Merge pull request #5445 from FBumann/main

feat: Add facet_row support to px.imshow

Author: emilykl

CHANGELOG.md

@@ -4,6 +4,9 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
 
+### Added
+- Add `facet_row` support to `px.imshow` for creating subplots along an additional dimension [[#5445](https://github.com/plotly/plotly.py/pull/5445)]
+
 ### Fixed
 - Update `numpy.percentile` syntax to stop using deprecated alias [[5483](https://github.com/plotly/plotly.py/pull/5483)], with thanks to @Mr-Neutr0n for the contribution!
   - `numpy` with a version less than 1.22 is no longer supported.

plotly/express/_imshow.py

@@ -63,6 +63,7 @@ def imshow(
     y=None,
     animation_frame=None,
     facet_col=None,
+    facet_row=None,
     facet_col_wrap=None,
     facet_col_spacing=None,
     facet_row_spacing=None,
@@ -128,10 +129,15 @@ def imshow(
         axis number along which the image array is sliced to create a facetted plot.
         If `img` is an xarray, `facet_col` can be the name of one the dimensions.
 
+    facet_row: int or str, optional (default None)
+        axis number along which the image array is sliced to create a vertically
+        facetted plot. If `img` is an xarray, `facet_row` can be the name of one
+        the dimensions.
+
     facet_col_wrap: int
         Maximum number of facet columns. Wraps the column variable at this width,
         so that the column facets span multiple rows.
-        Ignored if `facet_col` is None.
+        Ignored if `facet_col` is None or if `facet_row` is set.
 
     facet_col_spacing: float between 0 and 1
         Spacing between facet columns, in paper units. Default is 0.02.
@@ -235,30 +241,46 @@ def imshow(
     args = locals()
     apply_default_cascade(args, constructor=None)
     labels = labels.copy()
-    nslices_facet = 1
+    nslices_facet_col = 1
+    nslices_facet_row = 1
+    facet_col_slices = None
+    facet_row_slices = None
     if facet_col is not None:
         if isinstance(facet_col, str):
             facet_col = img.dims.index(facet_col)
-        nslices_facet = img.shape[facet_col]
-        facet_slices = range(nslices_facet)
-        ncols = int(facet_col_wrap) if facet_col_wrap is not None else nslices_facet
+        nslices_facet_col = img.shape[facet_col]
+        facet_col_slices = range(nslices_facet_col)
+    if facet_row is not None:
+        if isinstance(facet_row, str):
+            facet_row = img.dims.index(facet_row)
+        nslices_facet_row = img.shape[facet_row]
+        facet_row_slices = range(nslices_facet_row)
+    # ignore facet_col_wrap when facet_row is set
+    if facet_row is not None:
+        facet_col_wrap = None
+
+    if facet_col_wrap is None:
+        ncols = nslices_facet_col
+        nrows = nslices_facet_row
+    else:
+        ncols = min(int(facet_col_wrap), nslices_facet_col)
         nrows = (
-            nslices_facet // ncols + 1
-            if nslices_facet % ncols
-            else nslices_facet // ncols
+            nslices_facet_col // ncols + 1
+            if nslices_facet_col % ncols
+            else nslices_facet_col // ncols
         )
-    else:
-        nrows = 1
-        ncols = 1
     if animation_frame is not None:
         if isinstance(animation_frame, str):
             animation_frame = img.dims.index(animation_frame)
         nslices_animation = img.shape[animation_frame]
         animation_slices = range(nslices_animation)
-    slice_dimensions = (facet_col is not None) + (
-        animation_frame is not None
-    )  # 0, 1, or 2
-    facet_label = None
+    slice_dimensions = (
+        (facet_col is not None)
+        + (facet_row is not None)
+        + (animation_frame is not None)
+    )  # 0, 1, 2, or 3
+    facet_col_label = None
+    facet_row_label = None
     animation_label = None
     img_is_xarray = False
     # ----- Define x and y, set labels if img is an xarray -------------------
@@ -267,9 +289,13 @@ def imshow(
         img_is_xarray = True
         pop_indexes = []
         if facet_col is not None:
-            facet_slices = img.coords[img.dims[facet_col]].values
+            facet_col_slices = img.coords[img.dims[facet_col]].values
             pop_indexes.append(facet_col)
-            facet_label = img.dims[facet_col]
+            facet_col_label = img.dims[facet_col]
+        if facet_row is not None:
+            facet_row_slices = img.coords[img.dims[facet_row]].values
+            pop_indexes.append(facet_row)
+            facet_row_label = img.dims[facet_row]
         if animation_frame is not None:
             animation_slices = img.coords[img.dims[animation_frame]].values
             pop_indexes.append(animation_frame)
@@ -295,7 +321,9 @@ def imshow(
         if labels.get("animation_frame", None) is None:
             labels["animation_frame"] = animation_label
         if labels.get("facet_col", None) is None:
-            labels["facet_col"] = facet_label
+            labels["facet_col"] = facet_col_label
+        if labels.get("facet_row", None) is None:
+            labels["facet_row"] = facet_row_label
         if labels.get("color", None) is None:
             labels["color"] = xarray.plot.utils.label_from_attrs(img)
             labels["color"] = labels["color"].replace("\n", "<br>")
@@ -331,12 +359,20 @@ def imshow(
 
     # --------------- Starting from here img is always a numpy array --------
     img = np.asanyarray(img)
-    # Reshape array so that animation dimension comes first, then facets, then images
+    # Reshape array so that animation dimension comes first, then facet_row, then facet_col, then images
+    # We move axes to front in reverse order so each axis ends up at position 0 in the final order
     if facet_col is not None:
         img = np.moveaxis(img, facet_col, 0)
         if animation_frame is not None and animation_frame < facet_col:
             animation_frame += 1
+        if facet_row is not None and facet_row < facet_col:
+            facet_row += 1
         facet_col = True
+    if facet_row is not None:
+        img = np.moveaxis(img, facet_row, 0)
+        if animation_frame is not None and animation_frame < facet_row:
+            animation_frame += 1
+        facet_row = True
     if animation_frame is not None:
         img = np.moveaxis(img, animation_frame, 0)
         animation_frame = True
@@ -348,8 +384,10 @@ def imshow(
     iterables = ()
     if animation_frame is not None:
         iterables += (range(nslices_animation),)
+    if facet_row is not None:
+        iterables += (range(nslices_facet_row),)
     if facet_col is not None:
-        iterables += (range(nslices_facet),)
+        iterables += (range(nslices_facet_col),)
 
     # Default behaviour of binary_string: True for RGB images, False for 2D
     if binary_string is None:
@@ -535,19 +573,25 @@ def imshow(
         raise ValueError(
             "px.imshow only accepts 2D single-channel, RGB or RGBA images. "
             "An image of shape %s was provided. "
-            "Alternatively, 3- or 4-D single or multichannel datasets can be "
-            "visualized using the `facet_col` or/and `animation_frame` arguments."
+            "Alternatively, 3-, 4-, or 5-D single or multichannel datasets can be "
+            "visualized using the `facet_col`, `facet_row`, and/or `animation_frame` arguments."
             % str(img.shape)
         )
 
     # Now build figure
     col_labels = []
+    row_labels = []
     if facet_col is not None:
         slice_label = (
             "facet_col" if labels.get("facet_col") is None else labels["facet_col"]
         )
-        col_labels = [f"{slice_label}={i}" for i in facet_slices]
-    fig = init_figure(args, "xy", [], nrows, ncols, col_labels, [])
+        col_labels = [f"{slice_label}={i}" for i in facet_col_slices]
+    if facet_row is not None:
+        slice_label = (
+            "facet_row" if labels.get("facet_row") is None else labels["facet_row"]
+        )
+        row_labels = [f"{slice_label}={i}" for i in facet_row_slices]
+    fig = init_figure(args, "xy", [], nrows, ncols, col_labels, row_labels)
     for attr_name in ["height", "width"]:
         if args[attr_name]:
             layout[attr_name] = args[attr_name]
@@ -556,15 +600,22 @@ def imshow(
     elif args["template"].layout.margin.t is None:
         layout["margin"] = {"t": 60}
 
+    nslices_facets = nslices_facet_row * nslices_facet_col
     frame_list = []
     for index, trace in enumerate(traces):
-        if (facet_col and index < nrows * ncols) or index == 0:
-            fig.add_trace(trace, row=nrows - index // ncols, col=index % ncols + 1)
+        if ((facet_col or facet_row) and index < nrows * ncols) or index == 0:
+            # Calculate row and col position
+            # index is ordered by (facet_row, facet_col) from itertools.product
+            # When facet_col_wrap is used (and facet_row is None), traces are laid out
+            # across wrapped columns, so we use ncols for the calculation
+            row_idx = index // ncols
+            col_idx = index % ncols
+            fig.add_trace(trace, row=nrows - row_idx, col=col_idx + 1)
     if animation_frame is not None:
         for i, index in zip(range(nslices_animation), animation_slices):
             frame_list.append(
                 dict(
-                    data=traces[nslices_facet * i : nslices_facet * (i + 1)],
+                    data=traces[nslices_facets * i : nslices_facets * (i + 1)],
                     layout=layout,
                     name=str(index),
                 )

tests/test_optional/test_px/test_imshow.py

@@ -450,3 +450,89 @@ def test_animation_and_facet(binary_string):
     nslices = img.shape[0]
     assert len(fig.frames) == nslices
     assert len(fig.data) == img.shape[1]
+
+
+@pytest.mark.parametrize("facet_row", [0, 1, 2, -1])
+@pytest.mark.parametrize("binary_string", [False, True])
+def test_facet_row(facet_row, binary_string):
+    img = np.random.randint(255, size=(10, 9, 8))
+    fig = px.imshow(
+        img,
+        facet_row=facet_row,
+        binary_string=binary_string,
+    )
+    nslices = img.shape[facet_row]
+    nrows = nslices
+    ncols = 1
+    nmax = ncols * nrows
+    assert "yaxis%d" % nmax in fig.layout
+    assert "yaxis%d" % (nmax + 1) not in fig.layout
+    assert len(fig.data) == nslices
+
+
+@pytest.mark.parametrize("binary_string", [False, True])
+def test_facet_row_and_col(binary_string):
+    img = np.random.randint(255, size=(4, 3, 9, 8))
+    fig = px.imshow(
+        img,
+        facet_row=0,
+        facet_col=1,
+        binary_string=binary_string,
+    )
+    nrows = img.shape[0]
+    ncols = img.shape[1]
+    nmax = ncols * nrows
+    assert "yaxis%d" % nmax in fig.layout
+    assert "yaxis%d" % (nmax + 1) not in fig.layout
+    assert len(fig.data) == nrows * ncols
+
+
+@pytest.mark.parametrize("binary_string", [False, True])
+def test_animation_facet_row_and_col(binary_string):
+    img = np.random.randint(255, size=(5, 4, 3, 9, 8)).astype(np.uint8)
+    fig = px.imshow(
+        img,
+        animation_frame=0,
+        facet_row=1,
+        facet_col=2,
+        binary_string=binary_string,
+    )
+    nslices_animation = img.shape[0]
+    nrows = img.shape[1]
+    ncols = img.shape[2]
+    assert len(fig.frames) == nslices_animation
+    assert len(fig.data) == nrows * ncols
+
+
+def test_imshow_xarray_facet_row():
+    img = np.random.random((3, 4, 5))
+    da = xr.DataArray(
+        img, dims=["row_dim", "dim_1", "dim_2"], coords={"row_dim": ["A", "B", "C"]}
+    )
+    fig = px.imshow(da, facet_row="row_dim")
+    # Dimensions are used for axis labels and coordinates
+    assert fig.layout.xaxis.title.text == "dim_2"
+    assert fig.layout.yaxis.title.text == "dim_1"
+    assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_2"]))
+    assert len(fig.data) == 3
+    # Check row labels are present
+    annotations = [a.text for a in fig.layout.annotations]
+    assert any("row_dim=A" in a for a in annotations)
+
+
+def test_imshow_xarray_facet_row_and_col():
+    img = np.random.random((3, 4, 5, 6))
+    da = xr.DataArray(
+        img,
+        dims=["row_dim", "col_dim", "dim_y", "dim_x"],
+        coords={"row_dim": ["R1", "R2", "R3"], "col_dim": ["C1", "C2", "C3", "C4"]},
+    )
+    fig = px.imshow(da, facet_row="row_dim", facet_col="col_dim")
+    # Dimensions are used for axis labels and coordinates
+    assert fig.layout.xaxis.title.text == "dim_x"
+    assert fig.layout.yaxis.title.text == "dim_y"
+    assert len(fig.data) == 3 * 4
+    # Check labels are present
+    annotations = [a.text for a in fig.layout.annotations]
+    assert any("row_dim=R1" in a for a in annotations)
+    assert any("col_dim=C1" in a for a in annotations)