 255, 2], [255, 0, 206]];
const colorsHex = ['#ff0000', '#fbff00', '#001bff', '00ff02', 'ff00ce'];
const resultColors = Series.new({
type: new Uint32,
data: [
4278190335,
4278190335,
4227793151,
4227793151,
4227793151,
1835007,
4278190335,
3435973887 // default null Color
]
})
expect([...mapValuesToColorSeries(values, domain, colors)]).toEqual([...resultColors]);
expect([...mapValuesToColorSeries(values, domain, colorsHex)]).toEqual([...resultColors]);
})
});
| 0 |
rapidsai_public_repos/node/modules/deck.gl
|
rapidsai_public_repos/node/modules/deck.gl/test/tsconfig.json
|
{
"extends": "../tsconfig.json",
"include": [
"../src/**/*.ts",
"../test/**/*.ts"
],
"compilerOptions": {
"target": "esnext",
"module": "commonjs",
"allowJs": true,
"importHelpers": false,
"noEmitHelpers": false,
"noEmitOnError": false,
"sourceMap": false,
"inlineSources": false,
"inlineSourceMap": false,
"downlevelIteration": false,
"baseUrl": "../",
"paths": {
"@rapidsai/deck.gl": ["src/index"],
"@rapidsai/deck.gl/*": ["src/*"]
}
}
}
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/package.json
|
{
"name": "@rapidsai/cudf",
"version": "22.12.2",
"description": "cuDF - NVIDIA RAPIDS GPU DataFrame Library",
"license": "Apache-2.0",
"main": "index.js",
"types": "build/js",
"author": "NVIDIA, Inc. (https://nvidia.com/)",
"maintainers": [
"Paul Taylor <[email protected]>"
],
"homepage": "https://github.com/rapidsai/node/tree/main/modules/cudf#readme",
"bugs": {
"url": "https://github.com/rapidsai/node/issues"
},
"repository": {
"type": "git",
"url": "git+https://github.com/rapidsai/node.git"
},
"scripts": {
"install": "npx rapidsai-install-native-module",
"clean": "rimraf build doc compile_commands.json",
"doc": "rimraf doc && typedoc --options typedoc.js",
"test": "node -r dotenv/config node_modules/.bin/jest -c jest.config.js",
"build": "yarn tsc:build && yarn cpp:build",
"build:debug": "yarn tsc:build && yarn cpp:build:debug",
"compile": "yarn tsc:build && yarn cpp:compile",
"compile:debug": "yarn tsc:build && yarn cpp:compile:debug",
"rebuild": "yarn tsc:build && yarn cpp:rebuild",
"rebuild:debug": "yarn tsc:build && yarn cpp:rebuild:debug",
"cpp:clean": "npx cmake-js clean -O build/Release",
"cpp:clean:debug": "npx cmake-js clean -O build/Debug",
"cpp:build": "npx cmake-js build -g -O build/Release",
"cpp:build:debug": "npx cmake-js build -g -D -O build/Debug",
"cpp:compile": "npx cmake-js compile -g -O build/Release",
"postcpp:compile": "npx rapidsai-merge-compile-commands",
"cpp:compile:debug": "npx cmake-js compile -g -D -O build/Debug",
"postcpp:compile:debug": "npx rapidsai-merge-compile-commands",
"cpp:configure": "npx cmake-js configure -g -O build/Release",
"postcpp:configure": "npx rapidsai-merge-compile-commands",
"cpp:configure:debug": "npx cmake-js configure -g -D -O build/Debug",
"postcpp:configure:debug": "npx rapidsai-merge-compile-commands",
"cpp:rebuild": "npx cmake-js rebuild -g -O build/Release",
"postcpp:rebuild": "npx rapidsai-merge-compile-commands",
"cpp:rebuild:debug": "npx cmake-js rebuild -g -D -O build/Debug",
"postcpp:rebuild:debug": "npx rapidsai-merge-compile-commands",
"cpp:reconfigure": "npx cmake-js reconfigure -g -O build/Release",
"postcpp:reconfigure": "npx rapidsai-merge-compile-commands",
"cpp:reconfigure:debug": "npx cmake-js reconfigure -g -D -O build/Debug",
"postcpp:reconfigure:debug": "npx rapidsai-merge-compile-commands",
"tsc:clean": "rimraf build/js",
"tsc:build": "yarn tsc:clean && tsc -p ./tsconfig.json",
"tsc:watch": "yarn tsc:clean && tsc -p ./tsconfig.json -w",
"dev:cpack:enabled": "echo $npm_package_name"
},
"dependencies": {
"@rapidsai/cuda": "~22.12.2",
"@rapidsai/rmm": "~22.12.2",
"apache-arrow": "^9.0.0"
},
"devDependencies": {
"ix": "4.4.1"
},
"files": [
"LICENSE",
"README.md",
"index.js",
"package.json",
"CMakeLists.txt",
"src/node_cudf",
"build/js"
]
}
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/index.js
|
// Copyright (c) 2020, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
module.exports = require('./build/js/index');
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/jest.config.js
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
try {
require('dotenv').config();
} catch (e) {}
module.exports = {
'verbose': true,
'testEnvironment': 'node',
'maxWorkers': process.env.PARALLEL_LEVEL || 1,
'globals': {'ts-jest': {'diagnostics': false, 'tsconfig': 'test/tsconfig.json'}},
'rootDir': './',
'roots': ['<rootDir>/test/'],
'moduleFileExtensions': ['js', 'ts', 'tsx'],
'coverageReporters': ['lcov'],
'coveragePathIgnorePatterns': ['test\\/.*\\.(ts|tsx|js)$', '/node_modules/'],
'transform': {'^.+\\.jsx?$': 'ts-jest', '^.+\\.tsx?$': 'ts-jest'},
'transformIgnorePatterns':
['/build/(js|Debug|Release)/*$', '/node_modules/(?!web-stream-tools).+\\.js$'],
'testRegex': '(.*(-|\\.)(test|spec)s?)\\.(ts|tsx|js)$',
'preset': 'ts-jest',
'testMatch': null,
'moduleNameMapper': {
'^@rapidsai\/cudf(.*)': '<rootDir>/src/$1',
'^\.\.\/(Debug|Release)\/(rapidsai_cudf.node)$': '<rootDir>/build/$1/$2',
}
};
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/CMakeLists.txt
|
#=============================================================================
# Copyright (c) 2020-2022, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#=============================================================================
cmake_minimum_required(VERSION 3.24.1 FATAL_ERROR)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
unset(CMAKE_LIBRARY_OUTPUT_DIRECTORY)
unset(CMAKE_LIBRARY_OUTPUT_DIRECTORY CACHE)
option(NODE_RAPIDS_USE_SCCACHE "Enable caching compilation results with sccache" ON)
###################################################################################################
# - cmake modules ---------------------------------------------------------------------------------
execute_process(COMMAND node -p
"require('@rapidsai/core').cmake_modules_path"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
OUTPUT_VARIABLE NODE_RAPIDS_CMAKE_MODULES_PATH
OUTPUT_STRIP_TRAILING_WHITESPACE)
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/cmake_policies.cmake")
project(rapidsai_cudf VERSION $ENV{npm_package_version} LANGUAGES C CXX)
execute_process(COMMAND node -p
"require('path').dirname(require.resolve('@rapidsai/core'))"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
OUTPUT_VARIABLE NODE_RAPIDS_CORE_MODULE_PATH
OUTPUT_STRIP_TRAILING_WHITESPACE)
execute_process(COMMAND node -p
"require('path').dirname(require.resolve('@rapidsai/cuda'))"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
OUTPUT_VARIABLE NODE_RAPIDS_CUDA_MODULE_PATH
OUTPUT_STRIP_TRAILING_WHITESPACE)
execute_process(COMMAND node -p
"require('path').dirname(require.resolve('@rapidsai/rmm'))"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
OUTPUT_VARIABLE NODE_RAPIDS_RMM_MODULE_PATH
OUTPUT_STRIP_TRAILING_WHITESPACE)
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/ConfigureCXX.cmake")
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/ConfigureCUDA.cmake")
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/ConfigureNapi.cmake")
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/ConfigureCUDF.cmake")
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/install_utils.cmake")
###################################################################################################
# - rapidsai_cudf target ------------------------------------------------------------------------------
file(GLOB_RECURSE NODE_CUDF_SRC_FILES "${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp")
add_library(${PROJECT_NAME} SHARED ${NODE_CUDF_SRC_FILES} ${CMAKE_JS_SRC})
set_target_properties(${PROJECT_NAME}
PROPERTIES PREFIX ""
SUFFIX ".node"
BUILD_RPATH "\$ORIGIN"
INSTALL_RPATH "\$ORIGIN"
CXX_STANDARD 17
CXX_STANDARD_REQUIRED ON
CUDA_STANDARD 17
CUDA_STANDARD_REQUIRED ON
POSITION_INDEPENDENT_CODE ON
INTERFACE_POSITION_INDEPENDENT_CODE ON
)
target_compile_options(${PROJECT_NAME}
PRIVATE "$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:C>:${NODE_RAPIDS_CMAKE_C_FLAGS}>>"
"$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CXX>:${NODE_RAPIDS_CMAKE_CXX_FLAGS}>>"
"$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CUDA>:${NODE_RAPIDS_CMAKE_CUDA_FLAGS}>>"
)
target_compile_definitions(${PROJECT_NAME}
PUBLIC "$<$<COMPILE_LANGUAGE:CXX>:CUDA_API_PER_THREAD_DEFAULT_STREAM>"
"$<$<COMPILE_LANGUAGE:CUDA>:CUDA_API_PER_THREAD_DEFAULT_STREAM>"
)
target_include_directories(${PROJECT_NAME}
PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/src>"
"$<BUILD_INTERFACE:${NODE_RAPIDS_RMM_MODULE_PATH}/src>"
"$<BUILD_INTERFACE:${NODE_RAPIDS_CUDA_MODULE_PATH}/src>"
"$<BUILD_INTERFACE:${RAPIDS_CORE_INCLUDE_DIR}>"
"$<BUILD_INTERFACE:${NAPI_INCLUDE_DIRS}>"
)
target_link_libraries(${PROJECT_NAME}
PUBLIC ${CMAKE_JS_LIB}
cudf::cudf
"${NODE_RAPIDS_RMM_MODULE_PATH}/build/${CMAKE_BUILD_TYPE}/rapidsai_rmm.node"
"${NODE_RAPIDS_CUDA_MODULE_PATH}/build/${CMAKE_BUILD_TYPE}/rapidsai_cuda.node"
"${NODE_RAPIDS_CORE_MODULE_PATH}/build/${CMAKE_BUILD_TYPE}/rapidsai_core.node"
PRIVATE ${ARROW_LIBRARIES})
include("${NODE_RAPIDS_CMAKE_MODULES_PATH}/cuda_arch_helpers.cmake")
generate_arch_specific_custom_targets(
NAME ${PROJECT_NAME}
DEPENDENCIES "cudf::cudf"
)
generate_install_rules(
NAME ${PROJECT_NAME}
CUDA_ARCHITECTURES ${CMAKE_CUDA_ARCHITECTURES})
# Create a symlink to compile_commands.json for the llvm-vs-code-extensions.vscode-clangd plugin
execute_process(COMMAND
${CMAKE_COMMAND} -E create_symlink
${CMAKE_CURRENT_BINARY_DIR}/compile_commands.json
${CMAKE_CURRENT_SOURCE_DIR}/compile_commands.json)
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/README.md
|
# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/> node-rapids cuDF - GPU DataFrames</div>
### Installation
`npm install @rapidsai/cudf`
### About
The js bindings for [cuDF](https://github.com/rapidsai/cudf) provides an API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows in a Javascript runtime environment, without going into the details of CUDA programming.
For example, the following snippet creates a series, then uses the GPU to run some calculations:
```javascript
var { Series, Int32 } = require("@rapidsai/cudf");
var series1 = Series.new({ type: new Int32(), data: [1, 2, 3] });
console.log(series1.mean()); // 2
console.log(series1.max()); // 3
```
The following snippet creates a DataFrame, then uses the GPU to to run some calculations:
```javascript
var {
DataFrame,
DataType,
Float64,
GroupBy,
Int32,
Series,
} = require("@rapidsai/cudf");
var a = Series.new({ type: new Int32(), data: [5, 4, 3, 2, 1, 0] });
var b = Series.new({ type: new Int32(), data: [0, 0, 1, 1, 2, 2] });
var df = new DataFrame({ a: a, b: b });
var grp = new GroupBy({ obj: df, by: ["a"] });
var groups = grp.getGroups();
console.log(...groups["keys"].get("a")); // [0,1,2,3,4,5]
console.log(...groups.values?.get("b")); // [2,2,1,1,0,0]
console.log(...groups["offsets"]); // [0,1,2,3,4,5,6]
```
For detailed node-cuDF API, follow our [API Documentation](https://rapidsai.github.io/node/modules/cudf_src.html).
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/tsconfig.json
|
{
"include": ["src"],
"exclude": ["node_modules"],
"compilerOptions": {
"baseUrl": "./",
"paths": {
"@rapidsai/cudf": ["src/index"],
"@rapidsai/cudf/*": ["src/*"]
},
"target": "ESNEXT",
"module": "commonjs",
"outDir": "./build/js",
/* Decorators */
"experimentalDecorators": true,
/* Basic stuff */
"moduleResolution": "node",
"skipLibCheck": true,
"skipDefaultLibCheck": true,
"lib": ["dom", "esnext", "esnext.asynciterable"],
/* Control what is emitted */
"declaration": true,
"declarationMap": true,
"noEmitOnError": true,
"removeComments": false,
"downlevelIteration": true,
/* Create inline sourcemaps with sources */
"sourceMap": false,
"inlineSources": true,
"inlineSourceMap": true,
/* The most restrictive settings possible */
"strict": true,
"importHelpers": true,
"noEmitHelpers": true,
"noImplicitAny": true,
"noUnusedLocals": true,
"noImplicitReturns": true,
"allowUnusedLabels": false,
"noUnusedParameters": true,
"allowUnreachableCode": false,
"noFallthroughCasesInSwitch": true,
"forceConsistentCasingInFileNames": true
}
}
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/LICENSE
|
Apache License
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http://www.apache.org/licenses/
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6. Trademarks. This License does not grant permission to use the trade
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7. Disclaimer of Warranty. Unless required by applicable law or
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unless required by applicable law (such as deliberate and grossly
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liable to You for damages, including any direct, indirect, special,
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END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
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distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| 0 |
rapidsai_public_repos/node/modules
|
rapidsai_public_repos/node/modules/cudf/typedoc.js
|
module.exports = {
entryPoints: ['src/index.ts'],
out: 'doc',
name: '@rapidsai/cudf',
tsconfig: 'tsconfig.json',
excludePrivate: true,
excludeProtected: true,
excludeExternals: true,
};
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/.vscode/launch.json
|
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"compounds": [
{
"name": "Debug Tests (TS and C++)",
"configurations": [
"Debug Tests (launch gdb)",
// "Debug Tests (launch lldb)",
"Debug Tests (attach node)",
]
}
],
"configurations": [
{
"name": "Debug Tests (TS only)",
"type": "node",
"request": "launch",
"cwd": "${workspaceFolder}",
"console": "integratedTerminal",
"internalConsoleOptions": "neverOpen",
"program": "${workspaceFolder}/node_modules/.bin/jest",
"skipFiles": [
"<node_internals>/**",
"${workspaceFolder}/node_modules/**"
],
"env": {
"NODE_NO_WARNINGS": "1",
"NODE_ENV": "production",
"READABLE_STREAM": "disable",
},
"args": [
"--verbose",
"--runInBand",
"-c", "jest.config.js",
"${input:TEST_FILE}"
]
},
// {
// "name": "Debug Tests (launch lldb)",
// // hide the individual configurations from the debug dropdown list
// "presentation": { "hidden": true },
// "type": "lldb",
// "request": "launch",
// "stdio": null,
// "cwd": "${workspaceFolder}",
// "preLaunchTask": "cpp:ensure:debug:build",
// "env": {
// "NODE_DEBUG": "1",
// "NODE_NO_WARNINGS": "1",
// "NODE_ENV": "production",
// "READABLE_STREAM": "disable",
// },
// "stopOnEntry": false,
// "terminal": "console",
// "program": "${input:NODE_BINARY}",
// "initCommands": [
// "settings set target.disable-aslr false",
// ],
// "sourceLanguages": ["cpp", "cuda", "javascript"],
// "args": [
// "--inspect=9229",
// "--expose-internals",
// "${workspaceFolder}/node_modules/.bin/jest",
// "--verbose",
// "--runInBand",
// "-c",
// "jest.config.js",
// "${input:TEST_FILE}"
// ],
// },
{
"name": "Debug Tests (launch gdb)",
// hide the individual configurations from the debug dropdown list
"presentation": { "hidden": true },
"type": "cppdbg",
"request": "launch",
"stopAtEntry": false,
"externalConsole": false,
"cwd": "${workspaceFolder}",
"envFile": "${workspaceFolder}/.env",
"MIMode": "gdb",
"miDebuggerPath": "/usr/bin/gdb",
"setupCommands": [
{
"description": "Enable pretty-printing for gdb",
"text": "-enable-pretty-printing",
"ignoreFailures": true
}
],
"program": "${input:NODE_BINARY}",
"environment": [
{ "name": "NODE_DEBUG", "value": "1" },
{ "name": "NODE_NO_WARNINGS", "value": "1" },
{ "name": "NODE_ENV", "value": "production" },
{ "name": "READABLE_STREAM", "value": "disable" },
],
"args": [
"--inspect=9229",
"--expose-internals",
"${workspaceFolder}/node_modules/.bin/jest",
"--verbose",
"--runInBand",
"-c",
"jest.config.js",
"${input:TEST_FILE}"
],
},
{
"name": "Debug Tests (attach node)",
"type": "node",
"request": "attach",
// hide the individual configurations from the debug dropdown list
"presentation": { "hidden": true },
"port": 9229,
"timeout": 60000,
"cwd": "${workspaceFolder}",
"skipFiles": [
"<node_internals>/**",
"${workspaceFolder}/node_modules/**"
],
},
],
"inputs": [
{
"type": "command",
"id": "NODE_BINARY",
"command": "shellCommand.execute",
"args": {
"description": "path to node",
"command": "which node",
"useFirstResult": true,
}
},
{
"type": "command",
"id": "TEST_FILE",
"command": "shellCommand.execute",
"args": {
"cwd": "${workspaceFolder}/modules/cudf",
"description": "Select a file to debug",
"command": "./node_modules/.bin/jest --listTests | sed -r \"s@$PWD/test/@@g\"",
}
},
],
}
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/.vscode/tasks.json
|
{
"version": "2.0.0",
"tasks": [
{
"type": "shell",
"label": "Rebuild node_cudf TS and C++ (slow)",
"group": { "kind": "build", "isDefault": true, },
"command": "if [[ \"${input:CMAKE_BUILD_TYPE}\" == \"Release\" ]]; then yarn rebuild; else yarn rebuild:debug; fi",
"problemMatcher": [
"$tsc",
{
"owner": "cuda",
"fileLocation": ["relative", "${workspaceFolder}/build/${input:CMAKE_BUILD_TYPE}"],
"pattern": {
"file": 1,
"line": 2,
"severity": 3,
"message": 4,
"regexp": "^(.*)\\((\\d+)\\):\\s+(error|warning|note|info):\\s+(.*)$"
}
},
{
"owner": "cpp",
"fileLocation": ["relative", "${workspaceFolder}/build/${input:CMAKE_BUILD_TYPE}"],
"pattern": {
"file": 1,
"line": 2,
"severity": 4,
"message": 5,
"regexp": "^(.*):(\\d+):(\\d+):\\s+(error|warning|note|info):\\s+(.*)$"
}
},
],
},
{
"type": "npm",
"group": "build",
"label": "Recompile node_cudf TS (fast)",
"script": "tsc:build",
"detail": "yarn tsc:build",
"problemMatcher": ["$tsc"],
},
{
"type": "shell",
"group": "build",
"label": "Recompile node_cudf C++ (fast)",
"command": "ninja -C ${workspaceFolder}/build/${input:CMAKE_BUILD_TYPE}",
"problemMatcher": [
{
"owner": "cuda",
"fileLocation": ["relative", "${workspaceFolder}/build/${input:CMAKE_BUILD_TYPE}"],
"pattern": {
"file": 1,
"line": 2,
"severity": 3,
"message": 4,
"regexp": "^(.*)\\((\\d+)\\):\\s+(error|warning|note|info):\\s+(.*)$"
}
},
{
"owner": "cpp",
"fileLocation": ["relative", "${workspaceFolder}/build/${input:CMAKE_BUILD_TYPE}"],
"pattern": {
"file": 1,
"line": 2,
"severity": 4,
"message": 5,
"regexp": "^(.*):(\\d+):(\\d+):\\s+(error|warning|note|info):\\s+(.*)$"
}
},
],
},
],
"inputs": [
{
"type": "pickString",
"default": "Release",
"id": "CMAKE_BUILD_TYPE",
"options": ["Release", "Debug"],
"description": "C++ Build Type",
}
]
}
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/notebooks/README.md
|
# Node.js Notebook Examples
A series of data science and ETL notebooks to demonstrate [RAPIDS Bindings](https://github.com/rapidsai/node/blob/main/BINDINGS.md) coverage and functionality. Start with `Basic Demo.ipynb`!
## Data Requirements
Most notebooks contain built-in data creation or download functions. Check [SOURCES.md](https://github.com/rapidsai/node/blob/main/modules/cudf/notebooks/SOURCES.md) for details.
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rapidsai_public_repos/node/modules/cudf/notebooks/Speedtest.ipynb
|
if (!process.cwd().includes('modules/cudf/notebooks')) {
process.chdir('modules/cudf/notebooks');
}var cudf = require('@rapidsai/cudf');
var regexps = [
/Cloud|Overcast/,
/Rain|T-Storm|Thunderstorm|Squalls|Drizzle/,
/Snow/,
/Fog/,
/Ice|Hail|Freezing|Sleet/,
/Dust|Smoke|Sand/,
];function readUSAccidentsCSV() {
const boolDtype = new cudf.Bool8;
const int32Dtype = new cudf.Int32;
const strDtype = new cudf.Utf8String;
const float64Dtype = new cudf.Float64;
return cudf.DataFrame.readCSV({
header: 0,
sourceType: 'files',
sources: [`data/US_Accidents_Dec20.csv`],
dataTypes: {
id: strDtype,
source: strDtype,
tmc: float64Dtype,
severity: int32Dtype,
start_time: strDtype, end_time: strDtype,
start_lat: float64Dtype, start_lng: float64Dtype, end_lat: float64Dtype, end_lng: float64Dtype, distance: float64Dtype,
description: strDtype,
number: int32Dtype,
street: strDtype, side: strDtype, city: strDtype, county: strDtype, state: strDtype, zipcode: strDtype, country: strDtype, timezone: strDtype, airport_code: strDtype, weather_timestamp: strDtype,
temperature: float64Dtype, wind_chill: float64Dtype, humidity: float64Dtype, pressure: float64Dtype, visibility: float64Dtype, wind_direction: strDtype, wind_speed: float64Dtype, precipitation: float64Dtype,
weather_condition: strDtype,
amenity: boolDtype, bump: boolDtype, crossing: boolDtype, give_way: boolDtype, junction: boolDtype, no_exit: boolDtype, railway: boolDtype, roundabout: boolDtype, station: boolDtype, stop: boolDtype, traffic_calming: boolDtype, traffic_signal: boolDtype, turning_loop: boolDtype,
sunrise_sunset: strDtype, civil_twilight: strDtype, nautical_twighlight: strDtype, astronomical_twighlight: strDtype
},
})
}var weather_condition_gpu = readUSAccidentsCSV().get('weather_condition');
console.time(`GPU time`);
regexps.forEach((regexp) => {
console.time(`${regexp.source} time`);
const matches = weather_condition_gpu.containsRe(regexp.source).sum();
console.timeEnd(`${regexp.source} time`);
console.log(`${regexp.source} matches: ${matches.toLocaleString()}\n`);
});
console.timeEnd(`GPU time`);var weather_condition_cpu = (() => {
const categorical = weather_condition_gpu.cast(new cudf.Categorical(new cudf.Utf8String));
const categories = [...categorical.categories];
const codes = [...categorical.codes];
return codes.map((i) => categories[i]);
})();
console.time(`CPU time`);
regexps.forEach((regexp) => {
console.time(`${regexp.source} time`);
const matches = weather_condition_cpu.reduce((matches, weather_condition) => {
return matches + (regexp.exec(weather_condition) || []).length;
}, 0);
console.timeEnd(`${regexp.source} time`);
console.log(`${regexp.source} matches: ${matches.toLocaleString()}\n`);
});
console.timeEnd(`CPU time`);
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|
rapidsai_public_repos/node/modules/cudf/notebooks/SOURCES.md
|
# Notebook demo data sources
* [US_Accidents_Dec20.csv](https://www.kaggle.com/sobhanmoosavi/us-accidents?select=US_Accidents_Dec20.csv)
* [san_francisco_censustracts.geojson](https://movement.uber.com/explore/san_francisco/travel-times/query?lang=en-US&si=1277&ti=&ag=censustracts&dt[tpb]=ALL_DAY&dt[wd;]=1,2,3,4,5,6,7&dt[dr][sd]=2020-03-01&dt[dr][ed]=2020-03-31&cd=&sa;=&sdn=&lat.=37.7749295&lng.=-122.4547777&z.=12)
`Download data > Geo Boundaries > SAN_FRANCISCO_CENSUSTRACTS.JSON`
* [san_fran_uber.csv](https://movement.uber.com/explore/san_francisco/travel-times/query?lang=en-US&si=1277&ti=&ag=censustracts&dt[tpb]=ALL_DAY&dt[wd;]=1,2,3,4,5,6,7&dt[dr][sd]=2020-03-01&dt[dr][ed]=2020-03-31&cd=&sa;=&sdn=&lat.=37.7749295&lng.=-122.4547777&z.=12)
`Download data > All data > 2020 Quarter > Travel Times By Date By Hour Buckets (All Days) (1.7gb)`
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|
rapidsai_public_repos/node/modules/cudf/notebooks/Basic Demo.ipynb
|
var cudf = require("@rapidsai/cudf")console.time("readCSV")
df = cudf.DataFrame.readCSV({
header: 0,
sourceType: 'files',
sources: ["modules/cudf/notebooks/data/US_Accidents_Dec20.csv"]
});
console.timeEnd("readCSV")console.log("Number of rows:", df.numRows)
console.log("Number of cols:", df.numColumns)console.log(df.head().toString({maxColumns: 0}))var df = df.drop([
'Civil_Twilight', 'Nautical_Twilight', 'Astronomical_Twilight', 'Wind_Speed(mph)', 'Wind_Direction', 'Wind_Chill', 'Humidity(50)', 'Sunrise_Sunset',
'Pressure', 'Amenity', 'Bump', 'Give_Way', 'No_Exit', 'Railway', 'Roundabout', 'Station', 'Traffic_Calming', 'Turning_Loop', 'Timezone', 'Crossing', 'Stop', 'Traffic_Signal', 'Junction', 'Number', 'Side', 'County',
'Airport_Code', 'TMC', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng', 'End_Lat', 'End_Lng', 'Street', 'Country', 'Zipcode', 'Distance(mi)', 'Wind_Chill(F)', 'Pressure(in)', 'Humidity(%)']
)df.namesconsole.log(df.head().toString({maxColumns: 0}))temp = df.get('Temperature(F)')
console.log("Min temp:", temp.min())
console.log("Max temp:", temp.max())temp = df.get('Temperature(F)')
console.time("filter")
valid_temps = temp.lt(120).logicalAnd(temp.gt(-30))
df = df.filter(valid_temps)
console.timeEnd("filter")temp = df.get('Temperature(F)')
console.log("New number of rows:", df.numRows)
console.log("New min temp:", temp.min())
console.log("New max temp:", temp.max())weather_groups = df.groupBy({by: "Weather_Condition"})
JSON.stringify(weather_groups.nth(0).get("Weather_Condition").toArrow().toArray())weather = df.get("Weather_Condition")
console.time("regex")
clouds_mask = weather.containsRe("Cloud|Overcast");
rain_mask = weather.containsRe("Rain|T-Storm|Thunderstorm|Squalls|Drizzle");
snow_mask = weather.containsRe("Snow")
fog_mask = weather.containsRe("Fog")
ice_mask = weather.containsRe("Ice|Hail|Freezing|Sleet")
particulate_mask = weather.containsRe("Dust|Smoke|Sand")
console.timeEnd("regex")console.time("sum")
console.log("Severity with clouds :", clouds_mask.sum())
console.log("Severity with rain :", rain_mask.sum())
console.log("Severity with snow :", snow_mask.sum())
console.log("Severity with fog :", fog_mask.sum())
console.log("Severity with particulate:", particulate_mask.sum())
console.log("Severity with ice :", ice_mask.sum())
console.timeEnd("sum")console.time("means")
console.log("Severity with clouds :", df.filter(clouds_mask).get("Severity").mean())
console.log("Severity with rain :", df.filter(rain_mask).get("Severity").mean())
console.log("Severity with snow :", df.filter(snow_mask).get("Severity").mean())
console.log("Severity with fog :", df.filter(fog_mask).get("Severity").mean())
console.log("Severity with particulate:", df.filter(particulate_mask).get("Severity").mean())
console.log("Severity with ice :", df.filter(ice_mask).get("Severity").mean())
console.timeEnd("means")
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/notebooks/Wikipedia.ipynb
|
var cudf = require("@rapidsai/cudf");console.time("readCSV")
df = cudf.DataFrame.readCSV({header: 0, sourceType: 'files', sources: ["modules/cudf/notebooks/data/page_titles_en_0.csv"]})
console.timeEnd("readCSV")console.log("Number of rows:", df.numRows)
console.log("Number of cols:", df.numColumns)
console.log("Columns:", df.names)df = df.drop(['revid'])console.log(df.toString())df.get('title').len().max()title = df.get('title')
console.log([...title.filter(title.len().eq(228))])
text = df.get('text')
console.log("Max text:", text.len().max())
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/notebooks/Geospatial Demo.ipynb
|
var { DataFrame, Series, Int32 } = require('@rapidsai/cudf');
var { Field, Vector, Float32, Struct, List, FixedSizeList } = require('apache-arrow');
function readTracts() {
const { features } = JSON.parse(require('fs').readFileSync(
'modules/cudf/notebooks/data/san_francisco_censustracts.json',
{ encoding: 'utf8' }
));
// console.log(features[0])
// console.log(features[0].geometry)
// console.log(features[0].geometry.coordinates)
// console.log(features[0].geometry.coordinates[0][0][0])
// console.log(features[0].geometry.coordinates[0][0])
// console.log(features[0].geometry.coordinates[0])
const polygons = features
.filter((f) => f.geometry.type === 'MultiPolygon')
.reduce((x, { geometry }) => x.concat(geometry.coordinates), []);
return new DataFrame({
id: Series.sequence({ type: new Int32, init: 0, size: polygons.length }),
polygons: Series.new(featureToVector(polygons))
});
function featureToVector(coordinates) {
return Vector.from({
values: coordinates,
highWaterMark: Number.POSITIVE_INFINITY,
type: new List(Field.new({
name: 'rings', type: new List(Field.new({
name: 'coords', type: new List(Field.new({
name: 'points', type: new Float32()
}))
}))
})),
});
}
}
console.time("read tracts GeoJSON")
var tracts = readTracts()
console.timeEnd("read tracts GeoJSON")
console.log(tracts.toArrow().slice(0, 5).toArray())function readTrips() {
const trips = DataFrame.readCSV({
header: 0,
sourceType: 'files',
sources: ['modules/cudf/notebooks/data/san_fran_uber.csv']
// dataTypes: {
// sourceid: 'int',
// dstid: 'int16',
// month: 'int8',
// day: 'int8',
// start_hour: 'int8',
// end_hour: 'int8',
// travel_time: 'float32'
// }
});
return new DataFrame({
id: Series.sequence({ type: new Int32, init: 0, size: trips.numRows }),
sourceid: trips.get('sourceid'),
dstid: trips.get('dstid'),
month: trips.get('month'),
day: trips.get('day'),
start_hour: trips.get('start_hour'),
end_hour: trips.get('end_hour'),
travel_time: trips.get('mean_travel_time'),
});
}
console.time("read trips CSV")
var trips = readTrips()
console.timeEnd("read trips CSV")
console.log('number of trips:',
trips.numRows.toLocaleString())
console.log(trips.toArrow().slice(0, 5).toArray())console.time("groupBy sourceId and compute average trip time")
var averageTripTimesBySourceId = trips
.select(['sourceid', 'travel_time'])
.groupBy({ by: 'sourceid' })
.mean()
.sortValues({ sourceid: { ascending: true }})
console.timeEnd("groupBy sourceId and compute average trip time")
console.log('number of unique trips:',
averageTripTimesBySourceId.numRows.toLocaleString())
console.log(averageTripTimesBySourceId.toArrow().slice(0, 10).toArray())var sourceTracts = tracts.gather(averageTripTimesBySourceId.get('sourceid'))
console.log('number of source tracts:',
sourceTracts.numRows.toLocaleString())
console.log(sourceTracts.toArrow().slice(0, 5).toArray())
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/scalar.ts
|
// Copyright (c) 2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import CUDF from './addon';
import {DataType} from './types/dtypes';
type ScalarProps<T extends DataType> = {
type: T,
value?: T['scalarType']|null
};
export interface ScalarConstructor {
readonly prototype: Scalar;
new<T extends DataType = any>(props: ScalarProps<T>): Scalar<T>;
}
/**
* A low-level wrapper for libcudf Scalar values
*/
export interface Scalar<T extends DataType = any> {
type: T;
value: T['scalarType']|null;
}
// eslint-disable-next-line @typescript-eslint/no-redeclare
export const Scalar: ScalarConstructor = CUDF.Scalar;
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/index.ts
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
export * as addon from './addon';
export * from './column';
export * from './data_frame';
export * from './groupby';
export * from './series';
export * from './scope';
export * from './table';
export * from './types/csv';
export * from './types/enums';
export * from './types/dtypes';
export * from './types/mappings';
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rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/table.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {MemoryData} from '@rapidsai/cuda';
import {DeviceBuffer, MemoryResource} from '@rapidsai/rmm';
import {Field} from 'apache-arrow';
import CUDF from './addon';
import {Column} from './column';
import {Scalar} from './scalar';
import {ReadCSVOptions, TableWriteCSVOptions} from './types/csv';
import {Bool8, DataType, IndexType, Int32} from './types/dtypes';
import {DuplicateKeepOption} from './types/enums';
import {TypeMap} from './types/mappings';
import {ReadORCOptions, TableWriteORCOptions} from './types/orc';
import {ReadParquetOptions, TableWriteParquetOptions} from './types/parquet';
export type ToArrowMetadata = [string | number, ToArrowMetadata[]?];
export interface TableConstructor {
readonly prototype: Table;
new(props: {columns?: ReadonlyArray<Column>|null}): Table;
/**
* Reads a CSV dataset into a set of columns.
*
* @param options Settings for controlling reading behavior.
* @return The CSV data as a Table and a list of column names.
*/
readCSV<T extends TypeMap = any>(options: ReadCSVOptions<T>):
{names: (string&keyof T)[], table: Table};
/**
* Reads an ORC dataset into a set of columns.
*
* @param options Settings for controlling reading behavior.
* @return The ORC data as a Table and a list of column names.
*/
readORC(options: ReadORCOptions): {names: string[], table: Table};
/**
* Reads an Apache Parquet dataset into a set of columns.
*
* @param options Settings for controlling reading behavior.
* @return The CSV data as a Table and a list of column names.
*/
readParquet(options: ReadParquetOptions): {names: string[], table: Table};
/**
* Adapts an arrow Table in IPC format into a set of columns.
*
* @param memory A buffer holding Arrow table
* @return The Arrow data as a Table and a list of column names.
*/
fromArrow(memory: DeviceBuffer|MemoryData): {table: Table, fields: Field[]};
/**
* Returns tables concatenated to each other.
*
* @param tables The tables to concatenate
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
concat(tables: Table[], memoryResource?: MemoryResource): Table;
/**
* Returns a pair of row index vectors corresponding to a full (outer) join between the specified
* tables.
*
* @param left_keys The left table
* @param right_keys The right table
* @param nullEquality controls whether null join-key values should match or not
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
fullJoin(left: Table, right: Table, nullEquality: boolean, memoryResource?: MemoryResource):
[Column<Int32>, Column<Int32>];
/**
* Returns a pair of row index vectors corresponding to an inner join between the specified
* tables.
*
* @param left_keys The left table
* @param right_keys The right table
* @param nullEquality controls whether null join-key values should match or not
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
innerJoin(left: Table, right: Table, nullEquality: boolean, memoryResource?: MemoryResource):
[Column<Int32>, Column<Int32>];
/**
* Returns a pair of row index vectors corresponding to a left join between the specified tables.
*
* @param left_keys The left table
* @param right_keys The right table
* @param nullEquality controls whether null join-key values should match or not
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
leftJoin(left: Table, right: Table, nullEquality: boolean, memoryResource?: MemoryResource):
[Column<Int32>, Column<Int32>];
/**
* Returns an index vectors corresponding to a left semijoin between the specified tables.
*
* @param left_keys The left table
* @param right_keys The right table
* @param nullEquality controls whether null join-key values should match or not
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
leftSemiJoin(left: Table, right: Table, nullEquality: boolean, memoryResource?: MemoryResource):
Column<Int32>;
/**
* Returns an index vectors corresponding to a left antijoin between the specified tables.
*
* @param left_keys The left table
* @param right_keys The right table
* @param nullEquality controls whether null join-key values should match or not
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
leftAntiJoin(left: Table, right: Table, nullEquality: boolean, memoryResource?: MemoryResource):
Column<Int32>;
}
/**
* A low-level wrapper for libcudf Table objects
*/
export interface Table {
/**
* Number of columns in the table.
*/
readonly numColumns: number;
/**
* Number of rows in each column of the table.
*/
readonly numRows: number;
/**
* @summary Explicitly free the device memory associated with this Table.
*/
dispose(): void;
/**
* @summary Return sub-selection from a Table.
*
* @description Gathers the rows of the source columns according to `selection`, such that row "i"
* in the resulting Table's columns will contain row `selection[i]` from the source columns. The
* number of rows in the result table will be equal to the number of elements in selection. A
* negative value i in the selection is interpreted as i+n, where `n` is the number of rows in
* the source table.
*
* For dictionary columns, the keys column component is copied and not trimmed if the gather
* results in abandoned key elements.
*
* @param selection A Series of 8/16/32-bit signed or unsigned integer indices to gather.
* @param nullify_out_of_bounds If `true`, coerce rows that corresponds to out-of-bounds indices
* in the selection to null. If `false`, skips all bounds checking for selection values. Pass
* false if you are certain that the selection contains only valid indices for better
* performance. If `false` and there are out-of-bounds indices in the selection, the behavior
* is undefined. Defaults to `false`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
gather(selection: Column<IndexType>,
nullify_out_of_bounds: boolean,
memoryResource?: MemoryResource): Table;
/**
* Return sub-selection from a Table.
*
* @param selection A Column of booleans. Rows at true indices are returned, false are omitted.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
applyBooleanMask(selection: Column<Bool8>, memoryResource?: MemoryResource): Table;
/**
* Scatters row of values into this Table according to provided indices.
*
* @param source A column of values to be scattered in to this Series
* @param indices A column of integral indices that indicate the rows in the this Series to be
* replaced by `value`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
scatterScalar<T extends Scalar[]>(source: T,
indices: Column<IndexType>,
memoryResource?: MemoryResource): Table;
/**
* Scatters a Table of values into this Table according to provided indices.
*
* @param value A value to be scattered in to this Series
* @param indices A column of integral indices that indicate the rows in the this Series to be
* replaced by `value`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
scatterTable(source: Table, indices: Column<IndexType>, memoryResource?: MemoryResource): Table;
/**
* Get the Column at a specified index
*
* @param index The index position of the column to return
*
* @reurns The Column located at `index`
*/
getColumnByIndex<T extends DataType = any>(index: number): Column<T>;
/**
* Generate an ordering that sorts Table columns in a specified way
*
* @param column_orders The desired sort order for each column. Size must
* be equal to `numColumns`.
* @param null_orders Indicates how null values compare against all
* other values in a column
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @returns Column of permutation indices for the desired sort order
*/
orderBy(column_orders: boolean[], null_orders: boolean[], memoryResource?: MemoryResource):
Column<Int32>;
toArrow(names: ToArrowMetadata[]): Uint8Array;
/**
* Write this Table to CSV file format.
* @param options Settings for controlling writing behavior.
*/
writeCSV(options: TableWriteCSVOptions): void;
/**
* Write a Table to Apache ORC file format.
* @param filePath File path or root directory path.
* @param options Options controlling ORC writing behavior.
*/
writeORC(filePath: string, options: TableWriteORCOptions): void;
/**
* Write a Table to Apache Parquet file format.
* @param filePath File path or root directory path.
* @param options Options controlling parquet writing behavior.
*/
writeParquet(filePath: string, options: TableWriteParquetOptions): void;
dropNans(keys: number[], threshold: number): Table;
dropNulls(keys: number[], threshold: number): Table;
/**
* @summary Create a new table with consecutive duplicate rows removed.
*
* @param keys List of column indices on which to operate.
* @param keep Determines whether to keep the first, last, or none of the duplicate items.
* @param nullsEqual Determines whether nulls are handled as equal values.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
unique(keys: number[],
keep: DuplicateKeepOption,
nullsEqual: boolean,
memoryResource?: MemoryResource): Table;
/**
* @summary Create a new table with duplicate rows removed.
*
* @param keys List of column indices on which to operate.
* @param nullsEqual Determines whether nulls are handled as equal values.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
distinct(keys: number[], nullsEqual: boolean, memoryResource?: MemoryResource): Table;
/**
* @summary Explodes a list column's elements.
*
* Any list is exploded, which means the elements of the list in each row are expanded into new
* rows in the output. The corresponding rows for other columns in the input are duplicated.
*
* Example:
* ```
* [[5,10,15], 100],
* [[20,25], 200],
* [[30], 300],
* returns
* [5, 100],
* [10, 100],
* [15, 100],
* [20, 200],
* [25, 200],
* [30, 300],
* ```
*
* Nulls and empty lists propagate in different ways depending on what is null or empty.
* ```
* [[5,null,15], 100],
* [null, 200],
* [[], 300],
* returns
* [5, 100],
* [null, 100],
* [15, 100],
* ```
*
* @note null lists are not included in the resulting table, but nulls inside lists and empty
* lists will be represented with a null entry for that column in that row.
*
* @param {number} index Column index to explode inside the table.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
explode(index: number, memoryResource?: MemoryResource): Table;
/**
* @summary Explodes a list column's elements and includes a position column.
*
* Any list is exploded, which means the elements of the list in each row are expanded into new
* rows in the output. The corresponding rows for other columns in the input are duplicated. A
* position column is added that has the index inside the original list for each row.
*
* Example:
* ```
* [[5,10,15], 100],
* [[20,25], 200],
* [[30], 300],
* returns
* [0, 5, 100],
* [1, 10, 100],
* [2, 15, 100],
* [0, 20, 200],
* [1, 25, 200],
* [0, 30, 300],
* ```
*
* Nulls and empty lists propagate in different ways depending on what is null or empty.
* ```
* [[5,null,15], 100],
* [null, 200],
* [[], 300],
* returns
* [0, 5, 100],
* [1, null, 100],
* [2, 15, 100],
* ```
*
* @note null lists are not included in the resulting table, but nulls inside lists and empty
* lists will be represented with a null entry for that column in that row.
*
* @param {number} index Column index to explode inside the table.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
explodePosition(index: number, memoryResource?: MemoryResource): Table;
/**
* @summary Explodes a list column's elements retaining any null entries or empty lists inside.
*
* Any list is exploded, which means the elements of the list in each row are expanded into new
* rows in the output. The corresponding rows for other columns in the input are duplicated.
*
* Example:
* ```
* [[5,10,15], 100],
* [[20,25], 200],
* [[30], 300],
* returns
* [5, 100],
* [10, 100],
* [15, 100],
* [20, 200],
* [25, 200],
* [30, 300],
* ```
*
* Nulls and empty lists propagate as null entries in the result.
* ```
* [[5,null,15], 100],
* [null, 200],
* [[], 300],
* returns
* [5, 100],
* [null, 100],
* [15, 100],
* [null, 200],
* [null, 300],
* ```
*
* @param {number} index Column index to explode inside the table.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
explodeOuter(index: number, memoryResource?: MemoryResource): Table;
/**
* @summary Explodes a list column's elements retaining any null entries or empty lists and
* includes a position column.
*
* Any list is exploded, which means the elements of the list in each row are expanded into new
* rows in the output. The corresponding rows for other columns in the input are duplicated. A
* position column is added that has the index inside the original list for each row.
*
* Example:
* ```
* [[5,10,15], 100],
* [[20,25], 200],
* [[30], 300],
* returns
* [0, 5, 100],
* [1, 10, 100],
* [2, 15, 100],
* [0, 20, 200],
* [1, 25, 200],
* [0, 30, 300],
* ```
*
* Nulls and empty lists propagate as null entries in the result.
* ```
* [[5,null,15], 100],
* [null, 200],
* [[], 300],
* returns
* [0, 5, 100],
* [1, null, 100],
* [2, 15, 100],
* [0, null, 200],
* [0, null, 300],
* ```
*
* @param {number} index Column index to explode inside the table.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
explodeOuterPosition(index: number, memoryResource?: MemoryResource): Table;
/**
* Interleave Series columns of a table into a single column.
* Converts the column major table `cols` into a row major column.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
interleaveColumns(memoryResource?: MemoryResource): Column;
}
// eslint-disable-next-line @typescript-eslint/no-redeclare
export const Table: TableConstructor = CUDF.Table;
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/series.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {
Float32Buffer,
Float64Buffer,
Int16Buffer,
Int32Buffer,
Int64Buffer,
Int8Buffer,
MemoryData,
MemoryView,
Uint16Buffer,
Uint32Buffer,
Uint64Buffer,
Uint8Buffer,
Uint8ClampedBuffer,
} from '@rapidsai/cuda';
import {DeviceBuffer, MemoryResource} from '@rapidsai/rmm';
import * as arrow from 'apache-arrow';
import {JavaScriptArrayDataType} from 'apache-arrow/interfaces';
import {compareTypes} from 'apache-arrow/visitor/typecomparator';
import {Column} from './column';
import {fromArrow} from './column/from_arrow';
import {DataFrame} from './data_frame';
import {DisplayOptions} from './dataframe/print';
import {Scalar} from './scalar';
import {DISPOSER, scope} from './scope';
import {Table} from './table';
import {
Bool8,
Categorical,
DataType,
Float32,
Float64,
IndexType,
Int16,
Int32,
Int64,
Int8,
Integral,
List,
Numeric,
Struct,
TimestampDay,
TimestampMicrosecond,
TimestampMillisecond,
TimestampNanosecond,
TimestampSecond,
Uint16,
Uint32,
Uint64,
Uint8,
Utf8String,
} from './types/dtypes';
import {
DuplicateKeepOption,
NullOrder,
} from './types/enums';
import {ArrowToCUDFType, CommonType, findCommonType} from './types/mappings';
export type SeriesProps<T extends DataType = any> = {
/*
* SeriesProps *with* a `nullMask` shouldn't allow `data` to be an Array with elements and nulls:
* ```javascript
* Series.new({
* type: new Int32,
* data: [1, 0, 2, 3, 0], ///< must not include nulls
* nullMask: [true, false, true, true, false]
* })
* ```
*/
type: T;
data?: DeviceBuffer | MemoryData | arrow.Vector<T>| T['scalarType'][] | null;
offset?: number;
length?: number;
nullCount?: number;
nullMask?: DeviceBuffer | MemoryData | any[] | boolean | null;
children?: ReadonlyArray<Series>| null;
}|{
/*
* SeriesProps *without* a `nullMask` should allow `data` to be an Array with elements and nulls:
* ```javascript
* Series.new({
* type: new Int32,
* data: [1, null, 2, 3, null] ///< can include nulls
* })
* ```
*/
type: T;
data?: DeviceBuffer|MemoryData|arrow.Vector<T>|(T['scalarType'] | null | undefined)[]|null;
offset?: number;
length?: number;
nullCount?: number;
nullMask?: never;
children?: ReadonlyArray<Series>|null;
};
// clang-format off
/* eslint-disable @typescript-eslint/no-unused-vars */
class CastVisitor<T extends DataType> extends arrow.Visitor {
constructor(private series: AbstractSeries<T>, private memoryResource?: MemoryResource) { super(); }
public visitBool <T extends Bool8>(_dtype: T) { return this.series._castAsBool8(this.memoryResource); }
public visitInt8 <T extends Int8>(_dtype: T) { return this.series._castAsInt8(this.memoryResource); }
public visitInt16 <T extends Int16>(_dtype: T) { return this.series._castAsInt16(this.memoryResource); }
public visitInt32 <T extends Int32>(_dtype: T) { return this.series._castAsInt32(this.memoryResource); }
public visitInt64 <T extends Int64>(_dtype: T) { return this.series._castAsInt64(this.memoryResource); }
public visitUint8 <T extends Uint8>(_dtype: T) { return this.series._castAsUint8(this.memoryResource); }
public visitUint16 <T extends Uint16>(_dtype: T) { return this.series._castAsUint16(this.memoryResource); }
public visitUint32 <T extends Uint32>(_dtype: T) { return this.series._castAsUint32(this.memoryResource); }
public visitUint64 <T extends Uint64>(_dtype: T) { return this.series._castAsUint64(this.memoryResource); }
public visitFloat32 <T extends Float32>(_dtype: T) { return this.series._castAsFloat32(this.memoryResource); }
public visitFloat64 <T extends Float64>(_dtype: T) { return this.series._castAsFloat64(this.memoryResource); }
public visitUtf8 <T extends Utf8String>(_dtype: T) { return this.series._castAsString(this.memoryResource); }
public visitDateDay <T extends TimestampDay>(_dtype: T) { return this.series._castAsTimeStampDay(this.memoryResource); }
public visitDateMillisecond <T extends TimestampMillisecond>(_dtype: T) { return this.series._castAsTimeStampMillisecond(this.memoryResource); }
public visitTimestampSecond <T extends TimestampSecond>(_dtype: T) { return this.series._castAsTimeStampSecond(this.memoryResource); }
public visitTimestampMillisecond <T extends TimestampMillisecond>(_dtype: T) { return this.series._castAsTimeStampMillisecond(this.memoryResource); }
public visitTimestampMicrosecond <T extends TimestampMicrosecond>(_dtype: T) { return this.series._castAsTimeStampMicrosecond(this.memoryResource); }
public visitTimestampNanosecond <T extends TimestampNanosecond>(_dtype: T) { return this.series._castAsTimeStampNanosecond(this.memoryResource); }
public visitDictionary <T extends Categorical>(dtype: T) { return this.series._castAsCategorical(dtype, this.memoryResource); }
}
/* eslint-enable @typescript-eslint/no-unused-vars */
// clang-format on
export type Series<T extends arrow.DataType = any> = {
[arrow.Type.NONE]: never, // TODO
[arrow.Type.Null]: never, // TODO
[arrow.Type.Int]: never,
[arrow.Type.Int8]: Int8Series,
[arrow.Type.Int16]: Int16Series,
[arrow.Type.Int32]: Int32Series,
[arrow.Type.Int64]: Int64Series,
[arrow.Type.Uint8]: Uint8Series,
[arrow.Type.Uint16]: Uint16Series,
[arrow.Type.Uint32]: Uint32Series,
[arrow.Type.Uint64]: Uint64Series,
[arrow.Type.Float]: never,
[arrow.Type.Float16]: never,
[arrow.Type.Float32]: Float32Series,
[arrow.Type.Float64]: Float64Series,
[arrow.Type.Binary]: never,
[arrow.Type.Utf8]: StringSeries,
[arrow.Type.Bool]: Bool8Series,
[arrow.Type.Decimal]: never, // TODO
[arrow.Type.Date]: never, // TODO
[arrow.Type.DateDay]: TimestampDaySeries,
[arrow.Type.DateMillisecond]: TimestampMillisecondSeries,
[arrow.Type.Time]: never, // TODO
[arrow.Type.TimeSecond]: never, // TODO
[arrow.Type.TimeMillisecond]: never, // TODO
[arrow.Type.TimeMicrosecond]: never, // TODO
[arrow.Type.TimeNanosecond]: never, // TODO
[arrow.Type.Timestamp]: never, // TODO
[arrow.Type.TimestampSecond]: TimestampSecondSeries,
[arrow.Type.TimestampMillisecond]: TimestampMillisecondSeries,
[arrow.Type.TimestampMicrosecond]: TimestampMicrosecondSeries,
[arrow.Type.TimestampNanosecond]: TimestampNanosecondSeries,
[arrow.Type.Interval]: never, // TODO
[arrow.Type.IntervalDayTime]: never, // TODO
[arrow.Type.IntervalYearMonth]: never, // TODO
[arrow.Type.List]: ListSeries<(T extends List ? T['valueType'] : any)>,
[arrow.Type.Struct]: StructSeries<(T extends Struct ? T['dataTypes'] : any)>,
[arrow.Type.Union]: never, // TODO
[arrow.Type.DenseUnion]: never, // TODO
[arrow.Type.SparseUnion]: never, // TODO
[arrow.Type.FixedSizeBinary]: never, // TODO
[arrow.Type.FixedSizeList]: never, // TODO
[arrow.Type.Map]: never, // TODO
[arrow.Type.Dictionary]: CategoricalSeries<(T extends arrow.Dictionary ? T['valueType'] : any)>
}[T['TType']];
/**
* One-dimensional GPU array
*/
export class AbstractSeries<T extends DataType = any> {
/**
* Create a new cudf.Series from an apache arrow vector
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
* import * as arrow from 'apache-arrow';
*
* const arrow_vec = arrow.vectorFromArray(new Int32Array([1,2,3,4])));
* const a = Series.new(arrow_vec); // Int32Series [1, 2, 3, 4]
*
* const arrow_vec_list = arrow.vectorFromArray(
* [[0, 1, 2], [3, 4, 5], [6, 7, 8]],
* new arrow.List(arrow.Field.new({ name: 'ints', type: new arrow.Int32 })),
* );
*
* const b = Series.new(arrow_vec_list) // ListSeries [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
*
* const arrow_vec_struct = arrow.vectorFromArray(
* [{ x: 0, y: 3 }, { x: 1, y: 4 }, { x: 2, y: 5 }],
* new arrow.Struct([
* arrow.Field.new({ name: 'x', type: new arrow.Int32 }),
* arrow.Field.new({ name: 'y', type: new arrow.Int32 })
* ]),
* );
*
* const c = Series.new(arrow_vec_struct);
* // StructSeries [{ x: 0, y: 3 }, { x: 1, y: 4 }, { x: 2, y: 5 }]
* ```
*/
static new<T extends arrow.Vector>(input: T): Series<ArrowToCUDFType<T['type']>>;
/**
* Create a new cudf.Series from SeriesProps or a cudf.Column
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
*
* //using SeriesProps
* const a = Series.new({type: new Int32, data: [1, 2, 3, 4]}); // Int32Series [1, 2, 3, 4]
*
* //using underlying cudf.Column
* const b = Series.new(a._col); // Int32Series [1, 2, 3, 4]
* ```
*/
static new<T extends AbstractSeries>(input: T): T;
static new<T extends DataType>(input: Column<T>): Series<T>;
static new<T extends DataType>(input: SeriesProps<T>): Series<T>;
/**
* Create a new cudf.Int8Series
*
* @example
* ```typescript
* import {
* Series,
* Int8Series,
* Int8
* } from '@rapidsai/cudf';
*
* // Int8Series [1, 2, 3]
* const a = Series.new(new Int8Array([1, 2, 3]));
* const b = Series.new(new Int8Buffer([1, 2, 3]));
* ```
*/
static new(input: Int8Array|Int8Buffer): Series<Int8>;
/**
* Create a new cudf.Int16Series
*
* @example
* ```typescript
* import {
* Series,
* Int16Series,
* Int16
* } from '@rapidsai/cudf';
*
* // Int16Series [1, 2, 3]
* const a = Series.new(new Int16Array([1, 2, 3]));
* const b = Series.new(new Int16Buffer([1, 2, 3]));
* ```
*/
static new(input: Int16Array|Int16Buffer): Series<Int16>;
/**
* Create a new cudf.Int32Series
*
* @example
* ```typescript
* import {
* Series,
* Int32Series,
* Int32
* } from '@rapidsai/cudf';
*
* // Int32Series [1, 2, 3]
* const a = Series.new(new Int32Array([1, 2, 3]));
* const b = Series.new(new Int32Buffer([1, 2, 3]));
* ```
*/
static new(input: Int32Array|Int32Buffer): Series<Int32>;
/**
* Create a new cudf.Uint8Series
*
* @example
* ```typescript
* import {
* Series,
* Uint8Series,
* Uint8
* } from '@rapidsai/cudf';
*
* // Uint8Series [1, 2, 3]
* const a = Series.new(new Uint8Array([1, 2, 3]));
* const b = Series.new(new Uint8Buffer([1, 2, 3]));
* const c = Series.new(new Uint8ClampedArray([1, 2, 3]));
* const d = Series.new(new Uint8ClampedBuffer([1, 2, 3]));
* ```
*/
static new(input: Uint8Array|Uint8Buffer|Uint8ClampedArray|Uint8ClampedBuffer): Series<Uint8>;
/**
* Create a new cudf.Uint16Series
*
* @example
* ```typescript
* import {
* Series,
* Uint16Series,
* Uint16
* } from '@rapidsai/cudf';
*
* // Uint16Series [1, 2, 3]
* const a = Series.new(new Uint16Array([1, 2, 3]));
* const b = Series.new(new Uint16Buffer([1, 2, 3]));
* ```
*/
static new(input: Uint16Array|Uint16Buffer): Series<Uint16>;
/**
* Create a new cudf.Uint32Series
*
* @example
* ```typescript
* import {
* Series,
* Uint32Series,
* Uint32
* } from '@rapidsai/cudf';
*
* // Uint32Series [1, 2, 3]
* const a = Series.new(new Uint32Array([1, 2, 3]));
* const b = Series.new(new Uint32Buffer([1, 2, 3]));
* ```
*/
static new(input: Uint32Array|Uint32Buffer): Series<Uint32>;
/**
* Create a new cudf.Uint64Series
*
* @example
* ```typescript
* import {
* Series,
* Uint64Series,
* Uint64
* } from '@rapidsai/cudf';
*
* // Uint64Series [1n, 2n, 3n]
* const a = Series.new(new BigUint64Array([1n, 2n, 3n]));
* const b = Series.new(new Uint64Buffer([1n, 2n, 3n]));
* ```
*/
static new(input: BigUint64Array|Uint64Buffer): Series<Uint64>;
/**
* Create a new cudf.Float32Series
*
* @example
* ```typescript
* import {
* Series,
* Float32Series,
* Float32
* } from '@rapidsai/cudf';
*
* // Float32Series [1, 2, 3]
* const a = Series.new(new Float32Array([1, 2, 3]));
* const b = Series.new(new Float32Buffer([1, 2, 3]));
* ```
*/
static new(input: Float32Array|Float32Buffer): Series<Float32>;
/**
* Create a new cudf.StringSeries
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // StringSeries ["foo", "bar", "test", null]
* const a = Series.new(["foo", "bar", "test", null]);
* ```
*/
static new(input: (string|null|undefined)[]): Series<Utf8String>;
/**
* Create a new cudf.Float64Series
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series [1, 2, 3, null, 4]
* const a = Series.new([1, 2, 3, undefined, 4]);
* ```
*/
static new(input: (number|null|undefined)[]|Float64Array|Float64Buffer): Series<Float64>;
/**
* Create a new cudf.Int64Series
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Int64Series [1n, 2n, 3n, null, 4n]
* const a = Series.new([1n, 2n, 3n, undefined, 4n]);
* ```
*/
static new(input: (bigint|null|undefined)[]|BigInt64Array|Int64Buffer): Series<Int64>;
/**
* Create a new cudf.Bool8Series
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Bool8Series [true, false, null, false]
* const a = Series.new([true, false, undefined, false]);
* ```
*/
static new(input: (boolean|null|undefined)[]): Series<Bool8>;
/**
* Create a new cudf.TimestampMillisecondSeries
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // TimestampMillisecondSeries [2021-05-13T00:00:00.000Z, null, 2021-05-13T00:00:00.000Z,
* null] const a = Series.new([new Date(), undefined, new Date(), undefined]);
* ```
*/
static new(input: (Date|null|undefined)[]): Series<TimestampMillisecond>;
/**
* Create a new cudf.ListSeries that contain cudf.StringSeries elements.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // ListSeries [["foo", "bar"], ["test", null]]
* const a = Series.new([["foo", "bar"], ["test",null]]);
* a.getValue(0) // StringSeries ["foo", "bar"]
* a.getValue(1) // StringSeries ["test", null]
* ```
*/
static new(input: (string|null|undefined)[][]): Series<List<Utf8String>>;
/**
* Create a new cudf.ListSeries that contain cudf.Float64Series elements.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // ListSeries [[1, 2], [3, null, 4]]
* const a = Series.new([[1, 2], [3, undefined, 4]]);
* a.getValue(0) // Float64Series [1, 2]
* a.getValue(1) // Float64Series [3, null, 4]
* ```
*/
static new(input: (number|null|undefined)[][]): Series<List<Float64>>;
/**
* Create a new cudf.ListSeries that contain cudf.Int64Series elements.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // ListSeries [[1n, 2n], [3n, null, 4n]]
* const a = Series.new([[1n, 2n], [3n, undefined, 4n]]);
* a.getValue(0) // Int64Series [1n, 2n]
* a.getValue(1) // Int64Series [3n, null, 4n]
* ```
*/
static new(input: (bigint|null|undefined)[][]): Series<List<Int64>>;
/**
* Create a new cudf.ListSeries that contain cudf.Bool8Series elements.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // ListSeries [[true, false], [null, false]]
* const a = Series.new([[true, false], [undefined, false]]);
* a.getValue(0) // Bool8Series [true, false]
* a.getValue(1) // Bool8Series [null, false]
* ```
*/
static new(input: (boolean|null|undefined)[][]): Series<List<Bool8>>;
/**
* Create a new cudf.ListSeries that contain cudf.TimestampMillisecondSeries elements.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // ListSeries [[2021-05-13T00:00:00.000Z, null], [null, 2021-05-13T00:00:00.000Z]]
* const a = Series.new([[new Date(), undefined], [undefined, new Date()]]);
* a.getValue(0) // TimestampMillisecondSeries [2021-05-13T00:00:00.000Z, null]
* a.getValue(1) // TimestampMillisecondSeries [null, 2021-05-13T00:00:00.000Z]
* ```
*/
static new(input: (Date|null|undefined)[][]): Series<List<TimestampMillisecond>>;
static new<T extends readonly unknown[]>(input: T):
Series<ArrowToCUDFType<JavaScriptArrayDataType<T>>>;
static new<T extends DataType>(input: AbstractSeries<T>|Column<T>|SeriesProps<T>|arrow.Vector<T>|
(string|null|undefined)[]|(number|null|undefined)[]|
(bigint|null|undefined)[]|(boolean|null|undefined)[]|
(Date|null|undefined)[]|(string|null|undefined)[][]|
(number|null|undefined)[][]|(bigint|null|undefined)[][]|
(boolean|null|undefined)[][]|(Date|null|undefined)[][]): Series<T>;
static new<T extends DataType>(input: any) {
return columnToSeries(asColumn<T>(input)) as any as Series<T>;
}
/**
* Constructs a Series from a text file path.
*
* @note If delimiter is omitted, the default is ''.
*
* @param filepath Path of the input file.
* @param delimiter Optional delimiter.
*
* @returns StringSeries from the file, split by delimiter.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const infile = Series.readText('./inputAsciiFile.txt')
* ```
*/
public static readText(filepath: string, delimiter: string): Series<Utf8String> {
return Series.new(Column.readText(filepath, delimiter ?? ''));
}
/**
* Constructs a Series with a sequence of values.
*
* @note If init is omitted, the default is 0.
* @note If step is omitted, the default is 1.
* @note If type is omitted, the default is Int32.
*
* @param opts Options for creating the sequence
* @returns Series with the sequence
*
* @example
* ```typescript
* import {Series, Int64, Float32} from '@rapidsai/cudf';
*
* Series.sequence({size: 5}).toArray() // Int32Array[0, 1, 2, 3, 4]
* Series.sequence({size: 5, init: 5}).toArray() // Int32Array[5, 6, 7, 8, 9]
* Series
* .sequence({ size: 5, init: 0, type: new Int64 })
* .toArray() // BigInt64Array[0n, 1n, 2n, 3n, 4n]
* Series
* .sequence({ size: 5, step: 2, init: 1, type: new Float32 })
* .toArray() // Float32Array[1, 3, 5, 7, 9]
* ```
*/
static sequence<U extends Numeric = Int32>(opts: {
size: number;
type?: U; //
init?: U['scalarType'];
step?: U['scalarType'];
memoryResource?: MemoryResource;
}): Series<U> {
const type = opts.type ?? new Int32;
const init = new Scalar({type, value: <any>opts.init ?? 0}) as Scalar<U>;
const step = new Scalar({type, value: <any>opts.step ?? 1}) as Scalar<U>;
return Series.new(Column.sequence<U>(opts.size, init, step, opts.memoryResource));
}
/** @ignore */
declare public _col: Column<T>;
protected constructor(col: Column<T>) { DISPOSER.add(this._col = col); }
/**
* The data type of elements in the underlying data.
*/
get type() { return this._col.type; }
/**
* The DeviceBuffer for for the validity bitmask in GPU memory.
*/
get mask() { return this._col.mask; }
/**
* The offset of elements in this Series underlying Column.
*/
get offset() { return this._col.offset; }
/**
* The number of elements in this Series.
*/
get length() { return this._col.length; }
/**
* A boolean indicating whether a validity bitmask exists.
*/
get nullable() { return this._col.nullable; }
/**
* Whether the Series contains null elements.
*/
get hasNulls() { return this._col.hasNulls; }
/**
* The number of null elements in this Series.
*/
get nullCount() { return this._col.nullCount; }
/**
* The number of child columns in this Series.
*/
get numChildren() { return this._col.numChildren; }
/**
* Casts the values to a new dtype (similar to `static_cast` in C++).
*
* @param dataType The new dtype.
* @param memoryResource The optional MemoryResource used to allocate the result Series's device
* memory.
* @returns Series of same size as the current Series containing result of the `cast` operation.
* @example
* ```typescript
* import {Series, Bool8, Int32} from '@rapidsai/cudf';
*
* const a = Series.new({type:new Int32, data: [1,0,1,0]});
*
* a.cast(new Bool8); // Bool8Series [true, false, true, false];
* ```
*/
cast<R extends DataType>(dataType: R, memoryResource?: MemoryResource): Series<R> {
return new CastVisitor(this, memoryResource).visit(dataType);
}
// clang-format off
/* eslint-disable @typescript-eslint/no-unused-vars */
_castAsBool8(_memoryResource?: MemoryResource): Series<Bool8> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Bool8 unimplemented`); }
_castAsInt8(_memoryResource?: MemoryResource): Series<Int8> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Int8 unimplemented`); }
_castAsInt16(_memoryResource?: MemoryResource): Series<Int16> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Int16 unimplemented`); }
_castAsInt32(_memoryResource?: MemoryResource): Series<Int32> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Int32 unimplemented`); }
_castAsInt64(_memoryResource?: MemoryResource): Series<Int64> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Int64 unimplemented`); }
_castAsUint8(_memoryResource?: MemoryResource): Series<Uint8> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Uint8 unimplemented`); }
_castAsUint16(_memoryResource?: MemoryResource): Series<Uint16> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Uint16 unimplemented`); }
_castAsUint32(_memoryResource?: MemoryResource): Series<Uint32> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Uint32 unimplemented`); }
_castAsUint64(_memoryResource?: MemoryResource): Series<Uint64> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Uint64 unimplemented`); }
_castAsFloat32(_memoryResource?: MemoryResource): Series<Float32> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Float32 unimplemented`); }
_castAsFloat64(_memoryResource?: MemoryResource): Series<Float64> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to Float64 unimplemented`); }
_castAsString(_memoryResource?: MemoryResource): Series<Utf8String> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to String unimplemented`); }
_castAsTimeStampDay(_memoryResource?: MemoryResource): Series<TimestampDay> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to TimeStampDay unimplemented`); }
_castAsTimeStampSecond(_memoryResource?: MemoryResource): Series<TimestampSecond> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to TimeStampSecond unimplemented`); }
_castAsTimeStampMillisecond(_memoryResource?: MemoryResource): Series<TimestampMillisecond> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to TimeStampMillisecond unimplemented`); }
_castAsTimeStampMicrosecond(_memoryResource?: MemoryResource): Series<TimestampMicrosecond> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to TimeStampMicrosecond unimplemented`); }
_castAsTimeStampNanosecond(_memoryResource?: MemoryResource): Series<TimestampNanosecond> { throw new Error(`cast from ${arrow.Type[this.type.typeId]} to TimeStampNanosecond unimplemented`); }
/* eslint-enable @typescript-eslint/no-unused-vars */
// clang-format on
_castAsCategorical<R extends Categorical>(type: R, memoryResource?: MemoryResource): Series<R> {
const categories = scope(() => {
const uniq = scope(() => {
return new DataFrame({value: this, order: Series.sequence({size: this.length})})
.groupBy({by: 'value'})
.nth(0);
});
return uniq.sortValues({order: {ascending: true}}, memoryResource).get('value');
}, [this]);
const codes = this.encodeLabels(categories, undefined, undefined, memoryResource);
return Series.new<R>({
type,
length: codes.length,
nullMask: this.mask,
children: [codes, categories.cast(type.dictionary)]
});
}
/**
* Concat a Series to the end of the caller, returning a new Series of a common dtype.
*
* @param other The Series to concat to the end of the caller.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* Series.new([1, 2, 3]).concat(Series.new([4, 5, 6])) // [1, 2, 3, 4, 5, 6]
* ```
*/
concat<R extends Series<DataType>>(other: R, memoryResource?: MemoryResource):
Series<CommonType<T, R['type']>> {
type U = typeof type;
const type = findCommonType(this.type, other.type);
const lhs = <Column<U>>(compareTypes(type, this.type) ? this._col : this.cast(type)._col);
const rhs = <Column<U>>(compareTypes(type, other.type) ? other._col : other.cast(type)._col);
return Series.new(lhs.concat(rhs, memoryResource));
}
/**
* Return the number of non-null elements in the Series.
*
* @returns The number of non-null elements
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* Series.new([1, 2, 3]).countNonNulls(); // 3
* Series.new([1, null, 3]).countNonNulls(); // 2
* ```
*/
countNonNulls(): number { return this._col.length - this._col.nullCount; }
/**
* @summary Explicitly free the device memory associated with this Series.
*/
dispose() { this._col.dispose(); }
/**
* Encode the Series values into integer labels.
*
*
* @param categories The optional Series of values to encode into integers. Defaults to the
* unique elements in this Series.
* @param type The optional integer DataType to use for the returned Series. Defaults to
* Uint32.
* @param nullSentinel The optional value used to indicate missing category. Defaults to -1.
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
* @returns A sequence of encoded integer labels with values between `0` and `n-1`
* categories, and `nullSentinel` for any null values
*/
encodeLabels<R extends Integral = Uint32>(categories: Series<T> = this.unique(true),
type: R = new Uint32 as R,
nullSentinel: R['scalarType'] = -1,
memoryResource?: MemoryResource): Series<R> {
return scope(() => {
try {
// If there is a failure casting to the current dtype, catch the exception and return
// encoded labels with all values set to `nullSentinel`, since this means the Column
// cannot contain any of the encoded categories.
if (!compareTypes(this.type, categories.type)) { categories = categories.cast(this.type); }
} catch {
return Series.sequence(
{type, init: nullSentinel, step: 0, memoryResource, size: this.length});
}
// 1. Join this Series' values with the `categories` Series to determine the index
// positions (i.e. `codes`) of the values to keep.
const codes = scope(() => {
const lhs = new DataFrame(
{value: this, order: Series.sequence({type: new Uint32, size: this.length})});
const rhs = new DataFrame(
{value: categories, codes: Series.sequence({type, size: categories.length})});
return lhs.join({on: ['value'], how: 'left', nullEquality: true, other: rhs})
.drop(['value'])
// 2. Sort the codes by the original value's position in this Series.
.sortValues({order: {ascending: true}})
.get('codes');
});
// 3. Replace missing codes with `nullSentinel`.
return codes.replaceNulls(nullSentinel, memoryResource) as Series<R>;
}, [this, categories]);
}
/**
* Fills a range of elements in a column out-of-place with a scalar value.
*
* @param begin The starting index of the fill range (inclusive).
* @param end The index of the last element in the fill range (exclusive), default
* this.length
* .
* @param value The scalar value to fill.
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, 2, 3]).fill(0) // [0, 0, 0]
* // StringSeries
* Series.new(["foo", "bar", "test"]).fill("rplc", 0, 1) // ["rplc", "bar", "test"]
* // Bool8Series
* Series.new([true, true, true]).fill(false, 1) // [true, false, false]
* ```
*/
fill(value: T['scalarType'], begin = 0, end = this.length, memoryResource?: MemoryResource):
Series<T> {
return this.__construct(
this._col.fill(new Scalar({type: this.type, value}), begin, end, memoryResource));
}
/**
* Fills a range of elements in-place in a column with a scalar value.
*
* @param begin The starting index of the fill range (inclusive)
* @param end The index of the last element in the fill range (exclusive)
* @param value The scalar value to fill
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, 2, 3]).fillInPlace(0) // [0, 0, 0]
* // StringSeries
* Series.new(["foo", "bar", "test"]).fillInPlace("rplc", 0, 1) // ["rplc", "bar", "test"]
* // Bool8Series
* Series.new([true, true, true]).fillInPlace(false, 1) // [true, false, false]
* ```
*/
fillInPlace(value: T['scalarType'], begin = 0, end = this.length) {
this._col.fillInPlace(new Scalar({type: this.type, value}), begin, end);
return this;
}
/**
* Replace null values with a scalar value.
*
* @param value The scalar value to use in place of nulls.
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, null, 3]).replaceNulls(-1) // [1, -1, 3]
* // StringSeries
* Series.new(["foo", "bar", null]).replaceNulls("rplc") // ["foo", "bar", "rplc"]
* // Bool8Series
* Series.new([null, true, true]).replaceNulls(false) // [true, true, true]
* ```
*/
replaceNulls(value: T['scalarType']|any, memoryResource?: MemoryResource): Series<T>;
/**
* Replace null values with the corresponding elements from another Series.
*
* @param value The Series to use in place of nulls.
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
* const replace = Series.new([10, 10, 10]);
* const replaceBool = Series.new([false, false, false]);
*
* // Float64Series
* Series.new([1, null, 3]).replaceNulls(replace) // [1, 10, 3]
* // StringSeries
* Series.new(["foo", "bar", null]).replaceNulls(replace) // ["foo", "bar", "10"]
* // Bool8Series
* Series.new([null, true, true]).replaceNulls(replaceBool) // [false, true, true]
* ```
*/
replaceNulls(value: Series<T>, memoryResource?: MemoryResource): Series<T>;
replaceNulls(value: any, memoryResource?: MemoryResource): Series<T> {
if (value instanceof Series) {
return this.__construct(this._col.replaceNulls(value._col, memoryResource));
} else {
return this.__construct(
this._col.replaceNulls(new Scalar({type: this.type, value}), memoryResource));
}
}
/**
* Replace null values with the non-null value following the null value in the same series.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, null, 3]).replaceNullsFollowing() // [1, 3, 3]
* // StringSeries
* Series.new(["foo", "bar", null]).replaceNullsFollowing() // ["foo", "bar", null]
* Series.new(["foo", null, "bar"]).replaceNullsFollowing() // ["foo", "bar", "bar"]
* // Bool8Series
* Series.new([null, true, true]).replaceNullsFollowing() // [true, true, true]
* ```
*/
replaceNullsFollowing(memoryResource?: MemoryResource): Series<T> {
return this.__construct(this._col.replaceNulls(true, memoryResource));
}
/**
* Replace null values with the non-null value preceding the null value in the same series.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's
* device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, null, 3]).replaceNullsPreceding() // [1, 1, 3]
* // StringSeries
* Series.new([null, "foo", "bar"]).replaceNullsPreceding() // [null, "foo", "bar"]
* Series.new(["foo", null, "bar"]).replaceNullsPreceding() // ["foo", "foo", "bar"]
* // Bool8Series
* Series.new([true, null, false]).replaceNullsPreceding() // [true, true, false]
* ```
*/
replaceNullsPreceding(memoryResource?: MemoryResource): Series<T> {
return this.__construct(this._col.replaceNulls(false, memoryResource));
}
/**
* Returns a new series with reversed elements.
*
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, 2, 3]).reverse() // [3, 2, 1]
* // StringSeries
* Series.new(["foo", "bar"]).reverse() // ["bar", "foo"]
* // Bool8Series
* Series.new([false, true]).reverse() // [true, false]
* ```
*/
reverse(memoryResource?: MemoryResource): Series<T> {
return this.gather(
Series.sequence({size: this.length, step: -1, init: this.length - 1}), false, memoryResource);
}
/**
* @summary Return sub-selection from a Series using the specified integral indices.
*
* @description Gathers the rows of the source columns according to `selection`, such that row "i"
* in the resulting Series's columns will contain row `selection[i]` from the source columns. The
* number of rows in the result series will be equal to the number of elements in selection. A
* negative value i in the selection is interpreted as i+n, where `n` is the number of rows in
* the source series.
*
* For dictionary columns, the keys column component is copied and not trimmed if the gather
* results in abandoned key elements.
*
* @param indices A Series of 8/16/32-bit signed or unsigned integer indices to gather.
* @param nullify_out_of_bounds If `true`, coerce rows that corresponds to out-of-bounds indices
* in the selection to null. If `false`, skips all bounds checking for selection values. Pass
* false if you are certain that the selection contains only valid indices for better
* performance. If `false` and there are out-of-bounds indices in the selection, the behavior
* is undefined. Defaults to `false`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
*
* const a = Series.new([1,2,3]);
* const b = Series.new(["foo", "bar", "test"]);
* const c = Series.new([true, false, true]);
* const selection = Series.new({type: new Int32, data: [0,2]});
*
* a.gather(selection) // Float64Series [1,3]
* b.gather(selection) // StringSeries ["foo", "test"]
* c.gather(selection) // Bool8Series [true, true]
* ```
*/
gather(indices: Series<IndexType>|number[],
nullify_out_of_bounds = false,
memoryResource?: MemoryResource): Series<T> {
const map = Array.isArray(indices) ? Series.new(indices).cast(new Uint32) : indices;
return this.__construct(this._col.gather(map._col, nullify_out_of_bounds, memoryResource));
}
/**
* Return a copy of this Series.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const a = Series.new(["foo", "bar", "test"]);
*
* a.copy() // StringSeries ["foo", "bar", "test"]
* ```
*/
copy(memoryResource?: MemoryResource): Series<T> {
return this.__construct(this._col.copy(memoryResource));
}
/**
* Returns the n largest element(s).
*
* @param n The number of values to retrieve.
* @param keep Determines whether to keep the first or last of any duplicate values.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const a = Series.new([4, 6, 8, 10, 12, 1, 2]);
* const b = Series.new(["foo", "bar", "test"]);
*
* a.nLargest(); // [12, 10, 8, 6, 4]
* b.nLargest(1); // ["test"]
* a.nLargest(-1); // []
* ```
*/
nLargest(n = 5, keep: keyof typeof DuplicateKeepOption = 'first'): Series<T> {
return _nLargestOrSmallest(this as Series, true, n, keep);
}
/**
* Returns the n smallest element(s).
*
* @param n The number of values to retrieve.
* @param keep Determines whether to keep the first or last of any duplicate values.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const a = Series.new([4, 6, 8, 10, 12, 1, 2]);
* const b = Series.new(["foo", "bar", "test"]);
*
* a.nSmallest(); // [1, 2, 4, 6, 8]
* b.nSmallest(1); // ["bar"]
* a.nSmallest(-1); // []
* ```
*/
nSmallest(n = 5, keep: keyof typeof DuplicateKeepOption = 'first'): Series<T> {
return _nLargestOrSmallest(this as Series, false, n, keep);
}
/**
* Returns the first n rows.
*
* @param n The number of rows to return.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const a = Series.new([4, 6, 8, 10, 12, 1, 2]);
* const b = Series.new(["foo", "bar", "test"]);
*
* a.head(); // [4, 6, 8, 10, 12]
* b.head(1); // ["foo"]
* a.head(-1); // throws index out of bounds error
* ```
*/
head(n = 5): Series<T> {
if (n < 0) { throw new Error('Index provided is out of bounds'); }
const selection = Series.sequence(
{type: new Int32, size: n < this._col.length ? n : this._col.length, init: 0});
return this.__construct(this._col.gather(selection._col, false));
}
/**
* Returns the last n rows.
*
* @param n The number of rows to return.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* const a = Series.new([4, 6, 8, 10, 12, 1, 2]);
* const b = Series.new(["foo", "bar", "test"]);
*
* a.tail(); // [8, 10, 12, 1, 2]
* b.tail(1); // ["test"]
* a.tail(-1); // throws index out of bounds error
* ```
*/
tail(n = 5): Series<T> {
if (n < 0) { throw new Error('Index provided is out of bounds'); }
const length = n < this._col.length ? n : this._col.length;
const selection =
Series.sequence({type: new Int32, size: length, init: this._col.length - length});
return this.__construct(this._col.gather(selection._col, false));
}
/**
* Scatters single value into this Series according to provided indices.
*
* @param value A column of values to be scattered in to this Series
* @param indices A column of integral indices that indicate the rows in the this Series to be
* replaced by `value`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
* const a = Series.new({type: new Int32, data: [0, 1, 2, 3, 4]});
* const indices = Series.new({type: new Int32, data: [2, 4]});
* const indices_out_of_bounds = Series.new({type: new Int32, data: [5, 6]});
*
* a.scatter(-1, indices); // returns [0, 1, -1, 3, -1];
* a.scatter(-1, indices_out_of_bounds, true) // throws index out of bounds error
*
* ```
*/
scatter(value: T['scalarType'],
indices: Series<IndexType>|number[],
memoryResource?: MemoryResource): Series<T>;
/**
* Scatters a column of values into this Series according to provided indices.
*
* @param value A value to be scattered in to this Series
* @param indices A column of integral indices that indicate the rows in the this Series to be
* replaced by `value`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
* const a = Series.new({type: new Int32, data: [0, 1, 2, 3, 4]});
* const b = Series.new({type: new Int32, data: [200, 400]});
* const indices = Series.new({type: new Int32, data: [2, 4]});
* const indices_out_of_bounds = Series.new({type: new Int32, data: [5, 6]});
*
* a.scatter(b, indices); // returns [0, 1, 200, 3, 400];
* a.scatter(b, indices_out_of_bounds, true) // throws index out of bounds error
* ```
*/
scatter(values: Series<T>, indices: Series<IndexType>|number[], memoryResource?: MemoryResource):
Series<T>;
scatter(source: Series<T>|T['scalarType'],
indices: Series<IndexType>|number[],
memoryResource?: MemoryResource): Series<T> {
const dst = new Table({columns: [this._col]});
const map = Array.isArray(indices) ? Series.new(indices).cast(new Uint32) : indices;
if (source instanceof Series) {
const src = new Table({columns: [source.cast(this.type)._col]});
return this.__construct(dst.scatterTable(src, map._col, memoryResource).getColumnByIndex(0));
}
const src = [new Scalar({type: this.type, value: source})];
return this.__construct(dst.scatterScalar(src, map._col, memoryResource).getColumnByIndex(0));
}
/**
* Return a sub-selection of this Series using the specified boolean mask.
*
* @param mask A Series of boolean values for whose corresponding element in this Series
* will be selected or ignored.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {Series} from "@rapidsai/cudf";
* const mask = Series.new([true, false, true]);
*
* // Float64Series
* Series.new([1, 2, 3]).filter(mask) // [1, 3]
* // StringSeries
* Series.new(["foo", "bar", "test"]).filter(mask) // ["foo", "test"]
* // Bool8Series
* Series.new([false, true, true]).filter(mask) // [false, true]
* ```
*/
filter(mask: Series<Bool8>, memoryResource?: MemoryResource): Series<T> {
return this.__construct(this._col.applyBooleanMask(mask._col, memoryResource));
}
/**
* set values at the specified indices
*
* @param indices the indices in this Series to set values for
* @param values the values to set at Series of indices
*
* @example
* ```typescript
* import {Series, Int32} from '@rapidsai/cudf';
* const a = Series.new({type: new Int32, data: [0, 1, 2, 3, 4]});
* const values = Series.new({type: new Int32, data: [200, 400]});
* const indices = Series.new({type: new Int32, data: [2, 4]});
*
* a.setValues(indices, values); // inplace update [0, 1, 200, 3, 400];
* a.setValues(indices, -1); // inplace update [0, 1, -1, 3, -1];
* ```
*/
setValues(indices: Series<Int32>|number[], values: Series<T>|T['scalarType']): void {
this._col = this.scatter(values, indices)._col as Column<T>;
}
/**
* Copy the underlying device memory to host, and return an Iterator of the values.
*/
[Symbol.iterator](): IterableIterator<T['TValue']|null> {
return this.toArrow()[Symbol.iterator]();
}
/**
* Copy the underlying device memory to host and return an Array (or TypedArray) of the values.
* @returns
*/
toArray() { return this.toArrow().toArray(); }
/**
* Return a string with a tabular representation of the Series, pretty-printed according to the
* options given.
*
* @param options
*/
toString(options: DisplayOptions&{name?: string} = {}) {
return new DataFrame({[options.name ?? '0']: this}).toString(options);
}
/**
*
* @param mask The null-mask. Valid values are marked as 1; otherwise 0. The
* mask bit given the data index idx is computed as:
* ```
* (mask[idx // 8] >> (idx % 8)) & 1
* ```
* @param nullCount The number of null values. If None, it is calculated
* automatically.
*/
setNullMask(mask: MemoryData|ArrayLike<number>|ArrayLike<bigint>, nullCount?: number) {
this._col.setNullMask(mask, nullCount);
}
/**
* Copy a Series to an Arrow vector in host memory
*/
toArrow() {
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
return new DataFrame({0: this._col}).toArrow().getChildAt<T>(0)!;
}
/**
* Generate an ordering that sorts the Series in a specified way.
*
* @param ascending whether to sort ascending (true) or descending (false)
* @param null_order whether nulls should sort before or after other values
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @returns Series containting the permutation indices for the desired sort order
*
* @example
* ```typescript
* import {Series, NullOrder} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([50, 40, 30, 20, 10, 0]).orderBy(false) // [0, 1, 2, 3, 4, 5]
* Series.new([50, 40, 30, 20, 10, 0]).orderBy(true) // [5, 4, 3, 2, 1, 0]
*
* // StringSeries
* Series.new(["a", "b", "c", "d", "e"]).orderBy(false) // [4, 3, 2, 1, 0]
* Series.new(["a", "b", "c", "d", "e"]).orderBy(true) // [0, 1, 2, 3, 4]
*
* // Bool8Series
* Series.new([true, false, true, true, false]).orderBy(true) // [1, 4, 0, 2, 3]
* Series.new([true, false, true, true, false]).orderBy(false) // [0, 2, 3, 1, 4]
*
* // NullOrder usage
* Series.new([50, 40, 30, 20, 10, null]).orderBy(false, 'before') // [0, 1, 2, 3, 4, 5]
* Series.new([50, 40, 30, 20, 10, null]).orderBy(false, 'after') // [5, 0, 1, 2, 3, 4]
* ```
*/
orderBy(ascending = true,
null_order: keyof typeof NullOrder = 'after',
memoryResource?: MemoryResource) {
return Series.new(new Table({
columns: [this._col]
}).orderBy([ascending], [null_order === 'before'], memoryResource));
}
/**
* Generate a new Series that is sorted in a specified way.
*
* @param ascending whether to sort ascending (true) or descending (false)
* Default: true
* @param null_order whether nulls should sort before or after other values
* Default: before
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @returns Sorted values
*
* @example
* ```typescript
* import {Series, NullOrder} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([50, 40, 30, 20, 10, 0]).sortValues(false) // [50, 40, 30, 20, 10, 0]
* Series.new([50, 40, 30, 20, 10, 0]).sortValues(true) // [0, 10, 20, 30, 40, 50]
*
* // StringSeries
* Series.new(["a", "b", "c", "d", "e"]).sortValues(false) // ["e", "d", "c", "b", "a"]
* Series.new(["a", "b", "c", "d", "e"]).sortValues(true) // ["a", "b", "c", "d", "e"]
*
* // Bool8Series
* Series.new([true, false, true, true, false]).sortValues(true) // [false, false, true,
* true, true] Series.new([true, false, true, true, false]).sortValues(false) // [true,
* true, true, false, false]
*
* // NullOrder usage
* Series.new([50, 40, 30, 20, 10, null]).sortValues(false, 'before') // [50, 40, 30, 20,
* 10, null]
*
* Series.new([50, 40, 30, 20, 10, null]).sortValues(false, 'after') // [null, 50, 40, 30,
* 20, 10]
* ```
*/
sortValues(ascending = true,
null_order: keyof typeof NullOrder = 'after',
memoryResource?: MemoryResource): Series<T> {
return this.gather(this.orderBy(ascending, null_order), false, memoryResource);
}
/**
* Creates a Series of `BOOL8` elements where `true` indicates the value is null and `false`
* indicates the value is valid.
*
* @param memoryResource Memory resource used to allocate the result Column's device memory.
* @returns A non-nullable Series of `BOOL8` elements with `true` representing `null`
* values.
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, null, 3]).isNull() // [false, true, false]
* // StringSeries
* Series.new(["foo", "bar", null]).isNull() // [false, false, true]
* // Bool8Series
* Series.new([true, true, null]).isNull() // [false, false, true]
* ```
*/
isNull(memoryResource?: MemoryResource) { return Series.new(this._col.isNull(memoryResource)); }
/**
* Creates a Series of `BOOL8` elements where `true` indicates the value is valid and
* `false` indicates the value is null.
*
* @param memoryResource Memory resource used to allocate the result Column's device memory.
* @returns A non-nullable Series of `BOOL8` elements with `false` representing `null`
* values.
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, null, 3]).isNotNull() // [true, false, true]
* // StringSeries
* Series.new(["foo", "bar", null]).isNotNull() // [true, true, false]
* // Bool8Series
* Series.new([true, true, null]).isNotNull() // [true, true, false]
* ```
*/
isNotNull(memoryResource?: MemoryResource) {
return Series.new(this._col.isValid(memoryResource));
}
/**
* drop Null values from the series
* @param memoryResource Memory resource used to allocate the result Column's device memory.
* @returns series without Null values
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([1, undefined, 3]).dropNulls() // [1, 3]
* Series.new([1, null, 3]).dropNulls() // [1, 3]
* Series.new([1, , 3]).dropNulls() // [1, 3]
*
* // StringSeries
* Series.new(["foo", "bar", undefined]).dropNulls() // ["foo", "bar"]
* Series.new(["foo", "bar", null]).dropNulls() // ["foo", "bar"]
* Series.new(["foo", "bar", ,]).dropNulls() // ["foo", "bar"]
*
* // Bool8Series
* Series.new([true, true, undefined]).dropNulls() // [true, true]
* Series.new([true, true, null]).dropNulls() // [true, true]
* Series.new([true, true, ,]).dropNulls() // [true, true]
* ```
*/
dropNulls(memoryResource?: MemoryResource): Series<T> {
return this.__construct(this._col.dropNulls(memoryResource));
}
/**
* @summary Hook for specialized Series to override when constructing from a C++ Column.
*/
protected __construct(col: Column<T>): Series<T> {
return Series.new(Object.assign(col, {type: transferFields(this.type, col.type)}));
}
/**
* Returns an object with keys "value" and "count" whose respective values are new Series
* containing the unique values in the original series and the number of times they occur
* in the original series.
* @returns object with keys "value" and "count"
*/
valueCounts(): {count: Int32Series, value: Series<T>} {
const df = new DataFrame({count: this, value: this}).groupBy({by: 'value'}).count();
return {
count: df.get('count'),
value: df.get('value'),
};
}
/**
* Returns a new Series with only the unique values that were found in the original
*
* @param nullsEqual Determines whether nulls are handled as equal values.
* @param memoryResource Memory resource used to allocate the result Column's device memory.
* @returns series without duplicate values
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([null, null, 1, 2, 3, 3]).unique(true) // [null, 1, 2, 3]
* Series.new([null, null, 1, 2, 3, 3]).unique(false) // [null, null, 1, 2, 3]
* ```
*/
unique(nullsEqual = true, memoryResource?: MemoryResource) {
return this.dropDuplicates(true, nullsEqual, true, memoryResource);
}
/**
* Returns a new Series with duplicate values from the original removed
*
* @param keep Determines whether or not to keep the duplicate items.
* @param nullsEqual Determines whether nulls are handled as equal values.
* @param nullsFirst Determines whether null values are inserted before or after non-null
* values.
* @param memoryResource Memory resource used to allocate the result Column's device memory.
* @returns series without duplicate values
*
* @example
* ```typescript
* import {Series} from '@rapidsai/cudf';
*
* // Float64Series
* Series.new([4, null, 1, 2, null, 3, 4]).dropDuplicates(
* true,
* true,
* true
* ) // [null, 1, 2, 3, 4]
*
* Series.new([4, null, 1, 2, null, 3, 4]).dropDuplicates(
* false,
* true,
* true
* ) // [1, 2, 3]
* ```
*/
dropDuplicates(keep = true,
nullsEqual = true,
nullsFirst = true,
memoryResource?: MemoryResource) {
return new DataFrame({0: this})
.dropDuplicates(keep ? 'first' : 'none', nullsEqual, nullsFirst, ['0'], memoryResource)
.get('0');
}
}
// eslint-disable-next-line @typescript-eslint/no-redeclare
export const Series = AbstractSeries;
import {Bool8Series} from './series/bool';
import {CategoricalSeries} from './series/categorical';
import {Float32Series, Float64Series} from './series/float';
import {
Int8Series,
Int16Series,
Int32Series,
Uint8Series,
Uint16Series,
Uint32Series,
Int64Series,
Uint64Series
} from './series/integral';
import {StringSeries} from './series/string';
import {ListSeries} from './series/list';
import {StructSeries} from './series/struct';
import {
TimestampDaySeries,
TimestampMicrosecondSeries,
TimestampMillisecondSeries,
TimestampNanosecondSeries,
TimestampSecondSeries
} from './series/timestamp';
export {
Bool8Series,
Float32Series,
Float64Series,
Int8Series,
Int16Series,
Int32Series,
Uint8Series,
Uint16Series,
Uint32Series,
Int64Series,
Uint64Series,
StringSeries,
ListSeries,
StructSeries,
};
function inferType(value: any[]): DataType {
if (value.length === 0) { return new Float64; }
let nullsCount = 0;
let arraysCount = 0;
let objectsCount = 0;
let numbersCount = 0;
let stringsCount = 0;
let bigintsCount = 0;
let booleansCount = 0;
let datesCount = 0;
value.forEach((val) => {
if (val == null) { return ++nullsCount; }
switch (typeof val) {
case 'bigint': return ++bigintsCount;
case 'boolean': return ++booleansCount;
case 'number': return ++numbersCount;
case 'string': return ++stringsCount;
case 'object':
if (Array.isArray(val)) {
return ++arraysCount;
} else if (Object.prototype.toString.call(val) === '[object Date]') {
return ++datesCount;
} else {
return ++objectsCount;
}
}
throw new TypeError(
'Unable to infer Series type from input values, explicit type declaration expected');
});
if (numbersCount + nullsCount === value.length) {
return new Float64;
} else if (stringsCount + nullsCount === value.length) {
return new Utf8String;
} else if (bigintsCount + nullsCount === value.length) {
return new Int64;
} else if (booleansCount + nullsCount === value.length) {
return new Bool8;
} else if (datesCount + nullsCount === value.length) {
return new TimestampMillisecond;
} else if (arraysCount + nullsCount === value.length) {
const childType = inferType(value[value.findIndex((ary) => ary != null)]);
if (value.every((ary) => ary == null || compareTypes(childType, inferType(ary)))) {
return new List(new arrow.Field('', childType));
}
} else if (objectsCount + nullsCount === value.length) {
const fields = new Map<string, arrow.Field>();
value.forEach((val) => {
Object.keys(val).forEach((key) => {
if (!fields.has(key)) {
// use the type inferred for the first instance of a found key
fields.set(key, new arrow.Field(key, inferType([val[key]]), true));
}
});
}, {});
return new Struct<any>([...fields.values()]);
}
throw new TypeError(
'Unable to infer Series type from input values, explicit type declaration expected');
}
const arrayToDtype = (value: any[]|MemoryView|ArrayBufferView) => {
switch (value.constructor) {
case Int8Array: return new Int8;
case Int8Buffer: return new Int8;
case Int16Array: return new Int16;
case Int16Buffer: return new Int16;
case Int32Array: return new Int32;
case Int32Buffer: return new Int32;
case BigInt64Array: return new Int64;
case Int64Buffer: return new Int64;
case Uint8Array: return new Uint8;
case Uint8Buffer: return new Uint8;
case Uint8ClampedArray: return new Uint8;
case Uint8ClampedBuffer: return new Uint8;
case Uint16Array: return new Uint16;
case Uint16Buffer: return new Uint16;
case Uint32Array: return new Uint32;
case Uint32Buffer: return new Uint32;
case BigUint64Array: return new Uint64;
case Uint64Buffer: return new Uint64;
case Float32Array: return new Float32;
case Float32Buffer: return new Float32;
case Float64Array: return new Float64;
case Float64Buffer: return new Float64;
default: return inferType(value as any[]);
}
};
function asColumn(value: Int8Array|Int8Buffer): Column<Int8>;
function asColumn(value: Int16Array|Int16Buffer): Column<Int16>;
function asColumn(value: Int32Array|Int32Buffer): Column<Int32>;
function asColumn(value: BigInt64Array|Int64Buffer): Column<Int64>;
function asColumn(value: Uint8Array|Uint8Buffer): Column<Uint8>;
function asColumn(value: Uint8ClampedArray|Uint8ClampedBuffer): Column<Uint8>;
function asColumn(value: Uint16Array|Uint16Buffer): Column<Uint16>;
function asColumn(value: Uint32Array|Uint32Buffer): Column<Uint32>;
function asColumn(value: BigUint64Array|Uint64Buffer): Column<Uint64>;
function asColumn(value: Float32Array|Float32Buffer): Column<Float32>;
function asColumn(value: Float64Array|Float64Buffer): Column<Float64>;
function asColumn<T extends DataType>(value: AbstractSeries<T>|SeriesProps<T> //
|Column<T>|arrow.Vector<T> //
|(string | null | undefined)[] //
|(number | null | undefined)[] //
|(bigint | null | undefined)[] //
|(boolean | null | undefined)[] //
|(Date | null | undefined)[] //
|(string | null | undefined)[][] //
|(number | null | undefined)[][] //
|(bigint | null | undefined)[][] //
|(boolean | null | undefined)[][] //
|(Date | null | undefined)[][]): Column<T>;
function asColumn<T extends DataType>(value: any) {
if (!value) { value = []; }
// Return early if it's already a Series
if (value instanceof AbstractSeries) { return value._col; }
// Return early if it's already a Column
if (value instanceof Column) { return value as Column<T>; }
// If Array/Vector/TypedArray/MemoryView, wrap in a ColumnProps
if (value instanceof arrow.Vector) {
value = {data: value};
} else if (Array.isArray(value) || //
ArrayBuffer.isView(value) || //
value instanceof MemoryView) {
value = {data: value, type: arrayToDtype(value)};
}
let {data, offset} = value;
if (typeof data !== 'object') {
data = undefined;
} else {
// Use C++ Arrow-to-cuDF conversion if `data` is an Arrow Vector
if (data instanceof arrow.Vector) {
// Slice `offset` before converting it to a Column
return fromArrow<T>(typeof offset !== 'number' ? data : data.slice(offset));
}
// If `data` is an Array, convert it to a Vector and use C++ Arrow-to-cuDF conversion
if (Array.isArray(data)) {
return fromArrow<T>(arrow.vectorFromArray(
typeof offset !== 'number' ? data : data.slice(offset), value.type ?? inferType(data)));
}
// If `data.buffer` is a ArrayBuffer, copy it to a DeviceBuffer
if (ArrayBuffer.isView(value) || (data.buffer instanceof ArrayBuffer)) {
if (typeof data.length === 'number') { value.length = data.length; }
data = new DeviceBuffer(typeof offset !== 'number' ? data : data.subarray(offset));
offset = 0;
}
// If `data.buffer` is a DeviceBuffer, propagate its `byteOffset` to ColumnProps
else if (data.buffer instanceof DeviceBuffer) {
if (typeof data.length === 'number') { value.length = data.length; }
offset =
(typeof offset !== 'number' ? 0 : offset) + (data.byteOffset / data.BYTES_PER_ELEMENT);
}
}
return new Column<T>({...value, data, offset, children: value.children?.map(asColumn)});
}
const columnToSeries = (() => {
interface ColumnToSeriesVisitor extends arrow.Visitor {
visit<T extends DataType>(column: Column<T>): Series<T>;
visitMany<T extends DataType>(columns: Column<T>[]): Series<T>[];
getVisitFn<T extends DataType>(column: Column<T>): (column: Column<T>) => Series<T>;
}
/* eslint-disable @typescript-eslint/no-unused-vars */
class ColumnToSeriesVisitor extends arrow.Visitor {
public visit<T extends DataType>(col: Column<T>): Series<T> {
for (let i = -1, n = col.numChildren; ++i < n;) {
// Visit each child to ensure concrete dtypes
this.visit(col.getChild(i));
}
return super.visit(col);
}
public getVisitFn<T extends DataType>(column: Column<T>): (column: Column<T>) => Series<T> {
let {type} = column;
if (!(type instanceof arrow.DataType)) {
type = {...(<any>type), __proto__: arrow.DataType.prototype};
}
return super.getVisitFn(type);
}
public visitBool<T extends Bool8>(col: Column<T>) {
return new (<any>Bool8Series)(Object.assign(col, {type: new Bool8}));
}
public visitInt8<T extends Int8>(col: Column<T>) {
return new (<any>Int8Series)(Object.assign(col, {type: new Int8}));
}
public visitInt16<T extends Int16>(col: Column<T>) {
return new (<any>Int16Series)(Object.assign(col, {type: new Int16}));
}
public visitInt32<T extends Int32>(col: Column<T>) {
return new (<any>Int32Series)(Object.assign(col, {type: new Int32}));
}
public visitInt64<T extends Int64>(col: Column<T>) {
return new (<any>Int64Series)(Object.assign(col, {type: new Int64}));
}
public visitUint8<T extends Uint8>(col: Column<T>) {
return new (<any>Uint8Series)(Object.assign(col, {type: new Uint8}));
}
public visitUint16<T extends Uint16>(col: Column<T>) {
return new (<any>Uint16Series)(Object.assign(col, {type: new Uint16}));
}
public visitUint32<T extends Uint32>(col: Column<T>) {
return new (<any>Uint32Series)(Object.assign(col, {type: new Uint32}));
}
public visitUint64<T extends Uint64>(col: Column<T>) {
return new (<any>Uint64Series)(Object.assign(col, {type: new Uint64}));
}
public visitFloat32<T extends Float32>(col: Column<T>) {
return new (<any>Float32Series)(Object.assign(col, {type: new Float32}));
}
public visitFloat64<T extends Float64>(col: Column<T>) {
return new (<any>Float64Series)(Object.assign(col, {type: new Float64}));
}
public visitUtf8<T extends Utf8String>(col: Column<T>) {
return new (<any>StringSeries)(Object.assign(col, {type: new Utf8String}));
}
public visitDateDay<T extends TimestampDay>(col: Column<T>) {
return new (<any>TimestampDaySeries)(Object.assign(col, {type: new TimestampDay}));
}
public visitDateMillisecond<T extends TimestampMillisecond>(col: Column<T>) {
return new (<any>TimestampMillisecondSeries)(
Object.assign(col, {type: new TimestampMillisecond}));
}
public visitTimestampSecond<T extends TimestampSecond>(col: Column<T>) {
return new (<any>TimestampSecondSeries)(Object.assign(col, {type: new TimestampSecond}));
}
public visitTimestampMillisecond<T extends TimestampMillisecond>(col: Column<T>) {
return new (<any>TimestampMillisecondSeries)(
Object.assign(col, {type: new TimestampMillisecond}));
}
public visitTimestampMicrosecond<T extends TimestampMicrosecond>(col: Column<T>) {
return new (<any>TimestampMicrosecondSeries)(
Object.assign(col, {type: new TimestampMicrosecond}));
}
public visitTimestampNanosecond<T extends TimestampNanosecond>(col: Column<T>) {
return new (<any>TimestampNanosecondSeries)(
Object.assign(col, {type: new TimestampNanosecond}));
}
public visitList<T extends List>(col: Column<T>) {
const {type, nullable} = col.getChild(1); // elements
const {name, metadata} = col.type.children[0];
const childField = arrow.Field.new({name, type, nullable, metadata});
return new (<any>ListSeries)(Object.assign(col, {type: new List(childField)}));
}
public visitStruct<T extends Struct>(col: Column<T>) {
const childFields = col.type.children.map(({name, metadata}, i) => {
const {type, nullable} = col.getChild(i);
return arrow.Field.new({name, type, nullable, metadata});
});
return new (<any>StructSeries)(Object.assign(col, {type: new Struct(childFields)}));
}
public visitDictionary<T extends Categorical>(col: Column<T>) {
const {id, isOrdered} = col.type;
const {type: dictionary} = col.getChild(1); // categories
return new (<any>CategoricalSeries)(
Object.assign(col, {type: new Categorical(dictionary, id, isOrdered)}));
}
// clang-format off
// public visitBinary <T extends Binary>(col: Column<T>) { return new (BinarySeries as any)(col); }
// public visitFixedSizeBinary <T extends FixedSizeBinary>(col: Column<T>) { return new (FixedSizeBinarySeries as any)(col); }
// public visitTimeSecond <T extends TimeSecond>(col: Column<T>) { return new (TimeSecondSeries as any)(col); }
// public visitTimeMillisecond <T extends TimeMillisecond>(col: Column<T>) { return new (TimeMillisecondSeries as any)(col); }
// public visitTimeMicrosecond <T extends TimeMicrosecond>(col: Column<T>) { return new (TimeMicrosecondSeries as any)(col); }
// public visitTimeNanosecond <T extends TimeNanosecond>(col: Column<T>) { return new (TimeNanosecondSeries as any)(col); }
// public visitDecimal <T extends Decimal>(col: Column<T>) { return new (DecimalSeries as any)(col); }
// public visitDenseUnion <T extends DenseUnion>(col: Column<T>) { return new (DenseUnionSeries as any)(col); }
// public visitSparseUnion <T extends SparseUnion>(col: Column<T>) { return new (SparseUnionSeries as any)(col); }
// public visitIntervalDayTime <T extends IntervalDayTime>(col: Column<T>) { return new (IntervalDayTimeSeries as any)(col); }
// public visitIntervalYearMonth <T extends IntervalYearMonth>(col: Column<T>) { return new (IntervalYearMonthSeries as any)(col); }
// public visitFixedSizeList <T extends FixedSizeList>(col: Column<T>) { return new (FixedSizeListSeries as any)(col); }
// public visitMap <T extends Map>(col: Column<T>) { return new (MapSeries as any)(col); }
// clang-format on
}
/* eslint-enable @typescript-eslint/no-unused-vars */
const visitor = new ColumnToSeriesVisitor();
return function columnToSeries<T extends DataType>(column: Column<T>) {
return visitor.visit(column);
};
})();
function _nLargestOrSmallest<T extends DataType>(
series: Series<T>, ascending: boolean, n: number, keep: keyof typeof DuplicateKeepOption):
Series {
if (keep == 'first') {
return series.sortValues(!ascending).head(n < 0 ? 0 : n);
} else if (keep == 'last') {
return n <= 0 ? Series.new({type: series.type, data: new Array(0)})
: series.sortValues(ascending).tail(n).reverse();
} else {
throw new TypeError('keep must be either "first" or "last"');
}
}
function transferFields<T extends DataType>(lhs: T, rhs: T) {
const {children: lhsFields} = lhs;
const {children: rhsFields} = rhs;
if (lhsFields && rhsFields && lhsFields.length && rhsFields.length) {
lhsFields.forEach(({name, type, nullable, metadata}, i) => {
rhsFields[i] = arrow.Field.new({
name,
nullable,
metadata,
type: transferFields(type, rhsFields[i].type),
});
});
}
return rhs;
}
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/groupby.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {MemoryResource} from '@rapidsai/rmm';
import * as CUDF from './addon';
import {Column} from './column';
import {Table} from './table';
import {NullOrder} from './types/enums';
export {GroupByMultiple, GroupByMultipleProps} from './groupby/multiple';
export {GroupBySingle, GroupBySingleProps} from './groupby/single';
export interface GroupByBaseProps {
include_nulls?: boolean;
keys_are_sorted?: boolean;
column_order?: boolean[];
null_precedence?: NullOrder[];
}
export interface GroupByProps extends GroupByBaseProps {
keys: Table;
}
export interface GroupByConstructor {
readonly prototype: GroupBy;
new(props: GroupByProps): GroupBy;
}
export interface GroupBy {
_getGroups(values?: Table,
memoryResource?: MemoryResource): //
{keys: Table, offsets: Int32Array, values?: Table};
_argmax(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_argmin(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_count(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_max(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_mean(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_median(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_min(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_nth(values: Table, memoryResource?: MemoryResource, n?: number, include_nulls?: boolean):
{keys: Table, cols: Column[]};
_nunique(values: Table, memoryResource?: MemoryResource, include_nulls?: boolean):
{keys: Table, cols: Column[]};
_std(values: Table, memoryResource?: MemoryResource, ddof?: number):
{keys: Table, cols: Column[]};
_sum(values: Table, memoryResource?: MemoryResource): //
{keys: Table, cols: Column[]};
_var(values: Table, memoryResource?: MemoryResource, ddof?: number):
{keys: Table, cols: Column[]};
_quantile(values: Table, memoryResource?: MemoryResource, q?: number, interpolation?: number):
{keys: Table, cols: [Column]};
_collect_list(values: Table, memoryResource?: MemoryResource, include_nulls?: boolean):
{keys: Table, cols: Column[]};
_collect_set(values: Table,
memoryResource?: MemoryResource,
include_nulls?: boolean,
nulls_equal?: boolean,
nans_equal?: boolean): //
{keys: Table, cols: Column[]};
}
// eslint-disable-next-line @typescript-eslint/no-redeclare
export const GroupBy: GroupByConstructor = CUDF.GroupBy;
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/groupby.cpp
|
// Copyright (c) 2021-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <node_cudf/groupby.hpp>
#include <node_cudf/table.hpp>
#include <node_cudf/utilities/error.hpp>
#include <node_cudf/utilities/napi_to_cpp.hpp>
#include <cudf/groupby.hpp>
#include <cudf/types.hpp>
#include <node_cuda/utilities/error.hpp>
#include <napi.h>
namespace nv {
//
// Public API
//
Napi::Function GroupBy::Init(Napi::Env const& env, Napi::Object exports) {
return DefineClass(env,
"GroupBy",
{
InstanceMethod<&GroupBy::get_groups>("_getGroups"),
// aggregations
InstanceMethod<&GroupBy::argmax>("_argmax"),
InstanceMethod<&GroupBy::argmin>("_argmin"),
InstanceMethod<&GroupBy::count>("_count"),
InstanceMethod<&GroupBy::max>("_max"),
InstanceMethod<&GroupBy::mean>("_mean"),
InstanceMethod<&GroupBy::median>("_median"),
InstanceMethod<&GroupBy::min>("_min"),
InstanceMethod<&GroupBy::nth>("_nth"),
InstanceMethod<&GroupBy::nunique>("_nunique"),
InstanceMethod<&GroupBy::std>("_std"),
InstanceMethod<&GroupBy::sum>("_sum"),
InstanceMethod<&GroupBy::var>("_var"),
InstanceMethod<&GroupBy::quantile>("_quantile"),
InstanceMethod<&GroupBy::collect_list>("_collect_list"),
InstanceMethod<&GroupBy::collect_set>("_collect_set"),
});
}
GroupBy::wrapper_t GroupBy::New(Napi::Env const& env) {
return EnvLocalObjectWrap<GroupBy>::New(env, {});
}
GroupBy::GroupBy(CallbackArgs const& args) : EnvLocalObjectWrap<GroupBy>(args) {
using namespace cudf;
NapiToCPP::Object props = args[0];
NODE_CUDA_EXPECT(props.Has("keys"), "GroupBy constructor expects options to have a 'keys' field");
if (!Table::IsInstance(props.Get("keys"))) {
NAPI_THROW(Napi::Error::New(args.Env(), "GroupBy constructor 'keys' field expects a Table."));
}
Table::wrapper_t table = props.Get("keys").ToObject();
auto null_handling = null_policy::EXCLUDE;
if (props.Has("include_nulls")) { null_handling = props.Get("include_nulls"); }
auto keys_are_sorted = sorted::NO;
if (props.Has("keys_are_sorted")) { keys_are_sorted = props.Get("keys_are_sorted"); }
std::vector<order> column_order =
props.Has("column_order") ? props.Get("column_order") : NapiToCPP{args.Env().Null()};
std::vector<null_order> null_precedence =
props.Has("null_precedence") ? props.Get("null_precedence") : NapiToCPP{args.Env().Null()};
groupby_.reset(new groupby::groupby(
table->view(), null_handling, keys_are_sorted, column_order, null_precedence));
}
//
// Private API
//
Napi::Value GroupBy::get_groups(Napi::CallbackInfo const& info) {
auto values = info[0];
auto env = info.Env();
CallbackArgs args{info};
rmm::mr::device_memory_resource* mr = args[1];
cudf::table_view table{cudf::table{}};
if (Table::IsInstance(values)) { table = *Table::Unwrap(values.ToObject()); }
auto groups = groupby_->get_groups(table, mr);
auto result = Napi::Object::New(env);
result.Set("keys", Table::New(env, std::move(groups.keys)));
auto const& offsets = groups.offsets;
result.Set("offsets", CPPToNapi(info)(std::make_tuple(offsets.data(), offsets.size())));
if (groups.values != nullptr) { //
result.Set("values", Table::New(env, std::move(groups.values)));
}
return result;
}
Napi::Value GroupBy::argmax(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_argmax_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::argmin(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_argmin_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::count(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_count_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::max(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_max_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::mean(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_mean_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::median(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_median_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::min(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_min_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::nth(Napi::CallbackInfo const& info) {
CallbackArgs args{info};
auto [values, mr] = _get_basic_args(info);
cudf::size_type n = args[2];
auto include_nulls =
info[3].ToBoolean() ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(n, include_nulls);
});
}
Napi::Value GroupBy::nunique(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
auto include_nulls =
info[3].ToBoolean() ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_nunique_aggregation<cudf::groupby_aggregation>(include_nulls);
});
}
Napi::Value GroupBy::std(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
cudf::size_type ddof = info[3].IsNumber() ? info[3].ToNumber() : 1;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_std_aggregation<cudf::groupby_aggregation>(ddof);
});
}
Napi::Value GroupBy::sum(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
return _single_aggregation(
info, values, mr, []() { return cudf::make_sum_aggregation<cudf::groupby_aggregation>(); });
}
Napi::Value GroupBy::var(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
cudf::size_type ddof = info[3].IsNumber() ? info[3].ToNumber() : 1;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_variance_aggregation<cudf::groupby_aggregation>(ddof);
});
}
Napi::Value GroupBy::quantile(Napi::CallbackInfo const& info) {
CallbackArgs args{info};
auto [values, mr] = _get_basic_args(info);
std::vector<double> quantiles{args[2]};
cudf::interpolation interp = args[3];
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_quantile_aggregation<cudf::groupby_aggregation>(quantiles, interp);
});
}
Napi::Value GroupBy::collect_list(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
auto include_nulls =
info[2].ToBoolean() ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_collect_list_aggregation<cudf::groupby_aggregation>(include_nulls);
});
}
Napi::Value GroupBy::collect_set(Napi::CallbackInfo const& info) {
auto [values, mr] = _get_basic_args(info);
auto include_nulls =
info[2].ToBoolean() ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
auto nulls_equal =
info[3].ToBoolean() ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
auto nans_equal =
info[4].ToBoolean() ? cudf::nan_equality::UNEQUAL : cudf::nan_equality::ALL_EQUAL;
return _single_aggregation(info, values, mr, [&]() {
return cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
include_nulls, nulls_equal, nans_equal);
});
}
std::pair<Table::wrapper_t, rmm::mr::device_memory_resource*> GroupBy::_get_basic_args(
Napi::CallbackInfo const& info) {
CallbackArgs args{info};
auto values = args[0];
NODE_CUDA_EXPECT(Table::IsInstance(values), "aggregation expects to have a 'values' table");
return std::make_pair(values.ToObject(), args[1]);
}
template <typename MakeAggregation>
Napi::Value GroupBy::_single_aggregation(Napi::CallbackInfo const& info,
Table::wrapper_t const& values_table,
rmm::mr::device_memory_resource* const mr,
MakeAggregation const& make_aggregation) {
auto env = info.Env();
std::vector<cudf::groupby::aggregation_request> requests;
requests.reserve(values_table->num_columns());
for (cudf::size_type i = 0; i < values_table->num_columns(); ++i) {
auto request = cudf::groupby::aggregation_request();
request.values = values_table->get_column(i).view();
request.aggregations.push_back(std::move(make_aggregation()));
requests.push_back(std::move(request));
}
std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::groupby::aggregation_result>> result;
try {
result = groupby_->aggregate(requests, mr);
} catch (std::exception const& e) { throw Napi::Error::New(Env(), e.what()); }
auto result_keys = Table::New(env, std::move(result.first));
auto result_cols = Napi::Array::New(env, result.second.size());
for (size_t i = 0; i < result.second.size(); ++i) {
result_cols.Set(i, Column::New(env, std::move(result.second[i].results[0]))->Value());
}
auto obj = Napi::Object::New(env);
obj.Set("keys", result_keys);
obj.Set("cols", result_cols);
return obj;
}
} // namespace nv
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/scope.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {DeviceBuffer} from '@rapidsai/rmm';
import {Column} from './column';
import {DataFrame} from './data_frame';
import {Series} from './series';
import {Table} from './table';
type Resource = DataFrame|Series|Column|Table|DeviceBuffer;
export class Disposer {
private currentScopeId = -1;
private trackedResources: Resource[][] = [];
private ingoredResources: Resource[][] = [];
add(value: Resource) {
const {currentScopeId} = this;
if (currentScopeId > -1) { this.trackedResources[currentScopeId].push(value); }
}
enter(doNotDispose: Resource[]) {
this.ingoredResources.push(doNotDispose);
this.currentScopeId = this.trackedResources.push([]) - 1;
return this;
}
exit(result: any) {
if (this.currentScopeId > -1) {
this.currentScopeId -= 1;
const flatten = (xs: Resource[]) => flattenDeviceBuffers(xs);
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
const test = new Set(flatten(this.trackedResources.pop()!));
const keep = new Set(flatten([
result,
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
...this.ingoredResources.pop()!,
...this.ingoredResources.flat(1),
...this.trackedResources.flat(1),
]));
for (const buf of test) {
if (!keep.has(buf)) { buf.dispose(); }
}
}
return result;
}
}
export const DISPOSER = new Disposer();
export function scope<T extends Resource, F extends(() => T | Promise<T>)>(
cb: F, doNotDispose: Resource[] = []) {
DISPOSER.enter(doNotDispose);
const result = cb();
if (result instanceof Promise) { //
return result.then((x) => DISPOSER.exit(x)) as ReturnType<F>;
}
return DISPOSER.exit(result) as ReturnType<F>;
}
function flattenDeviceBuffers(input: Resource|null|undefined, visited = new Set): DeviceBuffer[] {
if (!input) { return []; }
if (input instanceof DeviceBuffer) { return [input]; }
if (input instanceof Series) { return flattenDeviceBuffers(input._col, visited); }
if (input instanceof DataFrame) { return flattenDeviceBuffers(input.asTable(), visited); }
if (Array.isArray(input)) { return input.flatMap((x) => flattenDeviceBuffers(x, visited)); }
if (input instanceof Column) {
let bufs = [input.data, input.mask];
for (let i = -1; ++i < input.numChildren;) {
bufs = bufs.concat(flattenDeviceBuffers(input.getChild(i), visited));
}
return bufs;
}
if (input instanceof Table) {
let bufs: DeviceBuffer[] = [];
for (let i = -1; ++i < input.numColumns;) {
bufs = bufs.concat(flattenDeviceBuffers(input.getColumnByIndex(i), visited));
}
return bufs;
}
if (typeof input === 'object' && !visited.has(input)) {
return flattenDeviceBuffers(Object.values(input), visited);
}
return [];
}
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/column.cpp
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <node_cudf/column.hpp>
#include <node_cudf/utilities/buffer.hpp>
#include <node_cudf/utilities/cpp_to_napi.hpp>
#include <node_cudf/utilities/dtypes.hpp>
#include <cudf/detail/null_mask.hpp>
#include <cudf/detail/nvtx/ranges.hpp>
namespace nv {
Napi::Function Column::Init(Napi::Env const& env, Napi::Object exports) {
return DefineClass(env,
"Column",
{
InstanceAccessor<&Column::type, &Column::type>("type"),
InstanceAccessor<&Column::data>("data"),
InstanceAccessor<&Column::null_mask>("mask"),
InstanceAccessor<&Column::disposed>("disposed"),
InstanceAccessor<&Column::offset>("offset"),
InstanceAccessor<&Column::size>("length"),
InstanceAccessor<&Column::has_nulls>("hasNulls"),
InstanceAccessor<&Column::null_count>("nullCount"),
InstanceAccessor<&Column::is_nullable>("nullable"),
InstanceAccessor<&Column::num_children>("numChildren"),
InstanceMethod<&Column::dispose>("dispose"),
InstanceMethod<&Column::get_child>("getChild"),
InstanceMethod<&Column::get_value>("getValue"),
InstanceMethod<&Column::set_null_mask>("setNullMask"),
InstanceMethod<&Column::set_null_count>("setNullCount"),
// column/copying.cpp
InstanceMethod<&Column::gather>("gather"),
InstanceMethod<&Column::apply_boolean_mask>("applyBooleanMask"),
InstanceMethod<&Column::copy>("copy"),
// column/filling.cpp
InstanceMethod<&Column::fill>("fill"),
InstanceMethod<&Column::fill_in_place>("fillInPlace"),
// column/binaryop.cpp
InstanceMethod<&Column::add>("add"),
InstanceMethod<&Column::sub>("sub"),
InstanceMethod<&Column::mul>("mul"),
InstanceMethod<&Column::div>("div"),
InstanceMethod<&Column::true_div>("trueDiv"),
InstanceMethod<&Column::floor_div>("floorDiv"),
InstanceMethod<&Column::mod>("mod"),
InstanceMethod<&Column::pow>("pow"),
InstanceMethod<&Column::eq>("eq"),
InstanceMethod<&Column::ne>("ne"),
InstanceMethod<&Column::lt>("lt"),
InstanceMethod<&Column::gt>("gt"),
InstanceMethod<&Column::le>("le"),
InstanceMethod<&Column::ge>("ge"),
InstanceMethod<&Column::bitwise_and>("bitwiseAnd"),
InstanceMethod<&Column::bitwise_or>("bitwiseOr"),
InstanceMethod<&Column::bitwise_xor>("bitwiseXor"),
InstanceMethod<&Column::logical_and>("logicalAnd"),
InstanceMethod<&Column::logical_or>("logicalOr"),
InstanceMethod<&Column::shift_left>("shiftLeft"),
InstanceMethod<&Column::shift_right>("shiftRight"),
InstanceMethod<&Column::shift_right_unsigned>("shiftRightUnsigned"),
InstanceMethod<&Column::log_base>("logBase"),
InstanceMethod<&Column::atan2>("atan2"),
InstanceMethod<&Column::null_equals>("nullEquals"),
InstanceMethod<&Column::null_max>("nullMax"),
InstanceMethod<&Column::null_min>("nullMin"),
// column/concatenate.cpp
InstanceMethod<&Column::concat>("concat"),
// column/stream_compaction.cpp
InstanceMethod<&Column::drop_nulls>("dropNulls"),
InstanceMethod<&Column::drop_nans>("dropNans"),
// column/filling.cpp
StaticMethod<&Column::sequence>("sequence"),
// column/transform.cpp
InstanceMethod<&Column::bools_to_mask>("boolsToMask"),
InstanceMethod<&Column::nans_to_nulls>("nansToNulls"),
// column/reduction.cpp
InstanceMethod<&Column::min>("min"),
InstanceMethod<&Column::max>("max"),
InstanceMethod<&Column::minmax>("minmax"),
InstanceMethod<&Column::sum>("sum"),
InstanceMethod<&Column::product>("product"),
InstanceMethod<&Column::any>("any"),
InstanceMethod<&Column::all>("all"),
InstanceMethod<&Column::sum_of_squares>("sumOfSquares"),
InstanceMethod<&Column::mean>("mean"),
InstanceMethod<&Column::median>("median"),
InstanceMethod<&Column::nunique>("nunique"),
InstanceMethod<&Column::variance>("var"),
InstanceMethod<&Column::std>("std"),
InstanceMethod<&Column::quantile>("quantile"),
InstanceMethod<&Column::cumulative_max>("cumulativeMax"),
InstanceMethod<&Column::cumulative_min>("cumulativeMin"),
InstanceMethod<&Column::cumulative_product>("cumulativeProduct"),
InstanceMethod<&Column::cumulative_sum>("cumulativeSum"),
// column/replacement.cpp
InstanceMethod<&Column::replace_nulls>("replaceNulls"),
InstanceMethod<&Column::replace_nans>("replaceNaNs"),
// column/unaryop.cpp
InstanceMethod<&Column::cast>("cast"),
InstanceMethod<&Column::is_null>("isNull"),
InstanceMethod<&Column::is_valid>("isValid"),
InstanceMethod<&Column::is_nan>("isNaN"),
InstanceMethod<&Column::is_not_nan>("isNotNaN"),
InstanceMethod<&Column::sin>("sin"),
InstanceMethod<&Column::cos>("cos"),
InstanceMethod<&Column::tan>("tan"),
InstanceMethod<&Column::arcsin>("asin"),
InstanceMethod<&Column::arccos>("acos"),
InstanceMethod<&Column::arctan>("atan"),
InstanceMethod<&Column::sinh>("sinh"),
InstanceMethod<&Column::cosh>("cosh"),
InstanceMethod<&Column::tanh>("tanh"),
InstanceMethod<&Column::arcsinh>("asinh"),
InstanceMethod<&Column::arccosh>("acosh"),
InstanceMethod<&Column::arctanh>("atanh"),
InstanceMethod<&Column::exp>("exp"),
InstanceMethod<&Column::log>("log"),
InstanceMethod<&Column::sqrt>("sqrt"),
InstanceMethod<&Column::cbrt>("cbrt"),
InstanceMethod<&Column::ceil>("ceil"),
InstanceMethod<&Column::floor>("floor"),
InstanceMethod<&Column::abs>("abs"),
InstanceMethod<&Column::rint>("rint"),
InstanceMethod<&Column::bit_invert>("bitInvert"),
InstanceMethod<&Column::unary_not>("not"),
// column/strings/attributes.cpp
InstanceMethod<&Column::count_bytes>("countBytes"),
InstanceMethod<&Column::count_characters>("countCharacters"),
// column/strings/combine.cpp
StaticMethod<&Column::concatenate>("concatenate"),
// column/strings/contains.cpp
InstanceMethod<&Column::contains_re>("containsRe"),
InstanceMethod<&Column::count_re>("countRe"),
InstanceMethod<&Column::matches_re>("matchesRe"),
// column/strings/json.cpp
InstanceMethod<&Column::get_json_object>("getJSONObject"),
// io/text/multibyte_split.cpp
StaticMethod<&Column::read_text>("readText"),
InstanceMethod<&Column::split>("split"),
// column/strings/padding.cpp
InstanceMethod<&Column::pad>("pad"),
InstanceMethod<&Column::zfill>("zfill"),
// column/strings/replace_re.cpp
InstanceMethod<&Column::string_partition>("partitionStrings"),
// column/strings/replace_re.cpp
InstanceMethod<&Column::replace_re>("replaceRe"),
// column/strings/replace.cpp
InstanceMethod<&Column::replace_slice>("replaceSlice"),
// column/convert.cpp
InstanceMethod<&Column::strings_from_lists>("stringsFromLists"),
InstanceMethod<&Column::strings_from_booleans>("stringsFromBooleans"),
InstanceMethod<&Column::strings_to_booleans>("stringsToBooleans"),
InstanceMethod<&Column::string_is_timestamp>("stringIsTimestamp"),
InstanceMethod<&Column::strings_from_timestamps>("stringsFromTimestamps"),
InstanceMethod<&Column::strings_to_timestamps>("stringsToTimestamps"),
InstanceMethod<&Column::string_is_float>("stringIsFloat"),
InstanceMethod<&Column::strings_from_floats>("stringsFromFloats"),
InstanceMethod<&Column::strings_to_floats>("stringsToFloats"),
InstanceMethod<&Column::string_is_integer>("stringIsInteger"),
InstanceMethod<&Column::strings_from_integers>("stringsFromIntegers"),
InstanceMethod<&Column::strings_to_integers>("stringsToIntegers"),
InstanceMethod<&Column::string_is_hex>("stringIsHex"),
InstanceMethod<&Column::hex_from_integers>("hexFromIntegers"),
InstanceMethod<&Column::hex_to_integers>("hexToIntegers"),
InstanceMethod<&Column::string_is_ipv4>("stringIsIpv4"),
InstanceMethod<&Column::ipv4_from_integers>("ipv4FromIntegers"),
InstanceMethod<&Column::ipv4_to_integers>("ipv4ToIntegers"),
});
}
Column::wrapper_t Column::New(Napi::Env const& env, std::unique_ptr<cudf::column> column) {
auto props = Napi::Object::New(env);
props.Set("offset", 0);
props.Set("length", column->size());
props.Set("nullCount", column->null_count());
auto type = column->type();
auto contents = column->release();
auto data = std::move(contents.data);
auto mask = std::move(contents.null_mask);
auto children = std::move(contents.children);
props.Set("children", [&]() {
auto ary = Napi::Array::New(env, children.size());
for (size_t i = 0; i < children.size(); ++i) { //
ary.Set(i, New(env, std::move(children.at(i))));
}
return ary;
}());
props.Set("type", column_to_arrow_type(env, type, props.Get("children").As<Napi::Array>()));
props.Set("data", DeviceBuffer::New(env, std::move(data)));
props.Set("nullMask", DeviceBuffer::New(env, std::move(mask)));
return EnvLocalObjectWrap<Column>::New(env, {props});
}
Column::Column(CallbackArgs const& args) : EnvLocalObjectWrap<Column>(args) {
auto env = args.Env();
NODE_CUDF_EXPECT(args[0].IsObject(), "Column constructor expects an Object of properties", env);
NapiToCPP::Object props = args[0];
NODE_CUDF_EXPECT(props.Has("type") && props.Get("type").IsObject(),
"Column constructor properties expects a DataType Object",
env);
offset_ = props.Has("offset") ? props.Get("offset") : 0;
type_ = Napi::Persistent(props.Get("type").As<Napi::Object>());
auto mask = Column::IsInstance(props) ? props.Get("mask").As<Napi::Value>()
: props.Has("nullMask") ? props.Get("nullMask").As<Napi::Value>()
: env.Null();
auto has_length = props.Has("length") && props.Get("length").IsNumber();
switch (type().id()) {
case cudf::type_id::INT8:
case cudf::type_id::INT16:
case cudf::type_id::INT32:
case cudf::type_id::INT64:
case cudf::type_id::UINT8:
case cudf::type_id::UINT16:
case cudf::type_id::UINT32:
case cudf::type_id::UINT64:
case cudf::type_id::FLOAT64:
case cudf::type_id::FLOAT32:
case cudf::type_id::BOOL8:
case cudf::type_id::TIMESTAMP_DAYS:
case cudf::type_id::TIMESTAMP_SECONDS:
case cudf::type_id::TIMESTAMP_MILLISECONDS:
case cudf::type_id::TIMESTAMP_MICROSECONDS:
case cudf::type_id::TIMESTAMP_NANOSECONDS: {
data_ = Napi::Persistent(data_to_devicebuffer(env, props.Get("data"), type()));
size_ =
has_length
? props.Get("length")
: std::max(0, cudf::size_type(data_.Value()->size() / cudf::size_of(type())) - offset_);
null_mask_ =
Napi::Persistent(mask.IsNull() ? data_to_null_bitmask(env, props.Get("data"), size_)
: mask_to_null_bitmask(env, mask, size_));
break;
}
case cudf::type_id::LIST:
case cudf::type_id::STRING:
case cudf::type_id::DICTIONARY32: {
[&](Napi::Array const& children) {
children_.reserve(children.Length());
for (uint32_t i = 0; i < children.Length(); ++i) {
children_.push_back(Napi::Persistent(Column::wrapper_t{children.Get(i).ToObject()}));
}
}(props.Get("children").As<Napi::Array>());
data_ = Napi::Persistent(DeviceBuffer::New(env));
size_ = has_length ? props.Get("length")
: std::max(0, (num_children() > 0 ? child(0)->size() - 1 : 0) - offset_);
null_mask_ = Napi::Persistent(mask_to_null_bitmask(env, mask, size_));
break;
}
case cudf::type_id::STRUCT: {
[&](Napi::Array const& children) {
children_.reserve(children.Length());
for (uint32_t i = 0; i < children.Length(); ++i) {
children_.push_back(Napi::Persistent(Column::wrapper_t{children.Get(i).ToObject()}));
}
}(props.Get("children").As<Napi::Array>());
data_ = Napi::Persistent(DeviceBuffer::New(env));
if (num_children() > 0) {
size_ = has_length ? props.Get("length") : std::max(0, child(0)->size() - offset_);
for (cudf::size_type i = 0; ++i < num_children();) {
NODE_CUDF_EXPECT((child(i)->size() - offset_) == size_,
"Struct column children must be the same size",
env);
}
}
null_mask_ = Napi::Persistent(mask_to_null_bitmask(env, mask, size_));
break;
}
default: break;
}
// size_ = props.Has("length") ? props.Get("length") : size_;
set_null_count([&]() -> cudf::size_type {
if (!nullable()) { return 0; }
if (props.Has("nullCount")) {
auto val = props.Get("nullCount");
if (val.IsNumber()) {
return std::max(cudf::UNKNOWN_NULL_COUNT, val.ToNumber().Int32Value());
}
if (val.IsBigInt()) {
bool lossless{false};
return std::max(cudf::UNKNOWN_NULL_COUNT,
static_cast<cudf::size_type>(val.As<Napi::BigInt>().Int64Value(&lossless)));
}
}
return cudf::UNKNOWN_NULL_COUNT;
}());
}
void Column::Finalize(Napi::Env env) {
//
}
void Column::dispose(Napi::Env env) {
if (children_.size()) {
for (auto& child : children_) { child.Value()->dispose(env); }
children_.clear();
}
if (!data_.IsEmpty() && offset_ == 0) { data_.Value()->dispose(env); }
if (!null_mask_.IsEmpty() && offset_ == 0) { null_mask_.Value()->dispose(env); }
data_.Reset();
null_mask_.Reset();
size_ = 0;
offset_ = 0;
null_count_ = 0;
disposed_ = true;
}
void Column::dispose(Napi::CallbackInfo const& info) { dispose(info.Env()); }
Napi::Value Column::disposed(Napi::CallbackInfo const& info) {
auto disposed = disposed_;
if (!disposed) {
if (!data_.IsEmpty() && !data_.Value()->is_empty()) {
disposed = false;
} else if (!null_mask_.IsEmpty() && !null_mask_.Value()->is_empty()) {
disposed = false;
} else {
disposed = children_.size() == 0 ||
std::all_of(children_.begin(), children_.end(), [&](auto const& child) {
return child.Value()->disposed(info);
});
}
if (disposed) { dispose(info.Env()); }
}
return Napi::Value::From(info.Env(), disposed_);
}
// If the null count is known, return it. Else, compute and return it
cudf::size_type Column::null_count() const {
CUDF_FUNC_RANGE();
if (!nullable()) {
null_count_ = 0;
} else if (null_count_ <= cudf::UNKNOWN_NULL_COUNT) {
try {
null_count_ =
cudf::detail::count_unset_bits(*null_mask(), 0, size_, rmm::cuda_stream_default);
} catch (std::exception const& e) {
null_count_ = cudf::UNKNOWN_NULL_COUNT;
NAPI_THROW(Napi::Error::New(Env(), e.what()));
}
}
return null_count_;
}
void Column::set_null_mask(Napi::Value const& new_null_mask, cudf::size_type new_null_count) {
if (new_null_mask.IsNull() or new_null_mask.IsUndefined() or !new_null_mask.IsObject()) {
null_mask_ = Napi::Persistent(DeviceBuffer::New(new_null_mask.Env()));
set_null_count(new_null_count);
} else {
DeviceBuffer::wrapper_t new_mask = new_null_mask.ToObject();
if (new_null_count > 0) {
NODE_CUDF_EXPECT(new_mask->size() >= cudf::bitmask_allocation_size_bytes(size_),
"Column with null values must be nullable, and the null mask "
"buffer size should match the size of the column.",
Env());
}
null_mask_ = Napi::Persistent(new_mask);
set_null_count(new_null_count);
}
}
void Column::set_null_count(cudf::size_type new_null_count) {
NODE_CUDF_EXPECT(nullable() || new_null_count <= 0, "Invalid null count.", Env());
null_count_ = std::max(std::min(size_, new_null_count), cudf::UNKNOWN_NULL_COUNT);
}
cudf::column_view Column::view() const {
auto const type = this->type();
auto const data = this->data();
auto const mask = this->null_mask();
// Create views of children
std::vector<cudf::column_view> child_views;
child_views.reserve(children_.size());
for (auto i = 0u; i < children_.size(); ++i) {
child_views.emplace_back(children_[i].Value()->view());
}
return cudf::column_view(type, size_, *data, *mask, null_count_, offset_, child_views);
}
cudf::mutable_column_view Column::mutable_view() {
auto type = this->type();
auto data = this->data();
auto mask = this->null_mask();
// Create views of children
std::vector<cudf::mutable_column_view> child_views;
child_views.reserve(children_.size());
for (auto i = 0u; i < children_.size(); ++i) {
child_views.emplace_back(children_[i].Value()->mutable_view());
}
// Store the old null count before resetting it. By accessing the value directly instead of
// calling `null_count()`, we can avoid a potential invocation of `count_unset_bits()`. This does
// however mean that calling `null_count()` on the resulting mutable view could still potentially
// invoke `count_unset_bits()`.
auto current_null_count = null_count_;
// The elements of a column could be changed through a `mutable_column_view`, therefore the
// existing `null_count` is no longer valid. Reset it to `UNKNOWN_NULL_COUNT` forcing it to be
// recomputed on the next invocation of `null_count()`.
set_null_count(cudf::UNKNOWN_NULL_COUNT);
return cudf::mutable_column_view{
type, size_, *data, *mask, current_null_count, offset_, child_views};
}
Column::wrapper_t Column::operator[](Column const& selection) const {
if (selection.type().id() == cudf::type_id::BOOL8) { //
return this->apply_boolean_mask(selection);
}
return this->gather(selection);
}
Napi::Value Column::type(Napi::CallbackInfo const& info) { return type_.Value(); }
void Column::type(Napi::CallbackInfo const& info, Napi::Value const& value) {
type_ = Napi::Persistent(value.As<Napi::Object>());
}
Napi::Value Column::size(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), size_);
}
Napi::Value Column::offset(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), offset_);
}
Napi::Value Column::data(Napi::CallbackInfo const& info) { return data_.Value(); }
Napi::Value Column::has_nulls(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), null_count() > 0);
}
Napi::Value Column::null_count(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), null_count());
}
Napi::Value Column::is_nullable(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), nullable());
}
Napi::Value Column::num_children(Napi::CallbackInfo const& info) {
return Napi::Value::From(info.Env(), num_children());
}
Napi::Value Column::null_mask(Napi::CallbackInfo const& info) { return null_mask_.Value(); }
void Column::set_null_mask(Napi::CallbackInfo const& info) {
auto env = info.Env();
auto mask = mask_to_null_bitmask(env, info[0], size_);
cudf::size_type null_count{info[0].IsNull() || info[0].IsUndefined() ? 0
: cudf::UNKNOWN_NULL_COUNT};
switch (info.Length()) {
case 0: break;
case 1: break;
default:
if (info[1].IsNumber()) {
null_count = info[1].ToNumber().Int32Value();
} else if (info[1].IsBigInt()) {
bool lossless{false};
null_count = info[1].As<Napi::BigInt>().Int64Value(&lossless);
}
break;
}
set_null_mask(mask, null_count);
}
void Column::set_null_count(Napi::CallbackInfo const& info) {
this->set_null_count(info[0].ToNumber());
}
Napi::Value Column::gather(Napi::CallbackInfo const& info) {
if (!Column::IsInstance(info[0])) {
throw Napi::Error::New(info.Env(), "gather selection argument expects a Column");
}
CallbackArgs args{info};
Column::wrapper_t selection = args[0].ToObject();
auto oob_policy = args[1].ToBoolean() ? cudf::out_of_bounds_policy::NULLIFY
: cudf::out_of_bounds_policy::DONT_CHECK;
rmm::mr::device_memory_resource* mr = args[2];
return this->gather(selection, oob_policy, mr);
}
Napi::Value Column::apply_boolean_mask(Napi::CallbackInfo const& info) {
if (!Column::IsInstance(info[0])) {
throw Napi::Error::New(info.Env(), "apply_boolean_mask selection argument expects a Column");
}
CallbackArgs args{info};
Column::wrapper_t selection = args[0].ToObject();
rmm::mr::device_memory_resource* mr = args[1];
return this->apply_boolean_mask(selection, mr);
}
Napi::Value Column::get_child(Napi::CallbackInfo const& info) {
return children_[info[0].ToNumber().Int32Value()].Value();
}
Napi::Value Column::get_value(Napi::CallbackInfo const& info) {
try {
return Napi::Value::From(info.Env(), cudf::get_element(*this, info[0].ToNumber()));
} catch (std::exception const& e) { throw Napi::Error::New(Env(), e.what()); }
}
} // namespace nv
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/scalar.cpp
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <node_cudf/scalar.hpp>
#include <node_cudf/utilities/cpp_to_napi.hpp>
#include <node_cudf/utilities/dtypes.hpp>
#include <node_cudf/utilities/napi_to_cpp.hpp>
#include <node_cudf/utilities/value_to_scalar.hpp>
#include <node_cuda/utilities/error.hpp>
#include <node_cuda/utilities/napi_to_cpp.hpp>
#include <node_rmm/utilities/napi_to_cpp.hpp>
#include <nv_node/utilities/cpp_to_napi.hpp>
#include <cudf/column/column_view.hpp>
#include <cudf/scalar/scalar_factories.hpp>
#include <cudf/utilities/traits.hpp>
#include <cudf/utilities/type_dispatcher.hpp>
#include <napi.h>
#include <type_traits>
namespace nv {
Napi::Function Scalar::Init(Napi::Env const& env, Napi::Object exports) {
return DefineClass(
env,
"Scalar",
{
InstanceAccessor("type", &Scalar::type, nullptr, napi_enumerable),
InstanceAccessor("value", &Scalar::get_value, &Scalar::set_value, napi_enumerable),
});
}
Scalar::wrapper_t Scalar::New(Napi::Env const& env, std::unique_ptr<cudf::scalar> scalar) {
auto opts = Napi::Object::New(env);
opts.Set("type", cudf_to_arrow_type(env, scalar->type()));
auto wrap = Napi::External<std::unique_ptr<cudf::scalar>>::New(env, &scalar);
return EnvLocalObjectWrap<Scalar>::New(env, {opts, wrap});
}
Scalar::wrapper_t Scalar::New(Napi::Env const& env, Napi::Number const& value) {
return New(env, value, cudf::data_type{cudf::type_id::FLOAT64});
}
Scalar::wrapper_t Scalar::New(Napi::Env const& env, Napi::BigInt const& value) {
return New(env, value, cudf::data_type{cudf::type_id::INT64});
}
Scalar::wrapper_t Scalar::New(Napi::Env const& env, Napi::String const& value) {
return New(env, value, cudf::data_type{cudf::type_id::STRING});
}
Scalar::wrapper_t Scalar::New(Napi::Env const& env,
Napi::Value const& value,
cudf::data_type type) {
auto opts = Napi::Object::New(env);
opts.Set("value", value);
opts.Set("type", cudf_to_arrow_type(env, type));
return EnvLocalObjectWrap<Scalar>::New(env, {opts});
}
Scalar::Scalar(CallbackArgs const& args) : EnvLocalObjectWrap<Scalar>(args) {
auto env = args.Env();
NODE_CUDF_EXPECT(args.Length() > 0 && args[0].IsObject(),
"Scalar constructor expects an Object options argument",
env);
NapiToCPP::Object props = args[0];
NODE_CUDF_EXPECT(
props.Has("type"), "Scalar constructor expects options to have a 'type' field", env);
NODE_CUDF_EXPECT(
props.Get("type").IsObject(), "Scalar constructor expects 'type' option to be a DataType", env);
type_ = Napi::Persistent<Napi::Object>(props.Get("type"));
if (args.Length() == 1) {
*this = cudf::make_default_constructed_scalar(this->type());
if (props.Has("value")) { set_value(args, props.Get("value")); }
} else if (args.Length() == 2 && args[1].IsExternal()) {
*this = std::move(*args[1].As<Napi::External<std::unique_ptr<cudf::scalar>>>().Data());
}
}
Scalar::operator cudf::scalar&() const { return *scalar_; }
Napi::Value Scalar::type(Napi::CallbackInfo const& info) { return type_.Value(); }
Napi::Value Scalar::get_value() const { return Napi::Value::From(Env(), scalar_); }
Napi::Value Scalar::get_value(Napi::CallbackInfo const& info) { return this->get_value(); }
void Scalar::set_value(Napi::CallbackInfo const& info, Napi::Value const& value) {
if (value.IsNull() or value.IsUndefined()) {
this->set_valid_async(false);
} else {
cudf::type_dispatcher(this->type(), detail::set_scalar_value{value}, scalar_);
}
}
} // namespace nv
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/column.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {MemoryData} from '@rapidsai/cuda';
import {DeviceBuffer, MemoryResource} from '@rapidsai/rmm';
import * as CUDF from './addon';
import {Scalar} from './scalar';
import {Table} from './table';
import {
Bool8,
DataType,
Float32,
Float64,
IndexType,
Int32,
Int64,
Integral,
Numeric,
Timestamp,
Utf8String
} from './types/dtypes';
import {GetJSONObjectOptions} from './types/json';
import {CommonType, Interpolation} from './types/mappings';
export type PadSideType = 'left'|'right'|'both';
export type ColumnProps<T extends DataType = any> = {
/*
* ColumnProps *with* a `nullMask` shouldn't allow `data` to be an Array with elements and
* nulls:
* ```javascript
* new Column({
* type: new Int32,
* data: [1, 0, 2, 3, 0], ///< must not include nulls
* nullMask: [true, false, true, true, false]
* })
* ```
*/
type: T;
data?: DeviceBuffer | MemoryData | T['scalarType'][] | null;
offset?: number;
length?: number;
nullCount?: number;
nullMask?: DeviceBuffer | MemoryData | any[] | boolean | null;
children?: ReadonlyArray<Column>| null;
}|{
/*
* ColumnProps *without* a `nullMask` should allow `data` to be an Array with elements and
* nulls:
* ```javascript
* new Column({
* type: new Int32,
* data: [1, null, 2, 3, null] ///< can include nulls
* })
* ```
*/
type: T;
data?: DeviceBuffer|MemoryData|(T['scalarType'] | null | undefined)[]|null;
offset?: number;
length?: number;
nullCount?: number;
nullMask?: never;
children?: ReadonlyArray<Column>|null;
};
export interface ColumnConstructor {
readonly prototype: Column;
new<T extends DataType = any>(props: ColumnProps<T>): Column<T>;
/**
* Row-wise concatenates the given list of strings columns and returns a single strings column
* result.
*
* @param columns List of string columns to concatenate.
* @param separator String that should inserted between each string from each row.
* @param nullRepr String that should be used in place of any null strings found in any column.
* Null value means any null entry in any column will produces a null result for that row.
* @param separate_nulls If true, then the separator is included for null rows if nullRepr is
* valid.
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory
* @returns New column with concatenated results.
*/
concatenate(columns: Table,
separator: string,
nullRepr: string|null,
separate_nulls: boolean,
memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Fills a column with a sequence of values specified by an initial value and a step of 1.
*
* @param size Size of the output column
* @param init First value in the sequence
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory
* @returns column with the sequence
*/
sequence<U extends DataType>(size: number, init: Scalar<U>, memoryResource?: MemoryResource):
Column<U>;
/**
* Fills a column with a sequence of values specified by an initial value and a step.
*
* @param size Size of the output column
* @param init First value in the sequence
* @param step Increment value
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory
* @returns column with the sequence
*/
sequence<U extends DataType>(size: number,
init: Scalar<U>,
step: Scalar<U>,
memoryResource?: MemoryResource): Column<U>;
/**
* Fills a column with the Utf-8 string located at filepath. If a delimiter is included then
* the input string will be split into a sequence of strings. The delimiter will remain
* at the end of each string in the column, except for the last. If no delimiter is included,
* the input string will be read into a single string at element 0 of the Colum.
*
* @param filepath The location of the input file.
* @param delimiter Optional delimiter.
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory
* @returns column containing one or more strings.
*
* @note The maximum size of a string read with this method is 2^30
*/
readText(filepath: string, delimiter: string, memoryResource?: MemoryResource):
Column<Utf8String>;
}
/**
* A low-level wrapper for libcudf Column Objects
*/
export interface Column<T extends DataType = any> {
readonly type: T;
readonly data: DeviceBuffer;
readonly mask: DeviceBuffer;
readonly disposed: boolean;
readonly offset: number;
readonly length: number;
readonly nullable: boolean;
readonly hasNulls: boolean;
readonly nullCount: number;
readonly numChildren: number;
/**
* @summary Explicitly free the device memory associated with this Column and all child Columns.
*/
dispose(): void;
/**
* @summary Return sub-selection from a Column.
*
* @description Gathers the rows of the source columns according to `selection`, such that row "i"
* in the resulting Column's columns will contain row `selection[i]` from the source columns. The
* number of rows in the result column will be equal to the number of elements in selection. A
* negative value i in the selection is interpreted as i+n, where `n` is the number of rows in
* the source column.
*
* For dictionary columns, the keys column component is copied and not trimmed if the gather
* results in abandoned key elements.
*
* @param selection A Series of 8/16/32-bit signed or unsigned integer indices to gather.
* @param nullify_out_of_bounds If `true`, coerce rows that corresponds to out-of-bounds indices
* in the selection to null. If `false`, skips all bounds checking for selection values. Pass
* false if you are certain that the selection contains only valid indices for better
* performance. If `false` and there are out-of-bounds indices in the selection, the behavior
* is undefined. Defaults to `false`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
gather(selection: Column<IndexType>,
nullify_out_of_bounds: boolean,
memoryResource?: MemoryResource): Column<T>;
/**
* Return sub-selection from a Column
*
* @param selection A Series of bools.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
applyBooleanMask(selection: Column<Bool8>, memoryResource?: MemoryResource): Column<T>;
/**
* Return a copy of a Column
*
*/
copy(memoryResource?: MemoryResource): Column<T>;
/**
* Return a child at the specified index to host memory
*
* @param index
*/
getChild<R extends DataType>(index: number): Column<R>;
/**
* Return a value at the specified index to host memory
*
* @param index
*/
getValue(index: number): T['scalarType']|null;
// setValue(index: number, value?: T['scalarType'] | null): void;
/**
* Set the null count for the null mask
*
* @param nullCount
*/
setNullCount(nullCount: number): void;
/**
*
* @param mask The null-mask. Valid values are marked as 1; otherwise 0. The
* mask bit given the data index idx is computed as:
* ```
* (mask[idx // 8] >> (idx % 8)) & 1
* ```
* @param nullCount The number of null values. If None, it is calculated
* automatically.
*/
setNullMask(mask: MemoryData|ArrayLike<number>|ArrayLike<bigint>, nullCount?: number): void;
/**
* Fills a range of elements in a column out-of-place with a scalar value.
*
* @param begin The starting index of the fill range (inclusive).
* @param end The index of the last element in the fill range (exclusive).
* @param value The scalar value to fill.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*/
fill(value: Scalar<T>, begin?: number, end?: number, memoryResource?: MemoryResource): Column<T>;
/**
* Fills a range of elements in-place in a column with a scalar value.
*
* @param begin The starting index of the fill range (inclusive)
* @param end The index of the last element in the fill range (exclusive)
* @param value The scalar value to fill
*/
fillInPlace(value: Scalar<T>, begin?: number, end?: number): Column<T>;
/**
* Replace null values with a `Column`, `Scalar`, or the first/last non-null value.
*
* @param value The value to use in place of nulls.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*/
replaceNulls(value: Column<T>, memoryResource?: MemoryResource): Column<T>;
replaceNulls(value: Scalar<T>, memoryResource?: MemoryResource): Column<T>;
replaceNulls(value: boolean, memoryResource?: MemoryResource): Column<T>;
/**
* Concat a Column to the end of the caller, returning a new Column.
*
* @param other The Column to concat to the end of the caller.
*/
concat(other: Column<T>, memoryResource?: MemoryResource): Column<T>;
/**
* Replace NaN values with a scalar value, or the corresponding elements from another Column.
*
* @param value The value to use in place of NaNs.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*/
replaceNaNs(value: Column<T>, memoryResource?: MemoryResource): Column<T>;
replaceNaNs(value: Scalar<T>, memoryResource?: MemoryResource): Column<T>;
/**
* Add this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to add to this Column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
add(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
add(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
add<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
add<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Subtract this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to subtract from this Column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
sub(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
sub(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
sub<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
sub<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Multiply this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to multiply this column by.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
mul(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
mul(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
mul<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
mul<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Divide this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to divide this Column by.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
div(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
div(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
div<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
div<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* True-divide this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to true-divide this Column by.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
trueDiv(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
trueDiv(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
trueDiv<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
trueDiv<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Floor-divide this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to floor-divide this Column by.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
floorDiv(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
floorDiv(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
floorDiv<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
floorDiv<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Modulo this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to mod with this Column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
mod(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
mod(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
mod<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
mod<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Power this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use as the exponent for the power operation.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
pow(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
pow(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
pow<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
pow<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform the binary '==' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
eq(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
eq(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
eq<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
eq<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform the binary '!=' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
ne(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
ne(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
ne<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
ne<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform the binary '<' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
lt(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
lt(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
lt<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
lt<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform the binary '<=' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
le(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
le(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
le<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
le<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform the binary '>' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
gt(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
gt(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
gt<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
gt<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform the binary '>=' operation between this column and another Column or scalar value.
*
* @rhs The other Column or scalar to compare with this column.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of booleans with the comparison result.
*/
ge(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
ge(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
ge<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
ge<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform a binary `&` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
bitwiseAnd(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
bitwiseAnd(rhs: number, memoryResource?: MemoryResource): Column<T>;
bitwiseAnd<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
bitwiseAnd<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `|` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
bitwiseOr(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
bitwiseOr(rhs: number, memoryResource?: MemoryResource): Column<T>;
bitwiseOr<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
bitwiseOr<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `^` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
bitwiseXor(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
bitwiseXor(rhs: number, memoryResource?: MemoryResource): Column<T>;
bitwiseXor<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
bitwiseXor<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `&&` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
logicalAnd(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
logicalAnd(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
logicalAnd<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
logicalAnd<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `||` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
logicalOr(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
logicalOr(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
logicalOr<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
logicalOr<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `<<` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
shiftLeft(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
shiftLeft(rhs: number, memoryResource?: MemoryResource): Column<T>;
shiftLeft<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
shiftLeft<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `>>` operation between this Column and another Column or scalar
* value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
shiftRight(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
shiftRight(rhs: number, memoryResource?: MemoryResource): Column<T>;
shiftRight<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
shiftRight<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `shiftRightUnsigned` operation between this Column and another Column or
* scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
shiftRightUnsigned(rhs: bigint, memoryResource?: MemoryResource): Column<T>;
shiftRightUnsigned(rhs: number, memoryResource?: MemoryResource): Column<T>;
shiftRightUnsigned<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
shiftRightUnsigned<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `logBase` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
logBase(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
logBase(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
logBase<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
logBase<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `atan2` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
atan2(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
atan2(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
atan2<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
atan2<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `nullEquals` operation between this Column and another Column or scalar
* value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
nullEquals(rhs: bigint, memoryResource?: MemoryResource): Column<Bool8>;
nullEquals(rhs: number, memoryResource?: MemoryResource): Column<Bool8>;
nullEquals<R extends Numeric>(rhs: Scalar<R>, memoryResource?: MemoryResource): Column<Bool8>;
nullEquals<R extends Numeric>(rhs: Column<R>, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Perform a binary `nullMax` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
nullMax(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
nullMax(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
nullMax<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
nullMax<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Perform a binary `nullMin` operation between this Column and another Column or scalar value.
*
* @param rhs The other Column or scalar to use.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of a common numeric type with the results of the binary operation.
*/
nullMin(rhs: bigint, memoryResource?: MemoryResource): Column<Int64>;
nullMin(rhs: number, memoryResource?: MemoryResource): Column<Float64>;
nullMin<R extends Numeric>(rhs: Scalar<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
nullMin<R extends Numeric>(rhs: Column<R>,
memoryResource?: MemoryResource): Column<CommonType<T, R>>;
/**
* Casts data from dtype specified in input to dtype specified in output.
*
* @note Supports only fixed-width types.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns Column of same size as `input` containing result of the cast operation.
*/
cast<R extends DataType>(dataType: R, memoryResource?: MemoryResource): Column<R>;
/**
* Creates a column of `BOOL8` elements where `true` indicates the value is null and `false`
* indicates the row matches the given pattern
*
* @param pattern Regex pattern to match to each string.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing `null`
* values.
*/
containsRe(pattern: string, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Creates a column of `INT32` elements where `true` indicates the number of times the given
* regex pattern matches in each string.
*
* @param pattern Regex pattern to match to each string.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `INT32` counts
*/
countRe(pattern: string, memoryResource?: MemoryResource): Column<Int32>;
/**
* Creates a column of `BOOL8` elements where `true` indicates the value is null and `false`
* indicates the row matches the given pattern only at the beginning of the string
*
* @param pattern Regex pattern to match to each string.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing `null`
* values.
*/
matchesRe(pattern: string, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Creates a column of `BOOL8` elements where `true` indicates the value is null and `false`
* indicates the value is valid.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing `null`
* values.
*/
isNull(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Creates a column of `BOOL8` elements where `true` indicates the value is valid and `false`
* indicates the value is null.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `false` representing `null`
* values.
*/
isValid(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Creates a column of `BOOL8` elements indicating the presence of `NaN` values in a
* column of floating point values. The output element at row `i` is `true` if the element in
* `input` at row i is `NAN`, else `false`
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing `NAN`
* values
*/
isNaN(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Creates a column of `BOOL8` elements indicating the absence of `NaN` values in a
* column of floating point values. The output element at row `i` is `false` if the element in
* `input` at row i is `NAN`, else `true`
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing non-`NAN`
* values
*/
isNotNaN(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Convert a list column of strings into a formatted strings column.
*
* The `separators` column should contain 3 strings elements in the following order:
* - element separator (default is comma `,`)
* - left-hand enclosure (default is `[`)
* - right-hand enclosure (default is `]`)
*
* @param na_rep Replacement string for null elements.
* @param separators Strings to use for enclosing list components and separating elements.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with the lists as strings.
*/
stringsFromLists(na_rep: string, separators: Column<Utf8String>, memoryResource?: MemoryResource):
Column<Utf8String>;
/**
* Returns a new strings column converting the boolean values from the provided column into
* strings.
*
* Any null entries will result in corresponding null entries in the output column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with booleans as strings.
*/
stringsFromBooleans(memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a new Bool8 column parsing true/false values from the provided strings column.
*
* Any null entries will result in corresponding null entries in the output column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of boolean type with the results of the conversion.
*/
stringsToBooleans(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Returns a new timestamp column converting a strings column into timestamps using the provided
* format pattern.
*
* The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z".
*
* @param format The string specifying output format. Default format is "%Y-%m-%dT%H:%M:%SZ".
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of boolean type with the results of the conversion.
*/
stringIsTimestamp(format: string, memoryResource?: MemoryResource): Column<Bool8>;
/**
* Returns a new strings column converting a timestamp column into strings using the provided
* format pattern.
*
* The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z,%Z"
*
* @param format The string specifying output format. Default format is "%Y-%m-%dT%H:%M:%SZ".
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of string type with the results of the conversion.
*/
stringsFromTimestamps(format: string, memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a new timestamp column converting a strings column into timestamps using the provided
* format pattern.
*
* The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z".
*
* @param type The timestamp type used for creating the output column.
* @param format String specifying the timestamp format in strings.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of timestamp type with the results of the conversion.
*/
stringsToTimestamps<T extends Timestamp>(type: T,
format: string,
memoryResource?: MemoryResource): Column<T>;
/**
* Creates a column of `BOOL8` elements indicating strings in which all characters are valid for
* conversion to floats.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing convertible
* values
*/
stringIsFloat(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Returns a new strings column converting the float values from the provided column into strings.
*
* Any null entries will result in corresponding null entries in the output column.
*
* For each float, a string is created in base-10 decimal. Negative numbers will include a '-'
* prefix. Numbers producing more than 10 significant digits will produce a string that includes
* scientific notation (e.g. "-1.78e+15").
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with floats as strings.
*/
stringsFromFloats(memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a new floating point numeric column parsing float values from the provided
* strings column.
*
* Any null entries will result in corresponding null entries in the output column.
*
* Only characters [0-9] plus a prefix '-' and '+' and decimal '.' are recognized. Additionally,
* scientific notation is also supported (e.g. "-1.78e+5").
*
* @param dataType Type of floating numeric column to return.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of a the specified float type with the results of the conversion.
*/
stringsToFloats<R extends Float32|Float64>(dataType: R,
memoryResource?: MemoryResource): Column<R>;
/**
* Creates a column of `BOOL8` elements indicating strings in which all characters are valid for
* conversion to floats.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing convertible
* values
*/
stringIsInteger(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Returns a new strings column converting the integer values from the provided column into
* strings.
*
* Any null entries will result in corresponding null entries in the output column.
*
* For each integer, a string is created in base-10 decimal. Negative numbers will include a '-'
* prefix.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with integers as strings.
*/
stringsFromIntegers(memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a new integer numeric column parsing integer values from the provided strings column.
*
* Any null entries will result in corresponding null entries in the output column.
*
* Only characters [0-9] plus a prefix '-' and '+' are recognized. When any other character is
* encountered, the parsing ends for that string and the current digits are converted into an
* integer.
*
* Overflow of the resulting integer type is not checked. Each string is converted using an int64
* type and then cast to the target integer type before storing it into the output column. If the
* resulting integer type is too small to hold the value, the stored value will be undefined.
*
* @param dataType Type of integer numeric column to return.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of a the specified integral type with the results of the conversion.
*/
stringsToIntegers<R extends DataType>(dataType: R, memoryResource?: MemoryResource): Column<R>;
/**
* Returns a boolean column identifying strings in which all characters are valid for conversion
* to integers from hex.
*
* The output row entry will be set to true if the corresponding string element has at least one
* character in [0-9A-Za-z]. Also, the string may start with '0x'.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing convertible
* values
*/
stringIsHex(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Returns a new strings column converting integer columns to hexadecimal characters.
*
* Any null entries will result in corresponding null entries in the output column.
*
* The output character set is '0'-'9' and 'A'-'F'. The output string width will be a multiple of
* 2 depending on the size of the integer type. A single leading zero is applied to the first
* non-zero output byte if it less than 0x10.
*
* Leading zeros are suppressed unless filling out a complete byte as in 1234 -> 04D2 instead of
* 000004D2 or 4D2.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with integers as strings.
*/
hexFromIntegers(memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a new integer numeric column parsing hexadecimal values from the provided strings
* column.
*
* Any null entries will result in corresponding null entries in the output column.
*
* Only characters [0-9] and [A-F] are recognized. When any other character is encountered,
* the parsing ends for that string. No interpretation is made on the sign of the integer.
*
* Overflow of the resulting integer type is not checked. Each string is converted using an
* int64 type and then cast to the target integer type before storing it into the output column.
* If the resulting integer type is too small to hold the value, the stored value will be
* undefined.
*
* @param dataType Type of integer numeric column to return.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A Column of a the specified integral type with the results of the conversion.
*/
hexToIntegers<R extends DataType>(dataType: R, memoryResource?: MemoryResource): Column<R>;
/**
* Returns a boolean column identifying strings in which all characters are valid for conversion
* to integers from IPv4 format.
*
* The output row entry will be set to true if the corresponding string element has the following
* format xxx.xxx.xxx.xxx where xxx is integer digits between 0-255.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A non-nullable column of `BOOL8` elements with `true` representing convertible
* values
*/
stringIsIpv4(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Converts integers into IPv4 addresses as strings.
*
* The IPv4 format is 1-3 character digits [0-9] between 3 dots (e.g. 123.45.67.890). Each section
* can have a value between [0-255].
*
* Each input integer is dissected into four integers by dividing the input into 8-bit sections.
* These sub-integers are then converted into [0-9] characters and placed between '.' characters.
*
* No checking is done on the input integer value. Only the lower 32-bits are used.
*
* Any null entries will result in corresponding null entries in the output column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns A string Column with ipv4 addresses as strings.
*/
ipv4FromIntegers(memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Converts IPv4 addresses into integers.
*
* The IPv4 format is 1-3 character digits [0-9] between 3 dots (e.g. 123.45.67.890). Each section
* can have a value between [0-255].
*
* The four sets of digits are converted to integers and placed in 8-bit fields inside the
* resulting integer.
*
* i0.i1.i2.i3 -> (i0 << 24) | (i1 << 16) | (i2 << 8) | (i3)
*
* No checking is done on the format. If a string is not in IPv4 format, the resulting integer is
* undefined.
*
* The resulting 32-bit integer is placed in an int64_t to avoid setting the sign-bit in an
* int32_t type. This could be changed if cudf supported a UINT32 type in the future.
*
* Any null entries will result in corresponding null entries in the output column.Returns a new
* Int64 numeric column parsing hexadecimal values from the provided strings column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @returns New INT64 column converted from strings.
*/
ipv4ToIntegers(memoryResource?: MemoryResource): Column<Int64>;
/**
* Compute the trigonometric sine for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
sin(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the trigonometric cosine for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
cos(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the trigonometric tangent for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
tan(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the trigonometric sine inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
asin(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the trigonometric cosine inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
acos(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the trigonometric tangent inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
atan(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic sine for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
sinh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic cosine for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
cosh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic tangent for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
tanh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic sine inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
asinh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic cosine inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
acosh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the hyperbolic tangent inverse for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
atanh(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the exponential (base e, euler number) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
exp(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the natural logarithm (base e) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
log(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the square-root (x^0.5) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
sqrt(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the cube-root (x^(1.0/3)) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
cbrt(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the smallest integer value not less than arg for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
ceil(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the largest integer value not greater than arg for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
floor(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the absolute value for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
abs(memoryResource?: MemoryResource): Column<T>;
/**
* Round floating-point to integer for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @throws Error if the Column's DataType isn't Float32 or Float64.
* @returns A Column of the same number of elements containing the result of the operation.
*/
rint(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the bitwise not (~) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
bitInvert(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the logical not (!) for each value in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A Column of the same number of elements containing the result of the operation.
*/
not(memoryResource?: MemoryResource): Column<Bool8>;
/**
* Compute the min of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The min of all the values in this Column.
*/
min(memoryResource?: MemoryResource): T['scalarType'];
/**
* Compute the max of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The max of all the values in this Column.
*/
max(memoryResource?: MemoryResource): T['scalarType'];
/**
* Compute a pair of [min,max] of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The pair of [min,max] of all the values in this Column.
*/
minmax(memoryResource?: MemoryResource): [T['scalarType'], T['scalarType']];
/**
* Compute the sum of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The sum of all the values in this Column.
*/
sum(memoryResource?: MemoryResource): T extends Integral? bigint: number;
/**
* Compute the product of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The product of all the values in this Column.
*/
product(memoryResource?: MemoryResource): T extends Integral? bigint: number;
/**
* Compute the sumOfSquares of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The sumOfSquares of all the values in this Column.
*/
sumOfSquares(memoryResource?: MemoryResource): T extends Integral? bigint: number;
/**
* Compute the mean of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The mean of all the values in this Column.
*/
mean(memoryResource?: MemoryResource): number;
/**
* Compute the median of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The median of all the values in this Column.
*/
median(memoryResource?: MemoryResource): T['scalarType'];
/**
* Compute the nunique of all values in this Column.
*
* @param dropna The dropna parameter if true, excludes nulls while computing nunique,
* if false, includes the nulls while computing nunique.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The number of unique values in this Column.
*/
nunique(dropna?: boolean, memoryResource?: MemoryResource): number;
/**
* Return whether all elements are true in column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns true if all elements are true in column, else false.
*/
all(memoryResource?: MemoryResource): boolean;
/**
* Return whether any elements are true in column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns true if any elements are true in column, else false.
*/
any(memoryResource?: MemoryResource): boolean;
/**
* Return unbiased variance of the column.
* Normalized by N-1 by default. This can be changed using the `ddof` argument
*
* @param ddof Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
* where N represents the number of elements.
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
* @returns The median of all the values in this column.
*/
var(ddof?: number, memoryResource?: MemoryResource): number;
/**
* Return sample standard deviation of the column.
* Normalized by N-1 by default. This can be changed using the `ddof` argument
*
* @param ddof Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
* where N represents the number of elements.
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
* @returns The median of all the values in this column.
*/
std(ddof?: number, memoryResource?: MemoryResource): number;
/**
* Return values at the given quantile.
*
* @param q the quantile(s) to compute, 0 <= q <= 1
* @param interpolation This optional parameter specifies the interpolation method to use,
* when the desired quantile lies between two data points i and j.
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
* @returns values at the given quantile.
*/
quantile(q?: number, interpolation?: Interpolation, memoryResource?: MemoryResource): number;
/**
* Compute the cumulative max of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The cumulative max of all the values in this Column.
*/
cumulativeMax(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the cumulative min of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The cumulative min of all the values in this Column.
*/
cumulativeMin(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the cumulative product of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The cumulative product of all the values in this Column.
*/
cumulativeProduct(memoryResource?: MemoryResource): Column<T>;
/**
* Compute the cumulative sum of all values in this Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns The cumulative sum of all the values in this Column.
*/
cumulativeSum(memoryResource?: MemoryResource): Column<T>;
/**
* drop NA values from the column if column is of floating-type
* values and contains NA values
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
* @returns column without NaN and Null values
*/
dropNulls(memoryResource?: MemoryResource): Column<T>;
/**
* drop NA values from the column if column is of floating-type
* values and contains NA values
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
* @returns column without NaN and Null values
*/
dropNans(memoryResource?: MemoryResource): Column<T>;
/**
* Compress the data from a Bool8 Column to bits and return a Buffer
*
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
*/
boolsToMask(memoryResource?: MemoryResource): [DeviceBuffer, number];
/**
* convert NaN values in the column with Null values,
* while also updating the nullMask and nullCount values
*
* @param memoryResource The optional MemoryResource used to allocate the result column's device
* memory.
*/
nansToNulls(memoryResource?: MemoryResource): Column<T>;
getJSONObject(jsonPath?: string, opts?: GetJSONObjectOptions, memoryResource?: MemoryResource):
Column<T>;
/**
* Returns the number of bytes of each string in the Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A column of `INT32` counts
*/
countBytes(memoryResource?: MemoryResource): Column<Int32>;
/**
* Returns the number of characters of each string in the Column.
*
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns A column of `INT32` counts
*/
countCharacters(memoryResource?: MemoryResource): Column<Int32>;
/**
* Add padding to each string using a provided character.
*
* If the string is already width or more characters, no padding is performed. No strings are
* truncated.
*
* Null string entries result in null entries in the output column.
*
* @param width The minimum number of characters for each string.
* @param side Where to place the padding characters
* @param fill_char Single UTF-8 character to use for padding.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns New column with padded strings.
*/
pad(width: number, side: PadSideType, fill_char: string, memoryResource?: MemoryResource):
Column<Utf8String>;
/**
* Add '0' as padding to the left of each string.
*
* If the string is already width or more characters, no padding is performed. No strings are
* truncated.
*
* This equivalent to `pad(width, 'left', '0')` but is more optimized for this special case.
*
* Null string entries result in null entries in the output column.
*
* @param width The minimum number of characters for each string.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns New column of strings.
*/
zfill(width: number, memoryResource?: MemoryResource): Column<Utf8String>;
/**
* For each string in the column, replaces any character sequence matching the given pattern with
* the provided replacement string.
*
* Null string entries will return null output string entries.
*
* Position values are 0-based meaning position 0 is the first character of each string.
*
* This function can be used to insert a string into specific position by specifying the same
* position value for start and stop. The repl string can be appended to each string by specifying
* -1 for both start and stop.
*
* @param pattern The regular expression pattern to search within each string.
* @param replacement The string used to replace the matched sequence in each string.
* Default is an empty string.
* @param maxReplaceCount The maximum number of times to replace the matched pattern within each
* string. Default replaces every substring that is matched.
* @param flags Regex flags for interpreting special characters in the pattern.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns New strings column with matching elements replaced.
*/
replaceRe(pattern: string,
replacement: string,
maxReplaceCount: number,
flags: {dotAll: boolean, multiline: boolean},
memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Replaces each string in the column with the provided repl string within the [start,stop)
* character position range.
*
* Null string entries will return null output string entries.
*
* Position values are 0-based meaning position 0 is the first character of each string.
*
* This function can be used to insert a string into specific position by specifying the same
* position value for start and stop. The repl string can be appended to each string by specifying
* -1 for both start and stop.
*
* @param repl Replacement string for specified positions found.
* @param start Start position where repl will be added. Default is 0, first character position.
* @param stop End position (exclusive) to use for replacement. Default of -1 specifies the end of
* each string.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns New strings column
*/
replaceSlice(repl: string, start: number, stop: number, memoryResource?: MemoryResource):
Column<Utf8String>;
/**
* Splits a string column by delimiter. The delimiter string will remain at the end of each
* string in the split column. This method will completely change the string boundaries of a
* string column according to the delimiter: old boundaries will be removed and new boundaries
* will be introduced according to the delimiter. In addition, if used without a delimiter,
* the string column will be combined from n string values into a single value.
*
* @example
* ```typescript
* let a = Series.new(['abcdefg', 'bcdefgh']);
* a.split('d');
* [ 'abcd', 'efgbcd', 'efgh' ]
* a.split('');
* [ 'abcdefgbcdefgh' ]
* ```
* @param delimiter split along the delimiter.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns New strings column
*/
split(delimiter: string, memoryResource?: MemoryResource): Column<Utf8String>;
/**
* Returns a set of 3 columns by splitting each string using the specified delimiter.
*
* The number of rows in the output columns will be the same as the input column. The first column
* will contain the first tokens of each string as a result of the split. The second column will
* contain the delimiter. The third column will contain the remaining characters of each string
* after the delimiter.
*
* Any null string entries return corresponding null output columns.
*
* @param delimiter UTF-8 encoded string indicating where to split each string. Default of empty
* string indicates split on whitespace.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
* @returns 3 new string columns representing before the delimiter, the delimiter, and after the
* delimiter.
*/
partitionStrings(delimiter: string, memoryResource?: MemoryResource):
[Column<Utf8String>, Column<Utf8String>, Column<Utf8String>];
}
// eslint-disable-next-line @typescript-eslint/no-redeclare
export const Column: ColumnConstructor = CUDF.Column;
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/node_cudf.ts
|
// Copyright (c) 2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
/* eslint-disable @typescript-eslint/no-redeclare */
import {ColumnConstructor} from './column';
import {GroupByConstructor} from './groupby';
import {ScalarConstructor} from './scalar';
import {TableConstructor} from './table';
import {DataType} from './types/dtypes';
import {CommonType} from './types/mappings';
/** @ignore */
export declare const _cpp_exports: any;
export declare const Column: ColumnConstructor;
export declare const GroupBy: GroupByConstructor;
export declare const Scalar: ScalarConstructor;
export declare const Table: TableConstructor;
export declare function findCommonType<T extends DataType, R extends DataType>(
lhs: T, rhs: R): CommonType<T, R>;
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/table.cpp
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "node_cudf/table.hpp"
#include "node_cudf/column.hpp"
#include "node_cudf/utilities/error.hpp"
#include "node_cudf/utilities/napi_to_cpp.hpp"
#include <cudf/column/column.hpp>
#include <cudf/sorting.hpp>
#include <cudf/types.hpp>
#include <napi.h>
namespace nv {
//
// Public API
//
Napi::Function Table::Init(Napi::Env const& env, Napi::Object exports) {
return DefineClass(env,
"Table",
{
InstanceAccessor<&Table::num_columns>("numColumns"),
InstanceAccessor<&Table::num_rows>("numRows"),
InstanceMethod<&Table::dispose>("dispose"),
InstanceMethod<&Table::scatter_scalar>("scatterScalar"),
InstanceMethod<&Table::scatter_table>("scatterTable"),
InstanceMethod<&Table::gather>("gather"),
InstanceMethod<&Table::apply_boolean_mask>("applyBooleanMask"),
InstanceMethod<&Table::get_column>("getColumnByIndex"),
InstanceMethod<&Table::to_arrow>("toArrow"),
InstanceMethod<&Table::order_by>("orderBy"),
StaticMethod<&Table::read_csv>("readCSV"),
InstanceMethod<&Table::write_csv>("writeCSV"),
StaticMethod<&Table::read_parquet>("readParquet"),
InstanceMethod<&Table::write_parquet>("writeParquet"),
StaticMethod<&Table::read_orc>("readORC"),
InstanceMethod<&Table::write_orc>("writeORC"),
StaticMethod<&Table::from_arrow>("fromArrow"),
InstanceMethod<&Table::drop_nans>("dropNans"),
InstanceMethod<&Table::drop_nulls>("dropNulls"),
InstanceMethod<&Table::unique>("unique"),
InstanceMethod<&Table::distinct>("distinct"),
InstanceMethod<&Table::interleave_columns>("interleaveColumns"),
StaticMethod<&Table::concat>("concat"),
StaticMethod<&Table::full_join>("fullJoin"),
StaticMethod<&Table::inner_join>("innerJoin"),
StaticMethod<&Table::left_join>("leftJoin"),
StaticMethod<&Table::left_semi_join>("leftSemiJoin"),
StaticMethod<&Table::left_anti_join>("leftAntiJoin"),
InstanceMethod<&Table::explode>("explode"),
InstanceMethod<&Table::explode_position>("explodePosition"),
InstanceMethod<&Table::explode_outer>("explodeOuter"),
InstanceMethod<&Table::explode_outer_position>("explodeOuterPosition"),
});
}
Table::wrapper_t Table::New(Napi::Env const& env, Napi::Array const& columns) {
auto opts = Napi::Object::New(env);
opts.Set("columns", columns);
return EnvLocalObjectWrap<Table>::New(env, {opts});
}
Table::wrapper_t Table::New(Napi::Env const& env, std::unique_ptr<cudf::table> table) {
auto contents = table->release();
auto columns = Napi::Array::New(env, contents.size());
for (auto i = 0u; i < columns.Length(); ++i) {
columns.Set(i, Column::New(env, std::move(contents[i])));
}
return New(env, columns);
}
Table::Table(CallbackArgs const& args) : EnvLocalObjectWrap<Table>(args) {
// std::cout << "Table::Table" << std::endl;
auto env = args.Env();
NODE_CUDF_EXPECT(args.IsConstructCall(), "Table constructor requires 'new'", env);
if (args.Length() != 1 || !args[0].IsObject()) { return; }
NapiToCPP::Object props = args[0];
num_rows_ = 0;
num_columns_ = 0;
if (props.Get("columns").IsArray()) {
auto cols = props.Get("columns").As<Napi::Array>();
num_columns_ = cols.Length();
for (auto i = 0; i < num_columns_; ++i) {
columns_.push_back(Napi::Persistent(Column::wrapper_t{cols.Get(i).ToObject()}));
}
}
if (num_columns_ > 0) {
num_rows_ = get_column(0).size();
for (auto i = 1; i < num_columns_; ++i) {
NODE_CUDF_EXPECT(
get_column(i).size() == num_rows_, "All Columns must be of same length", env);
}
}
}
void Table::Finalize(Napi::Env env) {
//
}
void Table::dispose(Napi::Env env) {
if (columns_.size() > 0) {
for (auto& child : columns_) { child.Value()->dispose(env); }
columns_.clear();
}
num_rows_ = 0;
num_columns_ = 0;
disposed_ = true;
}
void Table::dispose(Napi::CallbackInfo const& info) { dispose(info.Env()); }
cudf::table_view Table::view() const {
// Create views of children
std::vector<cudf::column_view> child_views;
child_views.reserve(num_columns_);
for (auto i = 0; i < num_columns_; ++i) { child_views.push_back(columns_[i].Value()->view()); }
return cudf::table_view{child_views};
}
cudf::mutable_table_view Table::mutable_view() {
// Create views of children
std::vector<cudf::mutable_column_view> child_views;
child_views.reserve(num_columns_);
for (auto i = 0; i < num_columns_; ++i) {
child_views.push_back(columns_[i].Value()->mutable_view());
}
return cudf::mutable_table_view{child_views};
}
//
// Private API
//
Napi::Value Table::num_columns(Napi::CallbackInfo const& info) {
return CPPToNapi(info)(num_columns());
}
Napi::Value Table::num_rows(Napi::CallbackInfo const& info) { return CPPToNapi(info)(num_rows()); }
Napi::Value Table::get_column(Napi::CallbackInfo const& info) {
cudf::size_type i = CallbackArgs{info}[0];
if (i >= num_columns_) { throw Napi::Error::New(info.Env(), "Column index out of bounds"); }
return columns_[i].Value();
}
Napi::Value Table::order_by(Napi::CallbackInfo const& info) {
CallbackArgs args{info};
NODE_CUDF_EXPECT(args[0].IsArray(), "order_by ascending argument expects an array", args.Env());
NODE_CUDF_EXPECT(args[1].IsArray(), "order_by null_order argument expects an array", args.Env());
std::vector<bool> ascending = args[0];
std::vector<bool> null_order = args[1];
rmm::mr::device_memory_resource* mr = args[2];
NODE_CUDF_EXPECT(ascending.size() == null_order.size(),
"ascending and null_order must be the same size",
args.Env());
auto table_view = view();
std::vector<cudf::order> column_order;
column_order.reserve(ascending.size());
for (auto i : ascending) {
if (i) {
column_order.push_back(cudf::order::ASCENDING);
} else {
column_order.push_back(cudf::order::DESCENDING);
}
}
std::vector<cudf::null_order> null_precedece;
null_precedece.reserve(null_order.size());
for (auto i : null_order) {
if (i) {
null_precedece.push_back(cudf::null_order::BEFORE);
} else {
null_precedece.push_back(cudf::null_order::AFTER);
}
}
try {
return Column::New(info.Env(),
cudf::sorted_order(table_view, column_order, null_precedece, mr));
} catch (std::exception const& e) { throw Napi::Error::New(Env(), e.what()); }
}
} // namespace nv
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/addon.cpp
|
// Copyright (c) 2020-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "node_cudf/column.hpp"
#include "node_cudf/groupby.hpp"
#include "node_cudf/scalar.hpp"
#include "node_cudf/table.hpp"
#include "node_cudf/utilities/dtypes.hpp"
#include <nv_node/addon.hpp>
#include <nv_node/macros.hpp>
#include <napi.h>
struct rapidsai_cudf : public nv::EnvLocalAddon, public Napi::Addon<rapidsai_cudf> {
rapidsai_cudf(Napi::Env const& env, Napi::Object exports) : EnvLocalAddon(env, exports) {
DefineAddon(exports,
{
InstanceMethod("init", &rapidsai_cudf::InitAddon),
InstanceValue("_cpp_exports", _cpp_exports.Value()),
InstanceMethod<&rapidsai_cudf::find_common_type>("findCommonType"),
InstanceValue("Column", InitClass<nv::Column>(env, exports)),
InstanceValue("Table", InitClass<nv::Table>(env, exports)),
InstanceValue("Scalar", InitClass<nv::Scalar>(env, exports)),
InstanceValue("GroupBy", InitClass<nv::GroupBy>(env, exports)),
});
}
private:
Napi::Value find_common_type(Napi::CallbackInfo const& info) {
return nv::find_common_type(info);
}
};
NODE_API_ADDON(rapidsai_cudf);
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/column_accessor.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {Column} from './column';
import {ColumnsMap, TypeMap} from './types/mappings';
export class ColumnAccessor<T extends TypeMap = any> {
private _labels_to_indices = new Map<keyof T, number>();
constructor(private _data: ColumnsMap<T>) {
const columns = Object.values(_data);
if (columns.length > 0) {
const N = columns[0].length;
if (!columns.every((col) => col.length == N)) {
throw new Error('Column lengths must all be the same');
}
}
this._data = _data;
this.names.forEach((val, index) => this._labels_to_indices.set(val, index));
}
private declare _names: ReadonlyArray<(string & keyof T)>;
get names() {
return this._names ||
(this._names = Object.freeze(Object.keys(this._data) as (string & keyof T)[]));
}
private declare _types: T;
get types() {
return this._types || (this._types = Object.freeze(this.names.reduce((types, name) => {
types[name] = this.get(name).type;
return types;
}, {} as T)));
}
private declare _columns: ReadonlyArray<Column<T[keyof T]>>;
get columns() {
return this._columns || (this._columns = Object.freeze(Object.values(this._data)));
}
get length() { return this._labels_to_indices.size; }
has(name: string) { return name in this._data; }
get<R extends keyof T>(name: R) {
if (!(name in this._data)) { throw new Error(`Unknown column name: ${name.toString()}`); }
return this._data[name];
}
addColumns<R extends TypeMap>(data: ColumnsMap<R>|ColumnAccessor<R>) {
data = (data instanceof ColumnAccessor) ? data._data : data;
return new ColumnAccessor(
{...this._data, ...data} as ColumnsMap<{
[P in keyof(T & R)]: P extends keyof R ? R[P] //
: P extends keyof T ? T[P] //
: never
}>);
}
dropColumns<R extends keyof T>(names: readonly R[]) {
const data = {} as any;
const namesMap = names.reduce((xs, x) => ({...xs, [x]: true}), {});
for (const name of this.names) {
if (!(name in namesMap)) { data[name] = this._data[name]; }
}
return new ColumnAccessor(data as ColumnsMap<{[P in Exclude<keyof T, R>]: T[P]}>);
}
selectByColumnName<R extends keyof T>(name: R) { return this.selectByColumnNames([name]); }
selectByColumnNames<R extends keyof T>(names: readonly R[]) {
const data: ColumnsMap<{[P in R]: T[P]}> = {} as any;
for (const name of names) {
if (this._data[name] && !data[name]) { data[name] = this._data[name]; }
}
return new ColumnAccessor(data);
}
columnNameToColumnIndex(name: keyof T): number|undefined {
return this._labels_to_indices.get(name);
}
}
| 0 |
rapidsai_public_repos/node/modules/cudf
|
rapidsai_public_repos/node/modules/cudf/src/data_frame.ts
|
// Copyright (c) 2020-2022, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import {MemoryData, MemoryView, Uint8Buffer} from '@rapidsai/cuda';
import {DeviceBuffer, MemoryResource} from '@rapidsai/rmm';
import * as arrow from 'apache-arrow';
import {compareTypes} from 'apache-arrow/visitor/typecomparator';
import {Readable} from 'stream';
import {Column} from './column';
import {ColumnAccessor} from './column_accessor';
import {concat as concatDataFrames} from './dataframe/concat';
import {Join, JoinResult} from './dataframe/join';
import {DataFrameFormatter, DisplayOptions} from './dataframe/print';
import {GroupByMultiple, GroupByMultipleProps, GroupBySingle, GroupBySingleProps} from './groupby';
import {DISPOSER, scope} from './scope';
import {Series} from './series';
import {Table, ToArrowMetadata} from './table';
import {ReadCSVOptions, ReadCSVOptionsCommon, WriteCSVOptions} from './types/csv';
import {
Bool8,
DataType,
FloatingPoint,
FloatTypes,
IndexType,
Int32,
Int64,
Integral,
IntegralTypes,
List,
Numeric,
NumericTypes,
Struct,
} from './types/dtypes';
import {DuplicateKeepOption, NullOrder} from './types/enums';
import {ColumnsMap, CommonType, TypeMap} from './types/mappings';
import {ReadORCOptions, ReadORCOptionsCommon, WriteORCOptions} from './types/orc';
import {ReadParquetOptions, ReadParquetOptionsCommon, WriteParquetOptions} from './types/parquet';
export type SeriesMap<T extends TypeMap = any> = {
[P in keyof T]: {readonly type: T[P]}
};
export type OrderSpec = {
ascending?: boolean,
null_order?: keyof typeof NullOrder
};
type JoinType = 'inner'|'outer'|'left'|'right'|'leftsemi'|'leftanti';
type JoinProps<
Rhs extends TypeMap,
TOn extends string,
How extends JoinType = 'inner',
LSuffix extends string = '',
RSuffix extends string = '',
> = {
other: DataFrame<Rhs>;
on: TOn[];
how?: How;
lsuffix?: LSuffix;
rsuffix?: RSuffix;
nullEquality?: boolean;
memoryResource?: MemoryResource;
};
type CombinedGroupByProps<T extends TypeMap, R extends keyof T, IndexKey extends string> =
GroupBySingleProps<T, R>|Partial<GroupByMultipleProps<T, R, IndexKey>>;
function _seriesToColumns<T extends TypeMap>(data: ColumnsMap<T>|SeriesMap<T>) {
const columns = {} as any;
for (const [name, col] of Object.entries(data)) {
if (col instanceof Series) {
columns[name] = col._col;
} else {
columns[name] = Series.new(col)._col;
}
}
return <ColumnsMap<T>>columns;
}
function _throwIfNonNumeric(type: DataType, operationName: string) {
if (!NumericTypes.some((t) => compareTypes(t, type))) {
throw new TypeError(`dtype ${type.toString()} cannot perform the operation: ${operationName}`);
}
}
/**
* A GPU Dataframe object.
*/
export class DataFrame<T extends TypeMap = any> {
/**
* Construct a DataFrame from a Table and list of column names.
*
* @param table The cudf.Table instance
* @param names List of string Column names
*/
public static fromTable<T extends TypeMap>(table: Table, names: readonly(string&keyof T)[]) {
return new DataFrame(names.reduce(
(map, name, i) => ({...map, [name]: table.getColumnByIndex(i)}), {} as ColumnsMap<T>));
}
/**
* Read a CSV file from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import * as cudf from '@rapidsai/cudf';
* const df = cudf.DataFrame.readCSV('test.csv', {
* header: 0,
* dataTypes: {
* a: new cudf.Int16,
* b: new cudf.Bool,
* c: new cudf.Float32,
* d: new cudf.Utf8String
* }
* })
* ```
*/
public static readCSV<T extends TypeMap = any>(path: string,
options?: ReadCSVOptionsCommon<T>): DataFrame<T>;
/**
* Read a CSV file from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame, Series, Int16, Bool, Float32, Utf8String} from '@rapidsai/cudf';
* const df = DataFrame.readCSV({
* header: 0,
* sourceType: 'files',
* sources: ['test.csv'],
* dataTypes: {
* a: new Int16,
* b: new Bool,
* c: new Float32,
* d: new Utf8String
* }
* })
* ```
*/
public static readCSV<T extends TypeMap = any>(options: ReadCSVOptions<T>): DataFrame<T>;
public static readCSV<T extends TypeMap = any>(...args: any[]) {
args = args.flat();
const sources: any[] = args.slice(0, -1);
let options = args[args.length - 1] as ReadCSVOptions<T>| string;
if (typeof options === 'string') {
sources.push(options);
options = {} as ReadCSVOptions<T>;
}
if (sources.length > 0 || !(options && typeof options === 'object')) {
options = {...options, sourceType: 'files', sources};
}
const {names, table} = Table.readCSV(options);
return DataFrame.fromTable<T>(table, names);
}
/**
* Read Apache ORC files from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame} from '@rapidsai/cudf';
* const df = DataFrame.readORC('test.orc', {
* skipRows: 10, numRows: 10,
* })
* ```
*/
public static readORC<T extends TypeMap = any>(paths: string|(string[]),
options?: ReadORCOptionsCommon): DataFrame<T>;
/**
* Read Apache ORC files from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame} from '@rapidsai/cudf';
* const df = DataFrame.readORC({
* sourceType: 'files',
* sources: ['test.orc'],
* })
* ```
*/
public static readORC<T extends TypeMap = any>(options: ReadORCOptions): DataFrame<T>;
public static readORC<T extends TypeMap = any>(...args: any[]) {
args = args.flat();
const sources: any[] = args.slice(0, -1);
let options = args[args.length - 1] as ReadORCOptions | string;
if (typeof options === 'string') {
sources.push(options);
options = {} as ReadORCOptions;
}
if (sources.length > 0 || !(options && typeof options === 'object')) {
options = {...options, sourceType: 'files', sources};
}
const {names, table} = Table.readORC(options);
return DataFrame.fromTable<T>(table, names);
}
/**
* Read Apache Parquet files from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame} from '@rapidsai/cudf';
* const df = DataFrame.readParquet('test.parquet', {
* skipRows: 10, numRows: 10,
* })
* ```
*/
// clang-format off
public static readParquet<T extends TypeMap = any>(paths: string|(string[]),
options?: ReadParquetOptionsCommon): DataFrame<T>;
// clang-format on
/**
* Read Apache Parquet files from disk and create a cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame} from '@rapidsai/cudf';
* const df = DataFrame.readParquet({
* sourceType: 'files',
* sources: ['test.parquet'],
* })
* ```
*/
public static readParquet<T extends TypeMap = any>(options: ReadParquetOptions): DataFrame<T>;
public static readParquet<T extends TypeMap = any>(...args: any[]) {
args = args.flat();
const sources: any[] = args.slice(0, -1);
let options = args[args.length - 1] as ReadParquetOptions | string;
if (typeof options === 'string') {
sources.push(options);
options = {} as ReadParquetOptions;
}
if (sources.length > 0 || !(options && typeof options === 'object')) {
options = {...options, sourceType: 'files', sources};
}
const {names, table} = Table.readParquet(options);
return DataFrame.fromTable<T>(table, names);
}
/**
* Adapts an Arrow Table in IPC format into a DataFrame.
*
* @param memory A buffer holding Arrow table
* @return The Arrow data as a DataFrame
*/
public static fromArrow<T extends TypeMap>(memory: DeviceBuffer|MemoryData): DataFrame<T> {
if (memory instanceof ArrayBuffer || ArrayBuffer.isView(memory)) {
memory = new Uint8Buffer(memory);
}
if (memory instanceof MemoryView) { memory = memory.buffer; }
const {table, fields} = Table.fromArrow(memory);
const colToSeries = (field: arrow.Field, col: Column): Series<any> => {
return Series.new({
type: field.type,
data: col.data,
offset: col.offset,
length: col.length,
nullMask: col.mask,
nullCount: col.nullCount,
children: (field.type.children as arrow.Field[] ?? [])
.map((f, i) => colToSeries(f, col.getChild(i))),
});
};
return new DataFrame(fields.reduce((seriesMap, f, i) => {
return ({...seriesMap, [f.name]: colToSeries(f, table.getColumnByIndex(i))});
}, {} as SeriesMap<T>));
}
declare private _accessor: ColumnAccessor<T>;
/**
* Create a new cudf.DataFrame
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new([true, false]),
* c: Series.new(["foo", "bar"])
* })
*
* ```
*/
constructor(data?: SeriesMap<T>);
constructor(data?: ColumnsMap<T>);
constructor(data?: ColumnAccessor<T>);
constructor(data: any = {}) {
this._accessor =
(data instanceof ColumnAccessor) ? data : new ColumnAccessor(_seriesToColumns(data));
DISPOSER.add(this.asTable());
}
/**
* The number of rows in each column of this DataFrame
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new([1, 2]),
* c: Series.new([1, 2])
* })
*
* df.numRows // 2
* ```
*/
get numRows() { return this._accessor.columns.length > 0 ? this._accessor.columns[0].length : 0; }
/**
* The number of columns in this DataFrame
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new([1, 2]),
* c: Series.new([1, 2])
* })
*
* df.numColumns // 3
* ```
*/
get numColumns() { return this._accessor.length; }
/**
* The names of columns in this DataFrame
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new([1, 2]),
* c: Series.new([1, 2])
* })
*
* df.names // ['a', 'b', 'c']
* ```
*/
get names() { return this._accessor.names; }
/**
* A map of this DataFrame's Series names to their DataTypes
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new(["foo", "bar"]),
* c: Series.new([[1, 2], [3]]),
* })
*
* df.types
* // {
* // a: [Object Float64],
* // b: [Object Utf8String],
* // c: [Object List]
* // }
* ```
*/
get types() { return this._accessor.types; }
/** @ignore */
asTable() { return new Table({columns: this._accessor.columns}); }
/** @ignore */
asStruct() {
const {types, _accessor: {columns}} = this;
return Series.new({
nullCount: 0,
children: columns,
length: this.numRows,
type: new Struct(
this.names.map((name, i) => arrow.Field.new(name, types[name], columns[i].nullable))),
});
}
/**
* Return a string with a tabular representation of the DataFrame, pretty-printed according to the
* options given.
*
* @param options
*/
toString(options: DisplayOptions = {}) { return new DataFrameFormatter(options, this).render(); }
[Symbol.for('nodejs.util.inspect.custom')]() {
const [width, maxRows] = process.stdout.getWindowSize();
const rows = this.toString({width, maxRows, maxColWidth: width});
return [
`cols=${this.numColumns.toLocaleString()}, rows=${this.numRows.toLocaleString()}`,
rows,
].join(`\n`);
}
/**
* Return a new DataFrame containing only specified columns.
*
* @param columns Names of columns keep.
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, 1, 1, 2, 2, 2]),
* b: Series.new([0, 1, 2, 3, 4, 4]),
* c: Series.new([1, 2, 3, 4, 5, 6])
* })
*
* df.select(['a', 'b']) // returns df with {a, b}
* ```
*/
select<R extends keyof T>(names: readonly R[]) {
return new DataFrame(this._accessor.selectByColumnNames(names));
}
/**
* Return a new DataFrame with new columns added.
*
* @param {SeriesMap<R>|DataFrame<R>} data mapping of names to new columns to add, or a GPU
* DataFrame object
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({a: [1, 2, 3]});
*
* df.assign({b: Series.new(["foo", "bar", "bar"])})
* // returns df {a: [1, 2, 3], b: ["foo", "bar", "bar"]}
* ```
*
* @example
* ```typescript
* import {DataFrame} from '@rapidsai/cudf';
*
* const df = new DataFrame({a: [1, 2, 3]});
* const df1 = new DataFrame({b: ["foo", "bar", "bar"]});
*
* df.assign(df1) // returns df {a: [1, 2, 3], b: ["foo", "bar", "bar"]}
* ```
*/
assign<R extends TypeMap>(data: SeriesMap<R>|DataFrame<R>) {
const columns = (data instanceof DataFrame) ? data._accessor : _seriesToColumns(data);
return new DataFrame(this._accessor.addColumns(columns));
}
/**
* Return a new DataFrame with specified columns removed.
*
* @param names Names of the columns to drop.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Float32, data: [0, 1, 2, 3, 4, 4]})
* });
*
* df.drop(['a']) // returns df {b: [0, 1, 2, 3, 4, 4]}
* ```
*/
drop<R extends keyof T>(names: readonly R[]) {
return new DataFrame(this._accessor.dropColumns(names));
}
/**
* Return a new DataFrame with specified columns renamed.
*
* @param nameMap Object mapping old to new Column names.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Float32, data: [0, 1, 2, 3, 4, 4]})
* });
*
* df.rename({a: 'c'}) // returns df {b: [0, 1, 2, 3, 4, 4], c: [0, 1, 1, 2, 2, 2]}
* ```
*/
rename<U extends string|number, P extends {[K in keyof T]?: U}>(nameMap: P) {
const names = Object.keys(nameMap) as (string & keyof P)[];
return this.drop(names).assign(
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
names.reduce((xs, x) => ({...xs, [`${nameMap[x]!}`]: this.get(x)}),
{} as SeriesMap<{[K in keyof P as `${NonNullable < P[K] >}`]: T[string & K]}>));
}
/**
* Return whether the DataFrame has a Series.
*
* @param name Name of the Series to return.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Float32, data: [0, 1, 2, 3, 4, 4]})
* });
*
* df.has('a') // true
* df.has('c') // false
* ```
*/
has(name: string) { return this._accessor.has(name); }
/**
* Return a series by name.
*
* @param name Name of the Series to return.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Float32, data: [0, 1, 2, 3, 4, 4]})
* });
*
* df.get('a') // Int32Series
* df.get('b') // Float32Series
* ```
*/
get<P extends keyof T>(name: P): Series<T[P]> { return Series.new(this._accessor.get(name)); }
/**
* Casts each selected Series in this DataFrame to a new dtype (similar to `static_cast` in C++).
*
* @param dataTypes The map from column names to new dtypes.
* @param memoryResource The optional MemoryResource used to allocate the result Series's device
* memory.
* @returns DataFrame of Series cast to the new dtype
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 4]})
* });
*
* df.cast({a: new Float32}); // returns df with a as Float32Series and b as Int32Series
* ```
*/
cast<R extends {[P in keyof T]?: DataType}>(dataTypes: R, memoryResource?: MemoryResource) {
const names = this.names;
const types = !(dataTypes instanceof arrow.DataType)
? dataTypes
: names.reduce((types, name) => ({...types, [name]: dataTypes}), {} as R);
return new DataFrame(names.reduce(
(columns, name) => ({
...columns,
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
[name]: name in types ? this.get(name).cast(types[name]!, memoryResource) : this.get(name)
}),
{} as SeriesMap<{[P in keyof(Omit<T, keyof R>& R)]: (Omit<T, keyof R>& R)[P]}>));
}
/**
* Casts all the Series in this DataFrame to a new dtype (similar to `static_cast` in C++).
*
* @param dataType The new dtype.
* @param memoryResource The optional MemoryResource used to allocate the result Series's device
* memory.
* @returns DataFrame of Series cast to the new dtype
*make notebooks.run
* a: Series.new({type: new Int32, data: [0, 1, 1, 2, 2, 2]}),
* b: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 4]})
* })
*
* df.castAll(new Float32); // returns df with a and b as Float32Series
* ```
*/
castAll<R extends DataType>(dataType: R, memoryResource?: MemoryResource) {
return new DataFrame(this.names.reduce(
(columns, name) => ({...columns, [name]: this.get(name).cast(dataType, memoryResource)}),
{} as SeriesMap<{[P in keyof T]: R}>));
}
/**
* Concat DataFrame(s) to the end of the caller, returning a new DataFrame.
*
* @param others The DataFrame(s) to concat to the end of the caller.
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([1, 2, 3, 4]),
* b: Series.new([1, 2, 3, 4]),
* });
*
* const df2 = new DataFrame({
* a: Series.new([5, 6, 7, 8]),
* });
*
* df.concat(df2);
* // return {
* // a: [1, 2, 3, 4, 5, 6, 7, 8],
* // b: [1, 2, 3, 4, null, null, null, null],
* // }
* ```
*/
concat<U extends DataFrame[]>(...others: U) { return concatDataFrames(this, ...others); }
/**
* @summary Explicitly free the device memory associated with this DataFrame.
*/
dispose() {
this.names.forEach((name) => this.get(name).dispose());
this._accessor = new ColumnAccessor({} as ColumnsMap<T>);
}
/**
* @summary Flatten the elements of this DataFrame's list columns, duplicating the corresponding
* rows for other columns in this DataFrame.
*
* @param {string[]} names Names of List Columns to flatten. Defaults to all list Columns.
* @param {boolean} [includeNulls=true] Whether to retain null entries and map empty lists to
* null.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
flatten<R extends string&keyof T>(names: readonly R[] = this.names as any,
includeNulls = true,
memoryResource?: MemoryResource) {
const listColumnIndices =
names.map((n) => [this.types[n], this.names.indexOf(n)] as [DataType, number])
.filter(([t]) => arrow.DataType.isList(t))
.map(([, i]) => i);
type ListChild<T extends DataType> = T extends List ? T['valueType'] : T;
type U = {
// clang-format off
[P in R | keyof T]:
P extends R
? T[P] extends List
? ListChild<T[P]>
: T[P]
: T[P]
// clang-format on
};
return scope(() => {
return listColumnIndices.reduce((df, i, j, a) => {
return scope(() => {
const mr = j === a.length - 1 ? memoryResource : undefined;
const table = includeNulls ? df.asTable().explodeOuter(i, mr) //
: df.asTable().explode(i, mr);
return new DataFrame(df.names.reduce((series_map, name, index) => {
if (index === i) {
series_map[name] =
(this.get(name) as any).elements.__construct(table.getColumnByIndex(index));
} else {
series_map[name] = df.__constructChild(name, table.getColumnByIndex(index));
}
return series_map;
}, {} as SeriesMap<U>));
}, [this]) as any;
}, new DataFrame<U>(this._accessor as any));
}, [this]);
}
/**
* @summary Flatten the elements of this DataFrame's list columns into their positions in its
* original list, duplicating the corresponding rows for other columns in this DataFrame.
*
* @param {string[]} names Names of List Columns to flatten. Defaults to all list Columns.
* @param {boolean} [includeNulls=true] Whether to retain null entries and map empty lists to
* null.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*/
flattenIndices<R extends string&keyof T>(names: readonly R[] = this.names as any,
includeNulls = true,
memoryResource?: MemoryResource) {
const listColumnIndices =
names.map((n) => [this.types[n], this.names.indexOf(n)] as [DataType, number])
.filter(([t]) => arrow.DataType.isList(t))
.map(([, i]) => i);
type U = {
// clang-format off
[P in R | keyof T]:
P extends R
? T[P] extends List
? Int32
: T[P]
: T[P]
// clang-format on
};
return scope(() => {
return listColumnIndices.reduce((df, i, j, a) => {
return scope(() => {
const mr = j === a.length - 1 ? memoryResource : undefined;
const table = includeNulls ? df.asTable().explodeOuterPosition(i, mr) //
: df.asTable().explodePosition(i, mr);
return new DataFrame(df.names.reduce((series_map, name, index) => {
if (index === i) {
series_map[name] = Series.new(table.getColumnByIndex(index));
} else {
series_map[name] =
df.__constructChild(name, table.getColumnByIndex(+(index >= i) + index));
}
return series_map;
}, {} as SeriesMap<U>));
}, [this]) as any;
}, new DataFrame<U>(this._accessor as any));
}, [this]);
}
/**
* @summary Interleave columns of a DataFrame into a single Series.
*
* @param dataType The dtype of the result Series (required if the DataFrame has mixed dtypes).
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @returns Series representing a packed row-major matrix of all the source DataFrame's Series.
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* new DataFrame({
* a: Series.new([1, 2, 3]),
* b: Series.new([4, 5, 6]),
* }).interleaveColumns()
* // Float64Series [
* // 1, 4, 2, 5, 3, 6
* // ]
*
* new DataFrame({
* b: Series.new([ [0, 1, 2], [3, 4, 5], [6, 7, 8]]),
* c: Series.new([[10, 11, 12], [13, 14, 15], [16, 17, 18]]),
* }).interleaveColumns()
* // ListSeries [
* // [0, 1, 2],
* // [10, 11, 12],
* // [3, 4, 5],
* // [13, 14, 15],
* // [6, 7, 8],
* // [16, 17, 18],
* // ]
*
*/
interleaveColumns<R extends T[keyof T] = T[keyof T]>(dataType?: R|null,
memoryResource?: MemoryResource) {
return Series.new<R>(
(dataType ? this.castAll(dataType) : this).asTable().interleaveColumns(memoryResource));
}
/**
* Generate an ordering that sorts DataFrame columns in a specified way
*
* @param options mapping of column names to sort order specifications
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @returns Series containting the permutation indices for the desired sort order
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, NullOrder} from '@rapidsai/cudf';
* const df = new DataFrame({a: Series.new([null, 4, 3, 2, 1, 0])});
*
* df.orderBy({a: {ascending: true, null_order: 'before'}});
* // Int32Series [0, 5, 4, 3, 2, 1]
*
* df.orderBy({a: {ascending: true, null_order: 'after'}});
* // Int32Series [5, 4, 3, 2, 1, 0]
*
* df.orderBy({a: {ascending: false, null_order: 'before'}});
* // Int32Series [1, 2, 3, 4, 5, 0]
*
* df.orderBy({a: {ascending: false, null_order: 'after'}});
* // Int32Series [0, 1, 2, 3, 4, 5]
* ```
*/
orderBy<R extends keyof T>(options: {[P in R]: OrderSpec}, memoryResource?: MemoryResource) {
const column_orders = new Array<boolean>();
const null_orders = new Array<boolean>();
const columns = new Array<Column<T[keyof T]>>();
const entries = Object.entries(options) as [R, OrderSpec][];
entries.forEach(([name, {ascending = true, null_order = 'after'}]) => {
const child = this.get(name);
if (child) {
columns.push(child._col as Column<T[keyof T]>);
column_orders.push(ascending);
null_orders.push(null_order === 'before');
}
});
// Compute the sorted sorted_indices
return Series.new(new Table({columns}).orderBy(column_orders, null_orders, memoryResource));
}
/**
* Generate a new DataFrame sorted in the specified way.
*
* @param ascending whether to sort ascending (true) or descending (false)
* Default: true
* @param null_order whether nulls should sort before or after other values
* Default: after
*
* @returns A new DataFrame of sorted values
*
* @example
* ```typescript
* import {DataFrame, Series, Int32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([null, 4, 3, 2, 1, 0]),
* b: Series.new([0, 1, 2, 3, 4, 5])
* });
*
* df.sortValues({a: {ascending: true, null_order: 'after'}})
* // {a: [0, 1, 2, 3, 4, null], b: [5, 4, 3, 2, 1, 0]}
*
* df.sortValues({a: {ascending: true, null_order: 'before'}})
* // {a: [null, 0, 1, 2, 3, 4], b: [0, 5, 4, 3, 2, 1]}
*
* df.sortValues({a: {ascending: false, null_order: 'after'}})
* // {a: [4, 3, 2, 1, 0, null], b: [1, 2, 3, 4, 5, 0]}
*
* df.sortValues({a: {ascending: false, null_order: 'before'}})
* // {a: [null, 4, 3, 2, 1, 0], b: [0, 1, 2, 3, 4, 5]}
* ```
*/
sortValues<R extends keyof T>(options: {[P in R]: OrderSpec}, memoryResource?: MemoryResource) {
return this.gather(this.orderBy(options), false, memoryResource);
}
/**
* @summary Return sub-selection from a DataFrame using the specified integral indices.
*
* @description Gathers the rows of the source columns according to `selection`, such that row "i"
* in the resulting Table's columns will contain row `selection[i]` from the source columns. The
* number of rows in the result table will be equal to the number of elements in selection. A
* negative value i in the selection is interpreted as i+n, where `n` is the number of rows in
* the source table.
*
* For dictionary columns, the keys column component is copied and not trimmed if the gather
* results in abandoned key elements.
*
* @param selection A Series of 8/16/32-bit signed or unsigned integer indices to gather.
* @param nullify_out_of_bounds If `true`, coerce rows that corresponds to out-of-bounds indices
* in the selection to null. If `false`, skips all bounds checking for selection values. Pass
* false if you are certain that the selection contains only valid indices for better
* performance. If `false` and there are out-of-bounds indices in the selection, the behavior
* is undefined. Defaults to `false`.
* @param memoryResource An optional MemoryResource used to allocate the result's device memory.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 5]}),
* b: Series.new([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
* });
*
* const selection = Series.new({type: new Int32, data: [2,4,5]});
*
* df.gather(selection); // {a: [2, 4, 5], b: [2.0, 4.0, 5.0]}
* ```
*/
gather<R extends IndexType>(selection: Series<R>,
nullify_out_of_bounds = false,
memoryResource?: MemoryResource) {
const columns = this.asTable().gather(selection._col, nullify_out_of_bounds, memoryResource);
const series_map = {} as SeriesMap<T>;
this.names.forEach((name, index) => {
series_map[name] = this.__constructChild(name, columns.getColumnByIndex(index));
});
return new DataFrame(series_map);
}
/**
* Returns the first n rows as a new DataFrame.
*
* @param n The number of rows to return.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 5, 6]}),
* b: Series.new([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
* });
*
* a.head();
* // {a: [0, 1, 2, 3, 4], b: [0.0, 1.0, 2.0, 3.0, 4.0]}
*
* b.head(1);
* // {a: [0], b: [0.0]}
*
* a.head(-1);
* // throws index out of bounds error
* ```
*/
head(n = 5): DataFrame<T> {
if (n < 0) { throw new Error('Index provided is out of bounds'); }
const selection =
Series.sequence({type: new Int32, size: n < this.numRows ? n : this.numRows, init: 0});
return this.gather(selection);
}
/**
* Returns the last n rows as a new DataFrame.
*
* @param n The number of rows to return.
*
* @example
* ```typescript
* import {DataFrame, Series, Int32} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 5, 6]}),
* b: Series.new([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
* });
*
* a.tail();
* // {a: [2, 3, 4, 5, 6], b: [2.0, 3.0, 4.0, 5.0, 6.0]}
*
* b.tail(1);
* // {a: [6], b: [6.0]}
*
* a.tail(-1);
* // throws index out of bounds error
* ```
*/
tail(n = 5): DataFrame<T> {
if (n < 0) { throw new Error('Index provided is out of bounds'); }
const length = n < this.numRows ? n : this.numRows;
const selection = Series.sequence({type: new Int32, size: length, init: this.numRows - length});
return this.gather(selection);
}
/**
* Return a group-by on a single column.
*
* @param props configuration for the groupby
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, 1, 1, 2, 2, 2]),
* b: Series.new([0, 1, 2, 3, 4, 4]),
* c: Series.new([1, 2, 3, 4, 5, 6])
* })
*
* df.groupby({by: 'a'}).max() // { a: [2, 1, 0], b: [4, 2, 0], c: [6, 3, 1] }
*
* ```
*/
groupBy<R extends keyof T>(props: GroupBySingleProps<T, R>): GroupBySingle<T, R>;
/**
* Return a group-by on a multiple columns.
*
* @param props configuration for the groupby
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, 1, 1, 2, 2, 2]),
* b: Series.new([0, 1, 2, 3, 4, 4]),
* c: Series.new([1, 2, 3, 4, 5, 6])
* })
*
* df.groupby({by: ['a', 'b']}).max()
* // {
* // "a_b": [{"a": [2, 1, 1, 2, 0], "b": [4, 2, 1, 3, 0]}],
* // "c": [6, 3, 2, 4, 1]
* // }
*
* ```
*/
groupBy<R extends keyof T, IndexKey extends string>(props: GroupByMultipleProps<T, R, IndexKey>):
GroupByMultiple<T, R, IndexKey>;
groupBy<R extends keyof T, IndexKey extends string>(props: CombinedGroupByProps<T, R, IndexKey>) {
if (!Array.isArray(props.by)) {
return new GroupBySingle(this, props as GroupBySingleProps<T, R>);
} else if ('index_key' in props) {
return new GroupByMultiple(this, props as GroupByMultipleProps<T, R, IndexKey>);
} else {
return new GroupByMultiple(this, {
...props,
index_key: props.by.join('_'),
} as GroupByMultipleProps<T, R, any>);
}
}
/**
* Return sub-selection from a DataFrame from the specified boolean mask.
*
* @param mask
*
* @example
* ```typescript
* import {DataFrame, Series, Bool8} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, 1, 2, 3, 4, 4]),
* b: Series.new([0, NaN, 2, 3, 4, 4])
* })
* const mask = Series.new({type: new Bool8, data: [0, 0, 1, 0, 1, 1]})
*
* df.filter(mask); // {a: [2, 4, 4], b: [2, 4, 4]}
*
* ```
*/
filter(mask: Series<Bool8>, memoryResource?: MemoryResource) {
const table = this.asTable().applyBooleanMask(mask._col, memoryResource);
const series_map = {} as SeriesMap<T>;
this.names.forEach((name, index) => {
series_map[name] = this.__constructChild(name, table.getColumnByIndex(index));
});
return new DataFrame(series_map);
}
/**
* Join columns with other DataFrame.
*
* @param props the configuration for the join
* @returns the joined DataFrame
*/
// clang-format off
join<R extends TypeMap, TOn extends (string & keyof T & keyof R), LSuffix extends string = '', RSuffix extends string = ''>(
props: JoinProps<R, TOn, 'inner'|'outer'|'left'|'right', LSuffix, RSuffix>
): DataFrame<{
[P in keyof JoinResult<T, R, TOn, LSuffix, RSuffix>]:
P extends TOn
? CommonType<T[P], R[P]>
: JoinResult<T, R, TOn, LSuffix, RSuffix>[P]
}>;
// clang-format on
/**
* Join columns with other DataFrame.
*
* @param props the configuration for the join
* @returns the joined DataFrame
*/
// clang-format off
join<R extends TypeMap, TOn extends (string & keyof T & keyof R)>(
props: JoinProps<R, TOn, 'leftsemi'|'leftanti'>
): DataFrame<T>;
// clang-format on
// clang-format off
join(props: any): any {
// clang-format on
const {how = 'inner', other, ...opts} = props;
switch (how) {
case 'left': return new Join({...opts, lhs: this, rhs: other}).left();
case 'right': return new Join({...opts, lhs: this, rhs: other}).right();
case 'inner': return new Join({...opts, lhs: this, rhs: other}).inner();
case 'outer': return new Join({...opts, lhs: this, rhs: other}).outer();
case 'leftsemi': return new Join({...opts, lhs: this, rhs: other}).leftSemi();
case 'leftanti': return new Join({...opts, lhs: this, rhs: other}).leftAnti();
}
}
/**
* Serialize this DataFrame to CSV format.
*
* @param options Options controlling CSV writing behavior.
*
* @returns A node ReadableStream of the CSV data.
*/
toCSV(options: WriteCSVOptions = {}) {
const readable = new Readable({encoding: 'utf8'});
this.asTable().writeCSV({
...options,
next(buf) { readable.push(buf); },
complete() { readable.push(null); },
columnNames: this.names as string[],
});
return readable as AsyncIterable<string>;
}
/**
* Write a DataFrame to ORC format.
*
* @param filePath File path or root directory path.
* @param options Options controlling ORC writing behavior.
*
*/
toORC(filePath: string, options: WriteORCOptions = {}) {
this.asTable().writeORC(filePath, {...options, columnNames: this.names as string[]});
}
/**
* Write a DataFrame to Parquet format.
*
* @param filePath File path or root directory path.
* @param options Options controlling Parquet writing behavior.
*
*/
toParquet(filePath: string, options: WriteParquetOptions = {}) {
this.asTable().writeParquet(filePath, {...options, columnNames: this.names as string[]});
}
/**
* Copy a Series to an Arrow vector in host memory
*
* @example
* ```typescript
* import {DataFrame, Series} from "@rapidsai/cudf";
*
* const df = new DataFrame({a: Series.new([0,1,2]), b: Series.new(["one", "two", "three"])});
*
* const arrow_df = df.toArrow(); // Arrow table
*
* arrow_df.toArray();
* // [
* // { "a": 0, "b": "one" },
* // { "a": 1, "b": "two" },
* // { "a": 2, "b": "three" }
* // ]
* ```
*/
toArrow() {
const toArrowMetadata = (name: string|number, type?: DataType): ToArrowMetadata => {
if (!type || !type.children || !type.children.length) { return [name]; }
if (type instanceof arrow.List) {
if (!type.children[0]) { return [name, [[0], [1]]]; }
return [name, [[0], toArrowMetadata(type.children[0].name, type.children[0].type)]];
}
return [name, type.children.map((f) => toArrowMetadata(f.name, f.type))];
};
const names = this.names.map((name) => toArrowMetadata(<string|number>name, this.types[name]));
return arrow.tableFromIPC<T>(this.asTable().toArrow(names));
}
protected __constructChild<P extends keyof T, R extends T[P]>(name: P,
col: Column<R>): Series<R> {
return (this.get(name) as any).__construct(col);
}
/**
* drop null rows
* @ignore
*/
protected _dropNullsRows(thresh = 1, subset = this.names) {
const column_indices: number[] = [];
const allNames = this.names;
subset.forEach((col) => {
if (this.has(col)) {
column_indices.push(allNames.indexOf(col));
} else {
throw new Error(`Unknown column name: ${col.toString()}`);
}
});
const result = this.asTable().dropNulls(column_indices, thresh);
return new DataFrame(allNames.reduce(
(map, name, i) => ({...map, [name]: this.__constructChild(name, result.getColumnByIndex(i))}),
{} as SeriesMap<T>));
}
/**
* drop rows with NaN values (float type only)
* @ignore
*/
protected _dropNaNsRows(thresh = 1, subset = this.names) {
const column_indices: number[] = [];
const allNames = this.names;
subset.forEach((col) => {
if (this.has(col) && FloatTypes.some((t) => compareTypes(this.types[col], t))) {
column_indices.push(allNames.indexOf(col));
} else if (!this.has(col)) {
throw new Error(`Unknown column name: ${col.toString()}`);
} else {
// col exists but not of floating type
thresh -= 1;
}
});
const result = this.asTable().dropNans(column_indices, thresh);
return new DataFrame(allNames.reduce(
(map, name, i) => ({...map, [name]: this.__constructChild(name, result.getColumnByIndex(i))}),
{} as SeriesMap<T>));
}
/**
* drop columns with nulls
* @ignore
*/
protected _dropNullsColumns(thresh = 1, subset?: Series) {
const column_names: (keyof T)[] = [];
const df = (subset !== undefined) ? this.gather(subset) : this;
this.names.forEach(col => {
const no_threshold_valid_count = (df.get(col).length - df.get(col).nullCount) < thresh;
if (!no_threshold_valid_count) { column_names.push(col as string); }
});
return new DataFrame(
column_names.reduce((map, name) => ({...map, [name]: this.get(name)}), {} as SeriesMap<T>));
}
/**
* drop columns with NaN values(float type only)
* @ignore
*/
protected _dropNaNsColumns(thresh = 1, subset?: Series, memoryResource?: MemoryResource) {
const column_names: (keyof T)[] = [];
const df = (subset !== undefined) ? this.gather(subset) : this;
this.names.forEach(col => {
if (FloatTypes.some((t) => compareTypes(this.get(col).type, t))) {
const nanCount =
df.get(col)._col.nansToNulls(memoryResource).nullCount - this.get(col).nullCount;
const no_threshold_valid_count = (df.get(col).length - nanCount) < thresh;
if (!no_threshold_valid_count) { column_names.push(col); }
} else {
column_names.push(col);
}
});
return new DataFrame(
column_names.reduce((map, name) => ({...map, [name]: this.get(name)}), {} as SeriesMap<T>));
}
/**
* Drops rows (or columns) containing nulls (*Note: only null values are dropped and not NaNs)
*
* @param axis Whether to drop rows (axis=0, default) or columns (axis=1) containing nulls
* @param thresh drops every row (or column) containing less than thresh non-null values.
*
* thresh=1 (default) drops rows (or columns) containing all null values (non-null < thresh(1)).
*
* if axis = 0, thresh=df.numColumns: drops only rows containing at-least one null value
* (non-null values in a row < thresh(df.numColumns)).
*
* if axis = 1, thresh=df.numRows: drops only columns containing at-least one null values
* (non-null values in a column < thresh(df.numRows)).
*
* @param subset List of columns to consider when dropping rows (all columns are considered by
* default).
* Alternatively, when dropping columns, subset is a Series<Integer> with indices to select rows
* (all rows are considered by default).
* @returns DataFrame<T> with dropped rows (or columns) containing nulls
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, null, 2, null, 4, 4]),
* b: Series.new([0, null, 2, 3, null, 4]),
* c: Series.new([null, null, null, null, null, null])
* });
*
* // delete rows with all nulls (default thresh=1)
* df.dropNulls(0);
* // return {
* // a: [0, 2, null, 4, 4], b: [0, 2, 3, null, 4],
* // c: [null, null, null, null, null]
* // }
*
* // delete rows with atleast one null
* df.dropNulls(0, df.numColumns);
* // returns empty df, since each row contains atleast one null
*
* // delete columns with all nulls (default thresh=1)
* df.dropNulls(1);
* // returns {a: [0, null, 2, null, 4, 4], b: [0, null, 2, 3, null, 4]}
*
* // delete columns with atleast one null
* df.dropNulls(1, df.numRows);
* // returns empty df, since each column contains atleast one null
*
* ```
*/
dropNulls<R extends IndexType>(axis = 0, thresh = 1, subset?: (string&keyof T)[]|Series<R>):
DataFrame<T> {
if (axis == 0) {
if (subset instanceof Series) {
throw new Error(
'for axis=0, expected \'subset\' to be one of {list of column_names, undefined(all columns)}');
}
return this._dropNullsRows(thresh, subset);
} else if (axis == 1) {
if (subset instanceof Array) {
throw new Error(
'for axis=1, expected \'subset\' to be one of {Series<Integer> with indices to select rows, undefined(all rows)}');
}
return this._dropNullsColumns(thresh, subset);
} else {
throw new Error('invalid axis value, expected {0, 1} ');
}
}
/**
* Drops rows (or columns) containing NaN, provided the columns are of type float
*
* @param axis Whether to drop rows (axis=0, default) or columns (axis=1) containing NaN
* @param thresh drops every row (or column) containing less than thresh non-NaN values.
*
* thresh=1 (default) drops rows (or columns) containing all NaN values (non-NaN < thresh(1)).
*
* if axis = 0, thresh=df.numColumns: drops only rows containing at-least one NaN value (non-NaN
* values in a row < thresh(df.numColumns)).
*
* if axis = 1, thresh=df.numRows: drops only columns containing at-least one NaN values
* (non-NaN values in a column < thresh(df.numRows)).
* @param subset List of float columns to consider when dropping rows (all float columns are
* considered by default).
* Alternatively, when dropping columns, subset is a Series<Integer> with indices to select rows
* (all rows are considered by default).
*
* @returns DataFrame<T> with dropped rows (or columns) containing NaN
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, NaN, 2, NaN, 4, 4]),
* b: Series.new([0, NaN, 2, 3, NaN, 4]),
* c: Series.new([NaN, NaN, NaN, NaN, NaN, NaN])
* });
*
* // delete rows with all NaNs (default thresh=1)
* df.dropNaNs(0);
* // return {
* // a: [0, 2, NaN, 4, 4], b: [0, 2, 3, NaN, 4],
* // c: [NaN, NaN, NaN, NaN,NaN]
* // }
*
* // delete rows with atleast one NaN
* df.dropNaNs(0, df.numColumns);
* // returns empty df, since each row contains atleast one NaN
*
* // delete columns with all NaNs (default thresh=1)
* df.dropNaNs(1);
* // returns {a: [0, NaN, 2, NaN, 4, 4], b: [0, NaN, 2, 3, NaN, 4]}
*
* // delete columns with atleast one NaN
* df.dropNaNs(1, df.numRows);
* // returns empty df, since each column contains atleast one NaN
*
* ```
*/
dropNaNs<R extends IndexType>(axis = 0, thresh = 1, subset?: (string&keyof T)[]|Series<R>):
DataFrame<T> {
if (axis == 0) {
if (subset instanceof Series) {
throw new Error(
'for axis=0, expected \'subset\' to be one of {list of column_names, undefined(all columns)}');
}
return this._dropNaNsRows(thresh, subset);
} else if (axis == 1) {
if (subset instanceof Array) {
throw new Error(
'for axis=1, expected \'subset\' to be one of {Series<Integer> with indices to select rows, undefined(all rows)}');
}
return this._dropNaNsColumns(thresh, subset);
} else {
throw new Error('invalid axis value, expected {0, 1} ');
}
}
/**
* Compute the trigonometric sine for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.sin();
* // return {
* // a: [0, 0, 0],
* // }
* ```
*/
sin<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `sin`);
series_map[name] = (ser as any).sin(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the trigonometric cosine for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.cos();
* // return {
* // a: [0, 1, 0],
* // }
* ```
*/
cos<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `cos`);
series_map[name] = (ser as any).cos(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the trigonometric tangent for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.tan();
* // return {
* // a: [0, 0, 0],
* // }
* ```
*/
tan<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `tan`);
series_map[name] = (ser as any).tan(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the trigonometric sine inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.asin();
* // return {
* // a: [0, 0, 0],
* // }
* ```
*/
asin<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `asin`);
series_map[name] = (ser as any).asin(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the trigonometric cosine inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.acos();
* // return {
* // a: [0, 1, 0],
* // }
* ```
*/
acos<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `acos`);
series_map[name] = (ser as any).acos(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the trigonometric tangent inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.atan();
* // return {
* // a: [-1, 0, 1],
* // }
* ```
*/
atan<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `atan`);
series_map[name] = (ser as any).atan(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic sine for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.sinh();
* // return {
* // a: [-10, 0, 10],
* // }
* ```
*/
sinh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `sinh`);
series_map[name] = (ser as any).sinh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic cosine for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.cosh();
* // return {
* // a: [10, 1, 10],
* // }
* ```
*/
cosh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `cosh`);
series_map[name] = (ser as any).cosh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic tangent for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.tanh();
* // return {
* // a: [0, 0, 0],
* // }
* ```
*/
tanh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `tanh`);
series_map[name] = (ser as any).tanh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic sine inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.asinh();
* // return {
* // a: [-1, 0, 1],
* // }
* ```
*/
asinh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `asinh`);
series_map[name] = (ser as any).asinh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic cosine inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.acosh();
* // return {
* // a: [0, 0, 1],
* // }
* ```
*/
acosh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `acosh`);
series_map[name] = (ser as any).acosh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the hyperbolic tangent inverse for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series, Int8} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new({type: new Int8, data: [-3, 0, 3]})
* });
* df.atanh();
* // return {
* // a: [0, 0, 0],
* // }
* ```
*/
atanh<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `atanh`);
series_map[name] = (ser as any).atanh(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the exponential (base e, euler number) for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5])
* });
* df.exp();
* // return {
* // a: [0.30119421191220214, 12.182493960703473],
* // }
* ```
*/
exp<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `exp`);
series_map[name] = (ser as any).exp(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the natural logarithm (base e) for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5, 4])
* });
* df.log();
* // return {
* // a: [NaN, 0.9162907318741551, 1.3862943611198906],
* // }
* ```
*/
log<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `log`);
series_map[name] = (ser as any).log(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the square-root (x^0.5) for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5, 4])
* });
* df.sqrt();
* // return {
* // a: [NaN, 1.5811388300841898, 2],
* // }
* ```
*/
sqrt<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `sqrt`);
series_map[name] = (ser as any).sqrt(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the cube-root (x^(1.0/3)) for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5])
* });
* df.cbrt();
* // return {
* // a: [-1.0626585691826111, 1.3572088082974534],
* // }
* ```
*/
cbrt<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `cbrt`);
series_map[name] = (ser as any).cbrt(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the smallest integer value not less than arg for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5, -3, 4.6, 5])
* });
* df.ceil();
* // return {
* // a: [-1, 3, -3, 5, 5],
* // }
* ```
*/
ceil<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `ceil`);
series_map[name] = (ser as any).ceil(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the largest integer value not greater than arg for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1.2, 2.5, -3, 4.6, 5])
* });
* df.floor();
* // return {
* // a: [-2, 2, -3, 4, 5],
* // }
* ```
*/
floor<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `floor`);
series_map[name] = (ser as any).floor(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the absolute value for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([-1, 2, -3, 4, 5])
* });
* df.abs();
* // return {
* // a: [1, 2, 3, 4, 5],
* // }
* ```
*/
abs<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `abs`);
series_map[name] = (ser as any).abs(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Compute the logical not (!) for all NumericSeries in the DataFrame
*
* @returns A DataFrame with the operation performed on all NumericSeries
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([0, 1, 2, 3, 4])
* });
* df.not();
* // return {
* // a: [true, false, false, false, false],
* // }
* ```
*/
not<P extends keyof T>(memoryResource?: MemoryResource) {
const series_map = {} as SeriesMap<T>;
this.names.forEach((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `not`);
series_map[name] = (ser as any).not(memoryResource);
});
return new DataFrame(series_map) as T[P] extends Numeric ? DataFrame<T>: never;
}
/**
* Return a Series containing the unbiased kurtosis result for each Series in the
* DataFrame.
*
* @param skipNulls Exclude NA/null values. If an entire row/column is NA, the result will be NA.
* @returns A Series containing the unbiased kurtosis result for all Series in the DataFrame
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([1, 2, 3, 4]),
* b: Series.new([7, 8, 9, 10])
* });
* df.kurtosis(); // {-1.1999999999999904, -1.2000000000000686}
* ```
*/
kurtosis<P extends keyof T>(skipNulls = true) {
const result = this.names.map((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `kurtosis`);
return (this.get(name) as any).kurtosis(skipNulls);
});
return Series.new(result) as any as Series < T[P] extends Numeric ? Numeric : never > ;
}
/**
* Return a Series containing the unbiased skew result for each Series in the
* DataFrame.
*
* @param skipNulls Exclude NA/null values. If an entire row/column is NA, the result will be NA.
* @returns A Series containing the unbiased skew result for all Series in the DataFrame
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([1, 2, 3, 4, 5, 6, 6]),
* b: Series.new([7, 8, 9, 10, 11, 12, 12])
* });
* df.skew(); // {-0.288195490292614, -0.2881954902926153}
* ```
*/
skew<P extends keyof T>(skipNulls = true) {
const result = this.names.map((name) => {
const ser = this.get(name);
_throwIfNonNumeric(ser.type, `skew`);
return (this.get(name) as any).skew(skipNulls);
});
return Series.new(result) as any as Series < T[P] extends Numeric ? Numeric : never > ;
}
/**
* Compute the sum for all Series in the DataFrame.
*
* @param subset List of columns to select (all columns are considered by
* default).
* @param skipNulls The optional skipNulls if true drops NA and null values before computing
* reduction,
* else if skipNulls is false, reduction is computed directly.
* @param memoryResource Memory resource used to allocate the result Column's device memory.
*
* @returns A Series containing the sum of all values for each Series
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([1, 2]),
* b: Series.new([3.5, 4])
* });
* df.sum(); // [3, 7.5]
*
* const df2 = new DataFrame({
* a: Series.new(['foo', 'bar']),
* b: Series.new([3, 4])
* });
*
* df2.sum(); // returns `never`
* ```
*/
sum<P extends keyof T = keyof T>(subset?: readonly P[],
skipNulls = true,
memoryResource?: MemoryResource) {
subset = (subset == undefined) ? this.names as readonly P[] : subset;
const containsAllFloatingPoint =
subset.every((name) => FloatTypes.some((t) => compareTypes(t, this.types[name])));
const containsAllIntegral =
subset.every((name) => IntegralTypes.some((t) => compareTypes(t, this.types[name])));
if (!(containsAllFloatingPoint !== containsAllIntegral)) {
throw new TypeError(
`sum operation requires dataframe to be entirely of dtype FloatingPoint OR Integral.`);
}
const sums = subset.map((name) => (this.get(name) as any).sum(skipNulls, memoryResource));
return (containsAllIntegral ? Series.new({type: new Int64, data: sums}) : Series.new(sums)) as (
Series < T[P] extends Integral ? T[P] extends FloatingPoint ? never : Integral
: T[P] extends FloatingPoint ? FloatingPoint : never >);
}
/**
* Convert NaNs (if any) to nulls.
*
* @param subset List of float columns to consider to replace NaNs with nulls.
*
* @returns DataFrame<T> with NaNs(if any) converted to nulls
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, 2, 3, 4, 4]}),
* b: Series.new({type: new Float32, data: [0, NaN, 2, 3, 4, 4]})
* });
* df.get("b").nullCount; // 0
* const df1 = df.nansToNulls();
* df1.get("b").nullCount; // 1
*
* ```
*/
nansToNulls(subset?: (keyof T)[], memoryResource?: MemoryResource): DataFrame<T> {
subset = (subset == undefined) ? this.names as (keyof T)[] : subset;
const temp = new Table({columns: this.select(subset)._accessor.columns});
const series_map = {} as SeriesMap<T>;
this.names.forEach((name, index) => {
if (FloatTypes.some((t) => compareTypes(this.types[name], t))) {
series_map[name] =
this.__constructChild(name, temp.getColumnByIndex(index).nansToNulls(memoryResource));
} else {
series_map[name] = this.__constructChild(name, temp.getColumnByIndex(index));
}
});
return new DataFrame(series_map);
}
/**
* Creates a DataFrame replacing any FloatSeries with a Bool8Series where `true` indicates the
* value is `NaN` and `false` indicates the value is valid.
*
* @returns a DataFrame replacing instances of FloatSeries with a Bool8Series where `true`
* indicates the value is `NaN`
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, null]}),
* b: Series.new({type: new Float32, data: [0, NaN, 2]})
* });
*
* df.isNaN()
* // return {
* // a: [0, 1, null],
* // b: [false, true, false],
* // }
* ```
*/
isNaN(memoryResource?: MemoryResource): DataFrame<T> {
return new DataFrame(
this.names.reduce((map, name) => ({
...map,
[name]: FloatTypes.some((t) => compareTypes(this.types[name], t))
? Series.new(this._accessor.get(name).isNaN(memoryResource))
: this.get(name)
}),
{} as SeriesMap<T>));
}
/**
* Creates a DataFrame of `BOOL8` Series where `true` indicates the value is null and
* `false` indicates the value is valid.
*
* @returns a DataFrame containing Series of 'BOOL8' where 'true' indicates the value is null
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, null, 2]),
* b: Series.new(['foo', 'bar', null])
* });
*
* df.isNull()
* // return {
* // a: [false, true, false],
* // b: [false, false, true],
* // }
* ```
*/
isNull(memoryResource?: MemoryResource): DataFrame<{[P in keyof T]: Bool8}> {
return new DataFrame(
this.names.reduce((cols, name) => ({...cols, [name]: this.get(name).isNull(memoryResource)}),
{} as SeriesMap<{[P in keyof T]: Bool8}>));
}
/**
* Creates a DataFrame replacing any FloatSeries with a Bool8Series where `false` indicates the
* value is `NaN` and `true` indicates the value is valid.
*
* @returns a DataFrame replacing instances of FloatSeries with a Bool8Series where `false`
* indicates the value is `NaN`
*
* @example
* ```typescript
* import {DataFrame, Series, Int32, Float32} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new({type: new Int32, data: [0, 1, null]}),
* b: Series.new({type: new Float32, data: [0, NaN, 2]})
* });
*
* df.isNotNaN()
* // return {
* // a: [0, 1, null],
* // b: [true, false, true],
* // }
* ```
*/
isNotNaN(): DataFrame<T> {
return new DataFrame(
this.names.reduce((map, name) => ({
...map,
[name]: FloatTypes.some((t) => compareTypes(this.types[name], t))
? (this.get(name) as Series<FloatingPoint>).isNotNaN()
: this.get(name)
}),
{} as SeriesMap<T>));
}
/**
* Creates a DataFrame of `BOOL8` Series where `false` indicates the value is null and
* `true` indicates the value is valid.
*
* @returns a DataFrame containing Series of 'BOOL8' where 'false' indicates the value is null
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
* const df = new DataFrame({
* a: Series.new([0, null, 2]),
* b: Series.new(['foo', 'bar', null])
* });
*
* df.isNotNull()
* // return {
* // a: [true, false, true],
* // b: [true, true, false],
* // }
* ```
*/
isNotNull(): DataFrame<{[P in keyof T]: Bool8}> {
return new DataFrame(
this.names.reduce((cols, name) => ({...cols, [name]: this.get(name).isNotNull()}),
{} as SeriesMap<{[P in keyof T]: Bool8}>));
}
/**
* Replace null values with a value.
*
* @param value The scalar value to use in place of nulls.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([0, null, 2]);
* b: Series.new([null, null, null]);
* });
*
* df.replaceNulls(1);
* // return {
* // a: [0, 1, 2],
* // b: [1, 1, 1],
* // }
* ```
*/
replaceNulls<R extends DataType>(value: R['scalarType'],
memoryResource?: MemoryResource): DataFrame<T>;
/**
* Replace null values with the corresponding elements from another Map of Series.
*
* @param value The map of Series to use in place of nulls.
* @param memoryResource The optional MemoryResource used to allocate the result Column's device
* memory.
*
* @example
* ```typescript
* import {DataFrame, Series} from '@rapidsai/cudf';
*
* const df = new DataFrame({
* a: Series.new([0, null, 2]);
* b: Series.new([null, null, null]);
* });
*
* df.replaceNulls({'a': Series.new([0, 1, 2]), 'b': Series.new([1, 1, 1])});
* // return {
* // a: [0, 1, 2],
* // b: [1, 1, 1],
* // }
* ```
*/
replaceNulls(value: SeriesMap<T>, memoryResource?: MemoryResource): DataFrame<T>;
replaceNulls<R extends DataType>(value: SeriesMap<T>|R['scalarType'],
memoryResource?: MemoryResource): DataFrame<T> {
if (value instanceof Object) {
const columns = new DataFrame(_seriesToColumns(value));
return new DataFrame(this.names.reduce(
(map, name) => ({
...map,
[name]: columns.has(name) ? this.get(name).replaceNulls(columns.get(name), memoryResource)
: this.get(name)
}),
{} as SeriesMap<T>));
} else {
return new DataFrame(this.names.reduce(
(map, name) => ({...map, [name]: this.get(name).replaceNulls(value, memoryResource)}),
{} as SeriesMap<T>));
}
}
/**
* Drops duplicate rows from a DataFrame
*
* @param keep Determines whether to keep the first, last, or none of the duplicate items.
* @param nullsEqual Determines whether nulls are handled as equal values.
* @param nullsFirst Determines whether null values are inserted before or after non-null
* values.
* @param subset List of columns to consider when dropping rows (all columns are considered by
* default).
* @param memoryResource Memory resource used to allocate the result Column's device memory.
*
* @returns a DataFrame without duplicate rows
* ```
*/
dropDuplicates(keep: keyof typeof DuplicateKeepOption = 'any',
nullsEqual = true,
nullsFirst = true,
subset = this.names,
memoryResource?: MemoryResource) {
const column_indices: number[] = [];
const allNames = this.names;
subset.forEach((col) => {
if (this.has(col)) {
column_indices.push(allNames.indexOf(col));
} else {
throw new Error(`Unknown column name: ${col}`);
}
});
const table = scope(() => {
const spec = {ascending: true, null_order: nullsFirst ? 'before' : 'after'};
const by = subset.reduce((by, key) => Object.assign(by, {[key]: spec}),
{} as {[P in keyof T]: OrderSpec});
return this.sortValues(by).asTable().unique(
column_indices, DuplicateKeepOption[keep], nullsEqual, memoryResource);
}, [this]);
return new DataFrame(allNames.reduce(
(map, name, i) => ({...map, [name]: this.__constructChild(name, table.getColumnByIndex(i))}),
{} as SeriesMap<T>));
}
}
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