From f75e027ed7ea18e2eddecc2230523d9664ae48ea Mon Sep 17 00:00:00 2001 From: anna-charlotte Date: Thu, 10 Nov 2022 17:17:55 +0100 Subject: [PATCH 1/4] docs: add colab notebook for 3d data visualization From ca69b5772f5449cc27bc96d790977f458a50124f Mon Sep 17 00:00:00 2001 From: anna-charlotte Date: Thu, 10 Nov 2022 17:40:34 +0100 Subject: [PATCH 2/4] docs: add colab notebook link --- docs/datatypes/mesh/index.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/docs/datatypes/mesh/index.md b/docs/datatypes/mesh/index.md index 41289eb5f1f..4a11eafb913 100644 --- a/docs/datatypes/mesh/index.md +++ b/docs/datatypes/mesh/index.md @@ -7,7 +7,14 @@ This feature requires `trimesh`. You can install it via `pip install "docarray[f A 3D mesh is the structural build of a 3D model consisting of polygons. Most 3D meshes are created via professional software packages, such as commercial suites like Unity, or the free open source Blender 3D. -DocArray supports .obj, .glb and .ply files. +DocArray supports the following file formats for 3D data handling: .obj, .glb and .ply. + +You can explore interactive 3D data visualization with DocArray in the following Google Colab Notebook: +

+ +Open In Colab + +

## Vertices and faces representation From a443070b3c1c11c3eed1c887a7c9d7e785cd6c34 Mon Sep 17 00:00:00 2001 From: anna-charlotte Date: Fri, 11 Nov 2022 11:32:21 +0100 Subject: [PATCH 3/4] docs: add 3d visualization notebook --- .../mesh/docarray_3d_data_visualization.ipynb | 10658 ++++++++++++++++ 1 file changed, 10658 insertions(+) create mode 100644 docs/datatypes/mesh/docarray_3d_data_visualization.ipynb diff --git a/docs/datatypes/mesh/docarray_3d_data_visualization.ipynb b/docs/datatypes/mesh/docarray_3d_data_visualization.ipynb new file mode 100644 index 00000000000..34276fff241 --- /dev/null +++ b/docs/datatypes/mesh/docarray_3d_data_visualization.ipynb @@ -0,0 +1,10658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "LSPs1SsSZo-9" + }, + "source": [ + "# Interactive visualization of 3D data with DocArray\n", + "\n", + "## Supported representations\n", + "\n", + "DocArray supports the following representations for 3D data:\n", + "- Point cloud representation\n", + "- Vertices and faces representation\n", + "\n", + "In this notebook we want to demontrate how to visualize 3D data with some toydata examples that we prepared and stored for you in DocumentArrays in Jina Cloud." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VwAlK2NAadXk" + }, + "source": [ + "# Install requirements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YvlWf4QCafjn", + "outputId": "600cb063-f960-4d25-88f2-10992d45f723" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting git+https://github.com/docarray/docarray\n", + " Cloning https://github.com/docarray/docarray to /tmp/pip-req-build-9b42qxfb\n", + " Running command git clone -q https://github.com/docarray/docarray 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"Y8uO3UfFzm0-" + }, + "source": [ + "# Login to Jina Cloud\n", + "\n", + "You need to login to Jina Cloud to access the 3D data stored in DocumentArrays which will be used in the course of this notebook. After logging in you will be able to pull those DocumentArrays from Jina Cloud and explore the stored data.\n", + "\n", + "If you don't have an account yet, you can [sign up here](https://jina-ai.us.auth0.com/u/signup?state=hKFo2SBVZEtMQVZVSlhXcXR2eVFOWTN2QnR6UmRhb19qUUd5WKFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIF9VZFFRQjJnT24wdkQ1aFU2YkRxd2FBWm5XcHAtVXNio2NpZNkgN3BYQVVBdGlScXJ1TmQ2S0o2VTNaZDl1aGs1b0xxWkE)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 203, + "referenced_widgets": [ + "8d89bf74920b4c91aa9d11631644061a", + "8c8c2507537a444bbe146a7d30d33789", + "b80ca6e400f343899639664af812194c", + "e07ea8692e664a7c9d2c403d037a113b", + "772f7a2315c7436483f137462c8020d8", + "1e5b7ac568884ed28b066345fc8b61c2", + "70b878bfa1764752b53b0b40658291dc", + "9797b89c6b3e4953ae58493a2053c462", + "f84a332b98d844c1b83d718bc485507c", + "fcca29238ace439f9cfbd8300df35ad1", + "5a87eb45543f4a6081354ee4e9f53a5b", + "9d3eeab5df8249a4a2286304651072a4", + "50ca8e7dfb4a498e8c8e81a73ccfee89", + "d5bf5eda0e5e4439902c16edf792085f", + "162a21b06e5741c0b8ff7f1c00811b2f", + "764743c1825e448eb1363a289a35a199", + "1ada44dd36694f9ebb6515790e53de22", + "a1fdad64eb184ec5ab9f1bcd06e164b5", + "e2c5e8eaa8e8417bb0fd83b3a54c6d5c", + "0279bc1378634bdba2e9ac4ae4448a15", + "8c33740ef70c45f899c00423951ac5f0", + "35b9109d22ae498b9c07961af57aa41a", + "ef309571b957429f8b441f37939cbb8d", + "51fd73032f654e34bae494bc87540d3f", + "8b8a38e1ba74439194cd9686a9ac6a2c", + "d9c24009135648cd866928dd1efd16af", + "0bf9b564b9f549c98bb78d7e81f663fa", + "4ea4a4e1f85b4eb59b422337b6f5e289", + "26f575198ecc41f9b1a1155015c65efb", + "297c88e32e7c47568746a85f1e94e338", + "1a263d8a3a2d483194b1b8379bd3e310" + ] + }, + "id": "svA3onmsgwKI", + "outputId": "91603d8b-818e-4b43-d12e-4ebd4264ab80" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(VBox(children=(HTML(value=\"\\n
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" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "📄 \u001b[1mDocument\u001b[0m: \u001b[36m5d809968e3f053840eb9a5eee8184aab\u001b[0m\n", + "╭────────────┬─────────────────────────────────────────────────────────────────╮\n", + "│\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + "├────────────┼─────────────────────────────────────────────────────────────────┤\n", + "│ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m30000\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: float64 │\n", + "│ mime_type │ application/x-tgif │\n", + "╰────────────┴─────────────────────────────────────────────────────────────────╯\n" + ], + "text/html": [ + "
📄 Document: 5d809968e3f053840eb9a5eee8184aab\n",
+              "╭────────────┬─────────────────────────────────────────────────────────────────╮\n",
+              "│ Attribute  │ Value                                                           │\n",
+              "├────────────┼─────────────────────────────────────────────────────────────────┤\n",
+              "│ tensor     │ <class 'numpy.ndarray'> in shape (30000, 3), dtype: float64     │\n",
+              "│ mime_type  │ application/x-tgif                                              │\n",
+              "╰────────────┴─────────────────────────────────────────────────────────────────╯\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "from IPython.display import Javascript\n", + "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})'''))\n", + "\n", + "for doc in da:\n", + " doc.summary()\n", + " doc.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ps5r-JCi4RR1" + }, + "source": [ + "# Point clouds of different densities\n", + "\n", + "In another DocumentArray we prepared some point cloud tensors of different sizes ranging from 2000 sampled datapoint up to 60000. For each Document we additionally stored the sample size value in the corresponding `.tags` dictionary. In the following you can choose the 3d object and the number of sampled points and display the corresponding point cloud." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "nvWJ0p4lkv8b", + "outputId": "49309d05-2e5a-4623-cd42-87a3cf99f524", + "cellView": "form" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\n" + ], + "text/html": [ + "
\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "from docarray import Document, DocumentArray\n", + "\n", + "sample_size = 100000 #@param [\"1000\", \"5000\", \"10000\", \"20000\", \"40000\", \"60000\", \"100000\"] {type:\"raw\"}\n", + "object_3d = \"mesh_man\" #@param [\"mesh_man\", \"flower\", \"skyscraper\"]\n", + "\n", + "da = DocumentArray().pull(f'da_point_cloud_different_densities_{object_3d}')\n", + "\n", + "for doc in da:\n", + " if doc.tags['sample size'] == sample_size:\n", + " doc.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-i7BelCPac38" + }, + "source": [ + "# Vertices and faces representation\n", + "\n", + "A 3D mesh can be represented by its vertices and faces. \n", + "Vertices are points in a 3D space, represented as a tensor of shape (n_points, 3). \n", + "Faces are triangular surfaces that can be defined by three points in 3D space, corresponding to the three vertices of a triangle. Faces can be represented as a tensor of shape (n_faces, 3). Each number in that tensor refers to an index of a vertex in the tensor of vertices.\n", + "\n", + "In DocArray, the vertices and faces of a mesh can be loaded and saved to a Document's `.chunks` by calling `.load_uri_to_vertices_and_faces()` on a Document instance.\n", + "\n", + "Again, we prepared a DocumentArray for you which we can now pull from Jina Cloud and interactivley visualize:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + }, + "id": "PyP3dR0eGkND", + "outputId": "24ed8d4d-1a01-4aa2-fd14-c879c81cd663" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\n" + ], + "text/html": [ + "
\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "╭───────────────────── Documents Summary ─────────────────────╮\n", + "│ │\n", + "│ Type DocumentArrayInMemory │\n", + "│ Length \u001b[1;36m3\u001b[0m │\n", + "│ Homogenous Documents \u001b[3;92mTrue\u001b[0m │\n", + "│ Has nested Documents in \u001b[1m(\u001b[0m\u001b[32m'chunks'\u001b[0m,\u001b[1m)\u001b[0m │\n", + "│ Common Attributes \u001b[1m(\u001b[0m\u001b[32m'id'\u001b[0m, \u001b[32m'mime_type'\u001b[0m, \u001b[32m'chunks'\u001b[0m\u001b[1m)\u001b[0m │\n", + "│ Multimodal dataclass \u001b[3;91mFalse\u001b[0m │\n", + "│ │\n", + "╰─────────────────────────────────────────────────────────────╯\n", + "╭──────────────────────── Attributes Summary ────────────────────────╮\n", + "│ │\n", + "│ \u001b[1m \u001b[0m\u001b[1mAttribute\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mData type \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m#Unique values\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mHas empty value\u001b[0m\u001b[1m \u001b[0m │\n", + "│ ──────────────────────────────────────────────────────────────── │\n", + "│ chunks \u001b[1m(\u001b[0m\u001b[32m'ChunkArray'\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m3\u001b[0m \u001b[3;91mFalse\u001b[0m │\n", + "│ id \u001b[1m(\u001b[0m\u001b[32m'str'\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m3\u001b[0m \u001b[3;91mFalse\u001b[0m │\n", + "│ mime_type \u001b[1m(\u001b[0m\u001b[32m'str'\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m1\u001b[0m \u001b[3;91mFalse\u001b[0m │\n", + "│ │\n", + "╰────────────────────────────────────────────────────────────────────╯\n" + ], + "text/html": [ + "
╭───────────────────── Documents Summary ─────────────────────╮\n",
+              "│                                                             │\n",
+              "│   Type                      DocumentArrayInMemory           │\n",
+              "│   Length                    3                               │\n",
+              "│   Homogenous Documents      True                            │\n",
+              "│   Has nested Documents in   ('chunks',)                     │\n",
+              "│   Common Attributes         ('id', 'mime_type', 'chunks')   │\n",
+              "│   Multimodal dataclass      False                           │\n",
+              "│                                                             │\n",
+              "╰─────────────────────────────────────────────────────────────╯\n",
+              "╭──────────────────────── Attributes Summary ────────────────────────╮\n",
+              "│                                                                    │\n",
+              "│   Attribute   Data type         #Unique values   Has empty value   │\n",
+              "│  ────────────────────────────────────────────────────────────────  │\n",
+              "│   chunks      ('ChunkArray',)   3                False             │\n",
+              "│   id          ('str',)          3                False             │\n",
+              "│   mime_type   ('str',)          1                False             │\n",
+              "│                                                                    │\n",
+              "╰────────────────────────────────────────────────────────────────────╯\n",
+              "
\n" + ] + }, + "metadata": {} + } + ], + "source": [ + "from docarray import Document, DocumentArray\n", + "\n", + "da = DocumentArray().pull('da_vertices_and_faces_without_uri')\n", + "da.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2790 + }, + "id": "yek-ihxpHu1J", + "outputId": "60b8cba7-0509-46b9-ca79-a94eeda45409" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "📄 \u001b[1mDocument\u001b[0m: \u001b[36m610b2fe1a6cd406794595152edf5eab2\u001b[0m\n", + "╭────────────────────────────┬─────────────────────────────────────────────────╮\n", + "│\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + "├────────────────────────────┼─────────────────────────────────────────────────┤\n", + "│ mime_type │ application/x-tgif │\n", + "╰────────────────────────────┴─────────────────────────────────────────────────╯\n", + "└── 💠 \u001b[1mChunks\u001b[0m\n", + " ├── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m04ea65faeb6f50190b48887f7a32824d\u001b[0m\n", + " │ ╭──────────────┬───────────────────────────────────────────────────────────────╮\n", + " │ │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + " │ ├──────────────┼───────────────────────────────────────────────────────────────┤\n", + " │ │ parent_id │ 610b2fe1a6cd406794595152edf5eab2 │\n", + " │ │ granularity │ 1 │\n", + " │ │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m3980\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: float64 │\n", + " │ │ tags │ {'name': 'vertices'} │\n", + " │ ╰──────────────┴───────────────────────────────────────────────────────────────╯\n", + " └── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m5082a63c5bd50c899248cf7f3398d782\u001b[0m\n", + " ╭───────────────┬──────────────────────────────────────────────────────────────╮\n", + " │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + " ├───────────────┼──────────────────────────────────────────────────────────────┤\n", + " │ parent_id │ 610b2fe1a6cd406794595152edf5eab2 │\n", + " │ granularity │ 1 │\n", + " │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m7152\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: int64 │\n", + " │ tags │ {'name': 'faces'} │\n", + " ╰───────────────┴──────────────────────────────────────────────────────────────╯\n" + ], + "text/html": [ + "
📄 Document: 610b2fe1a6cd406794595152edf5eab2\n",
+              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
+              "│ Attribute                  │ Value                                           │\n",
+              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
+              "│ mime_type                  │ application/x-tgif                              │\n",
+              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
+              "└── 💠 Chunks\n",
+              "    ├── 📄 Document: 04ea65faeb6f50190b48887f7a32824d\n",
+              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
+              "    │   │ Attribute    │ Value                                                         │\n",
+              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
+              "    │   │ parent_id    │ 610b2fe1a6cd406794595152edf5eab2                              │\n",
+              "    │   │ granularity  │ 1                                                             │\n",
+              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (3980, 3), dtype: float64    │\n",
+              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
+              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
+              "    └── 📄 Document: 5082a63c5bd50c899248cf7f3398d782\n",
+              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
+              "        │ Attribute     │ Value                                                        │\n",
+              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
+              "        │ parent_id     │ 610b2fe1a6cd406794595152edf5eab2                             │\n",
+              "        │ granularity   │ 1                                                            │\n",
+              "        │ tensor        │ <class 'numpy.ndarray'> in shape (7152, 3), dtype: int64     │\n",
+              "        │ tags          │ {'name': 'faces'}                                            │\n",
+              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "📄 \u001b[1mDocument\u001b[0m: \u001b[36m47b3938e596c1b743ea059aa7138f276\u001b[0m\n", + "╭────────────────────────────┬─────────────────────────────────────────────────╮\n", + "│\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + "├────────────────────────────┼─────────────────────────────────────────────────┤\n", + "│ mime_type │ application/x-tgif │\n", + "╰────────────────────────────┴─────────────────────────────────────────────────╯\n", + "└── 💠 \u001b[1mChunks\u001b[0m\n", + " ├── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m21f603a21abb880383fe945a642e2e2f\u001b[0m\n", + " │ ╭──────────────┬───────────────────────────────────────────────────────────────╮\n", + " │ │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + " │ ├──────────────┼───────────────────────────────────────────────────────────────┤\n", + " │ │ parent_id │ 47b3938e596c1b743ea059aa7138f276 │\n", + " │ │ granularity │ 1 │\n", + " │ │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m806\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: float64 │\n", + " │ │ tags │ {'name': 'vertices'} │\n", + " │ ╰──────────────┴───────────────────────────────────────────────────────────────╯\n", + " └── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m0b696ea4c0607ed76ac422a03204eba0\u001b[0m\n", + " ╭───────────────┬──────────────────────────────────────────────────────────────╮\n", + " │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", + " ├───────────────┼──────────────────────────────────────────────────────────────┤\n", + " │ parent_id │ 47b3938e596c1b743ea059aa7138f276 │\n", + " │ granularity │ 1 │\n", + " │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m1372\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: int64 │\n", + " │ tags │ {'name': 'faces'} │\n", + " ╰───────────────┴──────────────────────────────────────────────────────────────╯\n" + ], + "text/html": [ + "
📄 Document: 47b3938e596c1b743ea059aa7138f276\n",
+              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
+              "│ Attribute                  │ Value                                           │\n",
+              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
+              "│ mime_type                  │ application/x-tgif                              │\n",
+              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
+              "└── 💠 Chunks\n",
+              "    ├── 📄 Document: 21f603a21abb880383fe945a642e2e2f\n",
+              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
+              "    │   │ Attribute    │ Value                                                         │\n",
+              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
+              "    │   │ parent_id    │ 47b3938e596c1b743ea059aa7138f276                              │\n",
+              "    │   │ granularity  │ 1                                                             │\n",
+              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (806, 3), dtype: float64     │\n",
+              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
+              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
+              "    └── 📄 Document: 0b696ea4c0607ed76ac422a03204eba0\n",
+              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
+              "        │ Attribute     │ Value                                                        │\n",
+              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
+              "        │ parent_id     │ 47b3938e596c1b743ea059aa7138f276                             │\n",
+              "        │ granularity   │ 1                                                            │\n",
+              "        │ tensor        │ <class 'numpy.ndarray'> in shape (1372, 3), dtype: int64     │\n",
+              "        │ tags          │ {'name': 'faces'}                                            │\n",
+              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
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+              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
+              "│ Attribute                  │ Value                                           │\n",
+              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
+              "│ mime_type                  │ application/x-tgif                              │\n",
+              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
+              "└── 💠 Chunks\n",
+              "    ├── 📄 Document: 798f4a62718e2ef584e37f420ea9716f\n",
+              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
+              "    │   │ Attribute    │ Value                                                         │\n",
+              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
+              "    │   │ parent_id    │ 9c9c8f49829e59763f773e89c71428e5                              │\n",
+              "    │   │ granularity  │ 1                                                             │\n",
+              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (3020, 3), dtype: float64    │\n",
+              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
+              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
+              "    └── 📄 Document: 87ddf9d16145c2a8b650e0a4ad2490d9\n",
+              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
+              "        │ Attribute     │ Value                                                        │\n",
+              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
+              "        │ parent_id     │ 9c9c8f49829e59763f773e89c71428e5                             │\n",
+              "        │ granularity   │ 1                                                            │\n",
+              "        │ tensor        │ <class 'numpy.ndarray'> in shape (3692, 3), dtype: int64     │\n",
+              "        │ tags          │ {'name': 'faces'}                                            │\n",
+              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + } + ], + "source": [ + "from IPython.display import Javascript\n", + "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})'''))\n", + "\n", + "for doc in da:\n", + " doc.summary()\n", + " doc.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TIJnyFVqHz-_" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "8d89bf74920b4c91aa9d11631644061a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, 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\n \n
\n Jina AI\n
\n

\n Copy a Personal Access Token, paste it below, and press the Token login button.\n
\n If you don't have a token, press the Browser login button to log in via the browser.\n

\n \n Create\n \n
\n
\n" + } + }, + "9797b89c6b3e4953ae58493a2053c462": { + "model_module": "@jupyter-widgets/controls", + "model_name": "PasswordModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "PasswordModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "PasswordView", + "continuous_update": true, + "description": "", + "description_tooltip": null, + "disabled": true, + "layout": "IPY_MODEL_0279bc1378634bdba2e9ac4ae4448a15", + "placeholder": "Personal Access Token (PAT)", + "style": "IPY_MODEL_8c33740ef70c45f899c00423951ac5f0", + "value": "" + } + }, + "f84a332b98d844c1b83d718bc485507c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ButtonModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [ + "button1" + ], + "_model_module": "@jupyter-widgets/controls", + 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\n \n
\n Jina AI\n
\n

\n You are logged in to Jina AI!\n

\n

\n If you want to log in again, run notebook_login(force=True).\n

\n
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" - ] - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "from IPython.display import Javascript\n", "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})'''))\n", @@ -4243,1294 +125,16 @@ "source": [ "# Point clouds of different densities\n", "\n", - "In another DocumentArray we prepared some point cloud tensors of different sizes ranging from 2000 sampled datapoint up to 60000. For each Document we additionally stored the sample size value in the corresponding `.tags` dictionary. In the following you can choose the 3d object and the number of sampled points and display the corresponding point cloud." + "In another DocumentArray we prepared some point cloud tensors of different sizes ranging from 2000 sampled datapoints up to 60000. For each Document we additionally stored the sample size value in the corresponding `.tags` dictionary. In the following you can choose the 3d object and the number of sampled points and display the corresponding point cloud." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 521 - }, - "id": "nvWJ0p4lkv8b", - "outputId": "49309d05-2e5a-4623-cd42-87a3cf99f524", - "cellView": "form" + "id": "nvWJ0p4lkv8b" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\n" - ], - "text/html": [ - "
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" - ] - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "from docarray import Document, DocumentArray\n", "\n", @@ -5565,77 +169,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 331 - }, - "id": "PyP3dR0eGkND", - "outputId": "24ed8d4d-1a01-4aa2-fd14-c879c81cd663" + "id": "PyP3dR0eGkND" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "\n" - ], - "text/html": [ - "
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\n" - ] - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "from docarray import Document, DocumentArray\n", "\n", @@ -5647,3984 +183,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 2790 - }, - "id": "yek-ihxpHu1J", - "outputId": "60b8cba7-0509-46b9-ca79-a94eeda45409" + "id": "yek-ihxpHu1J" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "application/javascript": [ - "google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "📄 \u001b[1mDocument\u001b[0m: \u001b[36m610b2fe1a6cd406794595152edf5eab2\u001b[0m\n", - "╭────────────────────────────┬─────────────────────────────────────────────────╮\n", - "│\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", - "├────────────────────────────┼─────────────────────────────────────────────────┤\n", - "│ mime_type │ application/x-tgif │\n", - "╰────────────────────────────┴─────────────────────────────────────────────────╯\n", - "└── 💠 \u001b[1mChunks\u001b[0m\n", - " ├── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m04ea65faeb6f50190b48887f7a32824d\u001b[0m\n", - " │ ╭──────────────┬───────────────────────────────────────────────────────────────╮\n", - " │ │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", - " │ ├──────────────┼───────────────────────────────────────────────────────────────┤\n", - " │ │ parent_id │ 610b2fe1a6cd406794595152edf5eab2 │\n", - " │ │ granularity │ 1 │\n", - " │ │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m3980\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: float64 │\n", - " │ │ tags │ {'name': 'vertices'} │\n", - " │ ╰──────────────┴───────────────────────────────────────────────────────────────╯\n", - " └── 📄 \u001b[1mDocument\u001b[0m: \u001b[36m5082a63c5bd50c899248cf7f3398d782\u001b[0m\n", - " ╭───────────────┬──────────────────────────────────────────────────────────────╮\n", - " │\u001b[1m \u001b[0m\u001b[1mAttribute \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mValue \u001b[0m\u001b[1m \u001b[0m│\n", - " ├───────────────┼──────────────────────────────────────────────────────────────┤\n", - " │ parent_id │ 610b2fe1a6cd406794595152edf5eab2 │\n", - " │ granularity │ 1 │\n", - " │ tensor │ \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1m>\u001b[0m in shape \u001b[1m(\u001b[0m\u001b[1;36m7152\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, dtype: int64 │\n", - " │ tags │ {'name': 'faces'} │\n", - " ╰───────────────┴──────────────────────────────────────────────────────────────╯\n" - ], - "text/html": [ - "
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-              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
-              "│ Attribute                  │ Value                                           │\n",
-              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
-              "│ mime_type                  │ application/x-tgif                              │\n",
-              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
-              "└── 💠 Chunks\n",
-              "    ├── 📄 Document: 04ea65faeb6f50190b48887f7a32824d\n",
-              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
-              "    │   │ Attribute    │ Value                                                         │\n",
-              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
-              "    │   │ parent_id    │ 610b2fe1a6cd406794595152edf5eab2                              │\n",
-              "    │   │ granularity  │ 1                                                             │\n",
-              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (3980, 3), dtype: float64    │\n",
-              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
-              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
-              "    └── 📄 Document: 5082a63c5bd50c899248cf7f3398d782\n",
-              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
-              "        │ Attribute     │ Value                                                        │\n",
-              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
-              "        │ parent_id     │ 610b2fe1a6cd406794595152edf5eab2                             │\n",
-              "        │ granularity   │ 1                                                            │\n",
-              "        │ tensor        │ <class 'numpy.ndarray'> in shape (7152, 3), dtype: int64     │\n",
-              "        │ tags          │ {'name': 'faces'}                                            │\n",
-              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
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📄 Document: 47b3938e596c1b743ea059aa7138f276\n",
-              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
-              "│ Attribute                  │ Value                                           │\n",
-              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
-              "│ mime_type                  │ application/x-tgif                              │\n",
-              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
-              "└── 💠 Chunks\n",
-              "    ├── 📄 Document: 21f603a21abb880383fe945a642e2e2f\n",
-              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
-              "    │   │ Attribute    │ Value                                                         │\n",
-              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
-              "    │   │ parent_id    │ 47b3938e596c1b743ea059aa7138f276                              │\n",
-              "    │   │ granularity  │ 1                                                             │\n",
-              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (806, 3), dtype: float64     │\n",
-              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
-              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
-              "    └── 📄 Document: 0b696ea4c0607ed76ac422a03204eba0\n",
-              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
-              "        │ Attribute     │ Value                                                        │\n",
-              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
-              "        │ parent_id     │ 47b3938e596c1b743ea059aa7138f276                             │\n",
-              "        │ granularity   │ 1                                                            │\n",
-              "        │ tensor        │ <class 'numpy.ndarray'> in shape (1372, 3), dtype: int64     │\n",
-              "        │ tags          │ {'name': 'faces'}                                            │\n",
-              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
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📄 Document: 9c9c8f49829e59763f773e89c71428e5\n",
-              "╭────────────────────────────┬─────────────────────────────────────────────────╮\n",
-              "│ Attribute                  │ Value                                           │\n",
-              "├────────────────────────────┼─────────────────────────────────────────────────┤\n",
-              "│ mime_type                  │ application/x-tgif                              │\n",
-              "╰────────────────────────────┴─────────────────────────────────────────────────╯\n",
-              "└── 💠 Chunks\n",
-              "    ├── 📄 Document: 798f4a62718e2ef584e37f420ea9716f\n",
-              "    │   ╭──────────────┬───────────────────────────────────────────────────────────────╮\n",
-              "    │   │ Attribute    │ Value                                                         │\n",
-              "    │   ├──────────────┼───────────────────────────────────────────────────────────────┤\n",
-              "    │   │ parent_id    │ 9c9c8f49829e59763f773e89c71428e5                              │\n",
-              "    │   │ granularity  │ 1                                                             │\n",
-              "    │   │ tensor       │ <class 'numpy.ndarray'> in shape (3020, 3), dtype: float64    │\n",
-              "    │   │ tags         │ {'name': 'vertices'}                                          │\n",
-              "    │   ╰──────────────┴───────────────────────────────────────────────────────────────╯\n",
-              "    └── 📄 Document: 87ddf9d16145c2a8b650e0a4ad2490d9\n",
-              "        ╭───────────────┬──────────────────────────────────────────────────────────────╮\n",
-              "        │ Attribute     │ Value                                                        │\n",
-              "        ├───────────────┼──────────────────────────────────────────────────────────────┤\n",
-              "        │ parent_id     │ 9c9c8f49829e59763f773e89c71428e5                             │\n",
-              "        │ granularity   │ 1                                                            │\n",
-              "        │ tensor        │ <class 'numpy.ndarray'> in shape (3692, 3), dtype: int64     │\n",
-              "        │ tags          │ {'name': 'faces'}                                            │\n",
-              "        ╰───────────────┴──────────────────────────────────────────────────────────────╯\n",
-              "
\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
" - ] - }, - "metadata": {} - } - ], + "outputs": [], "source": [ "from IPython.display import Javascript\n", "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 5000})'''))\n", @@ -9655,1002 +216,6 @@ }, "language_info": { "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "8d89bf74920b4c91aa9d11631644061a": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8c8c2507537a444bbe146a7d30d33789", - "IPY_MODEL_b80ca6e400f343899639664af812194c", - "IPY_MODEL_e07ea8692e664a7c9d2c403d037a113b", - "IPY_MODEL_772f7a2315c7436483f137462c8020d8" - ], - "layout": "IPY_MODEL_1e5b7ac568884ed28b066345fc8b61c2" - } - }, - "8c8c2507537a444bbe146a7d30d33789": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_70b878bfa1764752b53b0b40658291dc", - "IPY_MODEL_9797b89c6b3e4953ae58493a2053c462", - "IPY_MODEL_f84a332b98d844c1b83d718bc485507c", - "IPY_MODEL_fcca29238ace439f9cfbd8300df35ad1" - ], - "layout": "IPY_MODEL_5a87eb45543f4a6081354ee4e9f53a5b" - } - }, - "b80ca6e400f343899639664af812194c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - 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