scverse / scverse/spatialdata-plot

Vega-like viewconfigurations

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Python
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86
Forks
21
Merge moyen
14 h 50 min
PR mergées (30 j)
3

Description

During the hackathon in Basel we have brainstormed on the specification that would allow for viewing data across the spatialdata visualization ecosystem with one viewconfig. This issue describes the current ideas of this viewconfiguration.

Data

The first field in the view configuration is related to the data and specifies the zarr store and the particular elements that we want to visualize. It also includes a filtering step. For example:

"schema": "https://spatialdata-plot.github.io/schema/viewconfig/v1.json"
"height": 15 # This is in inches
"width": 14
"data": [
        {
            "name": "{UUID1}",
            "url": "blobs.zarr"
            "format": "spatialdata",
            "version": "0.2.0"
        },
        {
          "name": "{UUID2}",
          "format": "spatialdata_image",
          "version": "0.1.0",
          "source": "UUID1",
          "transform": [
            {
              "type": "filter",
              "expr": "datum['images/blobs_image']"
            }
          ]
        },
  ]      

Here the first block contains the location of the zarr store. We specify the particular format of the zarr store and the version. The name is serving as a UUID in the document so that other blocks in the configuration can refer to a particular block. Typically, on the data a transform is applied. In most cases for SpatialData this would be a filter transform initially to get the particular element that we require. The height and width is the overall size in inches of the plot.

Scales

Scales provide a mapping of a series of values to a different series of values, whether that is axes limits and ticks
or a mapping of values to color.

"scales": [
        { <!-- Example of scale for continuous variable-->
          "name": "color_0", <!-- can chain render_... calls that would use different normalize objects-->
          "type": "linear",
          "zero": true, <!-- Whether to include 0 value in the color mapping, useful for labels-->
          "domain": [0, 1],  <!-- this is the vmin vmax-->
          "clamp": true,  <!-- This is the clip of the normalize object in matplotlib-->
          "range": {"scheme": "gray"}
        },
        { <!-- Example of scale for categorical variable-->
      "name": "color",
      "type": "ordinal",
      "domain": {"data": "UUID of spatial element", "field": "category"}, <!-- category here is column-->
      "range": {"scheme": "category20"} <!--This is only required if there are no hex strings specified before, in some cases a user can already have the hexstrings representing color. In this case the color encoding in marks must be used.-->
        },
        {
              "name": "X_SCALE",
              "type": "linear",
              "zero": true,
              "domain": [-180, 180], <!-- This is the extent of the axes after having applied the transform to coordinate system-->
              "range": "width" <!-- refers to plot width-->
            },
            {
              "name": "Y_SCALE",
              "type": "linear",
              "zero": true,
              "domain": [-81, 87],
              "range": "height" <!-- refers to plot height-->
            }
    ],

Marks

Marks define the actual plots of the particular elements.

"marks": [
        {
          "type": "raster", <!-- type of element -->
          "from": {"data": "UUID3"},
          "zindex": 0,
          "encode": {
              <!-- "channel": {"value": 0}, // with "data": "UUID2" -->
              "opacity": {"value": 1}, <!-- sdata-plot "alpha". Also, it was fillOpacity -->
              "color": {"scale": "normalize", "field": "channel_zero_name"}
              <!--"zindex": {"field": "point_importance"}-->
          }
        },
        {
          "type": "shape", <!-- type of element -->
          "from": {"data": "UUID5"},
          "zindex": 1,
          "encode": {
              <!-- "channel": {"value": 0},
              "opacity": {"value": 1}, <!-- sdata-plot "alpha". Also, it was fillOpacity -->
              "color": {hexstrings} <!-- in case of predetermined hexstrings --> 
              <!--"zindex": {"field": "point_importance"}-->
          }
        }
      ],

Guide de contribution

Ouvrir le guide de contribution

Par où commencer

  1. Lisez l'issue en entier, puis le guide de contribution du projet.
  2. Signalez en commentaire que vous la prenez — cela évite que deux personnes fassent le même travail.
  3. Forkez le dépôt et travaillez sur une branche.
  4. Ouvrez une pull request qui référence le numéro de l'issue.

Piste de recherche

Commencez par examiner les sections proposées de configuration de vue pour les données, les échelles et les marques, y compris leurs références à SpatialData et leurs transformations. Le travail est considéré comme terminé lorsque la spécification de configuration de vue a été résolue et approuvée dans l’ensemble de l’écosystème de visualisation spatialdata ; l’issue ne nomme aucun fichier d’implémentation ni aucun test.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Évaluation

Stack technique
python
Domaine
data-visualization
Type d'issue
Fonctionnalité
Difficulté
5/5
Temps estimé
Plus d'une semaine
Activité
À l'abandon
Clarté
À clarifier
Accessibilité débutants
20/100

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