Zarr Sink Frame Values

docs/getting_started.rst

@@ -334,25 +334,60 @@ You can also composite a multi-frame image into a false-color output:
 Writing an Image
 ----------------
 
-If you wish to visualize numpy data, large_image can write a tiled tiff.  This requires a tile source that supports writing to be installed.  As of this writing, the ``large-image-source-zarr`` and ``large-image-source-vips`` sources supports this.  If both are installed, the ``large-image-source-zarr`` is the default.
+If you wish to visualize numpy data, ``large_image`` can write a tiled image.
+This requires a tile source that supports writing to be installed.
+As of this writing, the ``large-image-source-zarr`` and ``large-image-source-vips`` sources both support this.
+If both are installed, the ``large-image-source-zarr`` is the default.
+Some of the API options available for ``large-image-source-zarr`` are not available for ``large-image-source-vips``.
 
 .. code-block:: python
 
     import large_image
+
     source = large_image.new()
     for nparray, x, y in fancy_algorithm():
         # We could optionally add a mask to limit the output
         source.addTile(nparray, x, y)
     source.write('/tmp/sample.tiff', lossy=False)
 
-The ``large-image-source-zarr`` can be used to store multiple frame data with arbitrary axes.
+Multiple Frames
+~~~~~~~~~~~~~~~
+
+``large-image-source-zarr`` can be used to store multiframe data with arbitrary axes.
+The example below demonstrates the creation of an image with five axes: T, Z, Y, X, S.
 
 .. code-block:: python
 
     import large_image
+
+    time_values = [0.5, 1.5, 2.5, 3.5]
+    z_values = [3, 6, 9]
+    tile_pos_values = [0, 1024, 2048, 3072, 4096]
+
     source = large_image.new()
-    for nparray, x, y, time, param1 in fancy_algorithm():
-        source.addTile(nparray, x, y, time=time, p1=param1)
+    for t_index, t_value in enumerate(time_values):
+        for z_index, z_value in enumerate(z_values):
+            for y_value in tile_pos_values:
+                for x_value in tile_pos_values:
+
+                    # tile is a numpy array with shape (1024, 1024, 3)
+                    # this shape corresponds to the following axes, respectively: (Y, X, S)
+                    tile = get_my_data_tile(x_value, y_value, z_value, t_value)
+
+                    source.addTile(
+                        tile,
+                        x_value,
+                        y_value,
+                        z=z_index,
+                        time=t_index,
+
+                        # z_value and t_value are optional parameters to store the
+                        # true values at the provided z index and t index
+                        z_value=z_value,
+                        time_value=t_value,
+                    )
+    source.frameUnits = dict(t='ms', z='cm')
+
     # The writer supports a variety of formats
     source.write('/tmp/sample.zarr.zip', lossy=False)
 
@@ -361,31 +396,37 @@ You may also choose to read tiles from one source and write modified tiles to a
 .. code-block:: python
 
     import large_image
+
     original_source = large_image.open('path/to/original/image.tiff')
     new_source = large_image.new()
     for frame in original_source.getMetadata().get('frames', []):
         for tile in original_source.tileIterator(frame=frame['Frame'], format='numpy'):
-            t, x, y = tile['tile'], tile['x'], tile['y']
+            tile_data, x, y = tile['tile'], tile['x'], tile['y']
             kwargs = {
                 'z': frame['IndexZ'],
                 'c': frame['IndexC'],
             }
-            modified_tile = modify_tile(t)
+            modified_tile = modify_tile(tile_data)
             new_source.addTile(modified_tile, x=x, y=y, **kwargs)
     new_source.write('path/to/new/image.tiff', lossy=False)
 
+Multiple processes
+~~~~~~~~~~~~~~~~~~
+
 In some cases, it may be beneficial to write to a single image from multiple processes or threads:
 
 .. code-block:: python
 
     import large_image
     import multiprocessing
+
     # Important: Must be a pickleable function
     def add_tile_to_source(tilesource, nparray, position):
         tilesource.addTile(
             nparray,
             **position
         )
+
     source = large_image.new()
     # Important: Maximum size must be allocated before any multiprocess concurrency
     add_tile_to_source(source, np.zeros(1, 1, 3), dict(x=max_x, y=max_y, z=max_z))
@@ -397,4 +438,10 @@ In some cases, it may be beneficial to write to a single image from multiple pro
         )
     source.write('/tmp/sample.zarr.zip', lossy=False)
 
+
+More examples
+~~~~~~~~~~~~~
+
+To see more examples of using ``large-image-source-zarr`` to write images, see :doc:`notebooks` and the `Zarr Sink Tests <https://github.com/girder/large_image/blob/master/test/test_sink.py>`_.
+
 .. _Girder: https://girder.readthedocs.io/en/latest/

docs/notebooks/zarr_sink_example.ipynb

@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4ba28d02",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "63c0c38f",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf6d0e9480cb43ef9851d7bd3ca7e356",
+       "model_id": "97c6787b148c4b4f9639d623f32f8a3a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "0e75e6de",
    "metadata": {},
    "outputs": [],
@@ -228,10 +228,22 @@
      "data": {
       "application/json": {
        "IndexRange": {
-        "IndexI": 3
+        "IndexFOOTPRINT": 3
        },
        "IndexStride": {
-        "IndexI": 1
+        "IndexFOOTPRINT": 1
+       },
+       "ValueFOOTPRINT": {
+        "datatype": "int64",
+        "max": 50,
+        "min": 1,
+        "uniform": true,
+        "units": null,
+        "values": [
+         1,
+         10,
+         50
+        ]
        },
        "bandCount": 3,
        "channelmap": {
@@ -246,19 +258,22 @@
          "Channel": "Band 1",
          "Frame": 0,
          "Index": 0,
-         "IndexI": 0
+         "IndexFOOTPRINT": 0,
+         "ValueFOOTPRINT": 1
         },
         {
          "Channel": "Band 1",
          "Frame": 1,
          "Index": 1,
-         "IndexI": 1
+         "IndexFOOTPRINT": 1,
+         "ValueFOOTPRINT": 10
         },
         {
          "Channel": "Band 1",
          "Frame": 2,
          "Index": 2,
-         "IndexI": 2
+         "IndexFOOTPRINT": 2,
+         "ValueFOOTPRINT": 50
         }
        ],
        "levels": 6,
@@ -281,16 +296,34 @@
        " 'mm_y': 0,\n",
        " 'dtype': 'float64',\n",
        " 'bandCount': 3,\n",
-       " 'frames': [{'Frame': 0, 'IndexI': 0, 'Index': 0, 'Channel': 'Band 1'},\n",
-       "  {'Frame': 1, 'IndexI': 1, 'Index': 1, 'Channel': 'Band 1'},\n",
-       "  {'Frame': 2, 'IndexI': 2, 'Index': 2, 'Channel': 'Band 1'}],\n",
-       " 'IndexRange': {'IndexI': 3},\n",
-       " 'IndexStride': {'IndexI': 1},\n",
+       " 'frames': [{'Frame': 0,\n",
+       "   'IndexFOOTPRINT': 0,\n",
+       "   'ValueFOOTPRINT': 1,\n",
+       "   'Index': 0,\n",
+       "   'Channel': 'Band 1'},\n",
+       "  {'Frame': 1,\n",
+       "   'IndexFOOTPRINT': 1,\n",
+       "   'ValueFOOTPRINT': 10,\n",
+       "   'Index': 1,\n",
+       "   'Channel': 'Band 1'},\n",
+       "  {'Frame': 2,\n",
+       "   'IndexFOOTPRINT': 2,\n",
+       "   'ValueFOOTPRINT': 50,\n",
+       "   'Index': 2,\n",
+       "   'Channel': 'Band 1'}],\n",
+       " 'ValueFOOTPRINT': {'values': [1, 10, 50],\n",
+       "  'uniform': True,\n",
+       "  'units': None,\n",
+       "  'min': 1,\n",
+       "  'max': 50,\n",
+       "  'datatype': 'int64'},\n",
+       " 'IndexRange': {'IndexFOOTPRINT': 3},\n",
+       " 'IndexStride': {'IndexFOOTPRINT': 1},\n",
        " 'channels': ['Band 1'],\n",
        " 'channelmap': {'Band 1': 0}}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,7 +348,7 @@
     "\n",
     "        # add modified tile to sink\n",
     "        # specify tile x, tile y, and any arbitrary frame parameters\n",
-    "        sink.addTile(processed_tile, x=tile['x'], y=tile['y'], i=i)\n",
+    "        sink.addTile(processed_tile, x=tile['x'], y=tile['y'], footprint=i, footprint_value=footprint_size)\n",
     "# view metadata\n",
     "sink.getMetadata()"
    ]
@@ -329,7 +362,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9b8e6175005d4af89cb4cfada7b72983",
+       "model_id": "fb60c2b967274a99a632f32f99389705",
        "version_major": 2,
        "version_minor": 0
       },
@@ -398,10 +431,10 @@
      "data": {
       "application/json": {
        "IndexRange": {
-        "IndexI": 3
+        "IndexFOOTPRINT": 3
        },
        "IndexStride": {
-        "IndexI": 1
+        "IndexFOOTPRINT": 1
        },
        "bandCount": 3,
        "channelmap": {
@@ -416,19 +449,19 @@
          "Channel": "Band 1",
          "Frame": 0,
          "Index": 0,
-         "IndexI": 0
+         "IndexFOOTPRINT": 0
         },
         {
          "Channel": "Band 1",
          "Frame": 1,
          "Index": 1,
-         "IndexI": 1
+         "IndexFOOTPRINT": 1
         },
         {
          "Channel": "Band 1",
          "Frame": 2,
          "Index": 2,
-         "IndexI": 2
+         "IndexFOOTPRINT": 2
         }
        ],
        "levels": 4,
@@ -451,16 +484,16 @@
        " 'mm_y': None,\n",
        " 'dtype': 'uint16',\n",
        " 'bandCount': 3,\n",
-       " 'frames': [{'Channel': 'Band 1', 'Frame': 0, 'Index': 0, 'IndexI': 0},\n",
-       "  {'Channel': 'Band 1', 'Frame': 1, 'Index': 1, 'IndexI': 1},\n",
-       "  {'Channel': 'Band 1', 'Frame': 2, 'Index': 2, 'IndexI': 2}],\n",
-       " 'IndexRange': {'IndexI': 3},\n",
-       " 'IndexStride': {'IndexI': 1},\n",
+       " 'frames': [{'Channel': 'Band 1', 'Frame': 0, 'Index': 0, 'IndexFOOTPRINT': 0},\n",
+       "  {'Channel': 'Band 1', 'Frame': 1, 'Index': 1, 'IndexFOOTPRINT': 1},\n",
+       "  {'Channel': 'Band 1', 'Frame': 2, 'Index': 2, 'IndexFOOTPRINT': 2}],\n",
+       " 'IndexRange': {'IndexFOOTPRINT': 3},\n",
+       " 'IndexStride': {'IndexFOOTPRINT': 1},\n",
        " 'channels': ['Band 1'],\n",
        " 'channelmap': {'Band 1': 0}}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -506,7 +539,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30812bb388a0426da9806e62bf5e8711",
+       "model_id": "a2d3df88fbd44079877b8f314ed97e6f",
        "version_major": 2,
        "version_minor": 0
       },

examples/algorithm_progression.py

@@ -86,12 +86,15 @@ def applyAlgorithm(self, sink, params):
             **tiparams,
         ):
             scaled = tile.get('scaled', 1)
+            axisparams = {
+                p['axis']: iteration_id[i] for i, p in enumerate(self.param_order.values())}
+            axisparams.update({
+                p['axis'] + '_value': params[i] for i, p in enumerate(self.param_order.values())})
             if self.overlay:
                 self.addTile(
                     tilesink,
                     tile['tile'], int(tile['x'] * scaled), int(tile['y'] * scaled),
-                    **{p['axis']: iteration_id[i] for i, p in enumerate(
-                        self.param_order.values())})
+                    **axisparams)
             altered_data = self.algorithm(tile['tile'], *params)
             mask = None
             if self.overlay:
@@ -104,7 +107,7 @@ def applyAlgorithm(self, sink, params):
             self.addTile(
                 tilesink,
                 altered_data, int(tile['x'] * scaled), int(tile['y'] * scaled), mask=mask,
-                **{p['axis']: iteration_id[i] for i, p in enumerate(self.param_order.values())})
+                **axisparams)
             if time.time() - lastlogtime > 10:
                 sys.stdout.write(
                     f'Processed {tile["tile_position"]["position"] + 1} of '

girder/girder_large_image/web_client/vue/components/FrameSelector.vue

@@ -36,6 +36,26 @@ export default Vue.extend({
         currentStyle() {
             const curStyle = this.style[this.currentModeId];
             return curStyle ? JSON.stringify(curStyle, null, null) : '';
+        },
+        sliderLabels() {
+            const labels = {};
+            labels.IndexC = this.imageMetadata.channels;
+            Object.entries(this.metadata).forEach(([key, info]) => {
+                if (key.includes('Value')) {
+                    const labelKey = key.replace('Value', 'Index');
+                    if (info.values) {
+                        if (info.uniform) {
+                            labels[labelKey] = info.values;
+                        } else {
+                            // non-uniform values have a value for every frame
+                            // labels will change with currentFrame, so only populate current label
+                            labels[labelKey] = new Array(this.indexInfo[labelKey].range + 1).fill('');
+                            labels[labelKey][this.indexInfo[labelKey].current] = info.values[this.currentFrame];
+                        }
+                    }
+                }
+            });
+            return labels;
         }
     },
     watch: {
@@ -328,7 +348,7 @@ export default Vue.extend({
         :current-value="indexInfo[index].current"
         :value-max="indexInfo[index].range"
         :label="index.replace('Index', '')"
-        :slider-labels="index === 'IndexC' ? imageMetadata.channels : []"
+        :slider-labels="sliderLabels[index]"
         :max-merge="indexInfo[index].maxMerge || false"
         @updateMaxMerge="(v) => updateMaxMergeAxis({index, maxMerge: v})"
         @updateValue="(v) => updateAxisSlider({index, frame: v})"