Merged in feature/RAM-3808_plotly (pull request #429)

Feature/RAM-3808 plotly

Approved-by: Randy Taylor
Approved-by: Hasan Ammar

docs/source/v4_changes.rst

@@ -48,11 +48,20 @@ Core
   * ``load_url``
   * ``.from_url(...)`` class methods. I've not seen a single use case for loading from a URL directly.
 
+Plotting
+--------
+
 * The plotting of analyzed images will migrate from ``matplotlib`` to ``plotly``.
   This is mainly related to being able to zoom in and otherwise examine an analyzed image
   more closely. Once plotly is supported, there is no reason to keep both plotting methods
   around. To reduce maintenance burden, the matplotlib plotting will be removed.
-
+* Methods involving saving figures (``.save_image(...)``) to file will be removed. Both matplotlib and plotly can
+  save to file programmatically, and plotly can do so interactively as well.
+* Plotting will always produce individual images. Pylinac would sometimes combine plots
+  in a subplot. This made for annoying figure sizing. Now, each plot will be its own figure.
+  E.g. for a 2D planar analysis, the plotting method will return up to 3 figures: the image,
+  the low-contrast plot, and the high-contrast plot. Previously, these would be plotted into
+  a single 3x1 subplot figure.
 
 
 Open-Field Analysis

pylinac/acr.py

@@ -10,6 +10,7 @@
 
 import numpy as np
 from matplotlib import pyplot as plt
+from plotly import graph_objects as go
 from pydantic import BaseModel, ConfigDict, Field
 from scipy import ndimage
 
@@ -41,6 +42,7 @@
 
 
 class CTModule(CatPhanModule):
+    common_name = "HU Linearity"
     attr_name = "ct_calibration_module"
     roi_dist_mm = 63
     roi_radius_mm = 10
@@ -184,6 +186,10 @@ def mtf(self) -> MTF:
             spacings=spacings, diskset=list(self.rois.values())
         )
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        for name, roi in self.rois.items():
+            roi.plotly(fig, color="green", name=name)
+
     def plot_rois(self, axis: plt.Axes) -> None:
         """Plot the ROIs to the axis. Override to set the color"""
         for roi, mtf in zip(self.rois.values(), self.mtf.norm_mtfs.values()):
@@ -317,6 +323,44 @@ def analyze(self) -> None:
             clear_borders=self.clear_borders,
         )
 
+    def plotly_analyzed_image(
+        self,
+        show: bool = True,
+        show_colorbar: bool = True,
+        show_legend: bool = True,
+        **kwargs,
+    ) -> dict[str, go.Figure]:
+        """Plot the analyzed image using Plotly. Will create multiple figures.
+
+        Parameters
+        ----------
+        show : bool
+            Whether to show the images. Set to False if doing further processing of the figure.
+        show_colorbar : bool
+            Whether to show the colorbar on the images.
+        show_legend : bool
+            Whether to show the legend on the images.
+        kwargs
+            Additional keyword arguments to pass to the figure.
+        """
+        figs = {}
+        for module in (
+            self.ct_calibration_module,
+            self.uniformity_module,
+            self.spatial_resolution_module,
+            self.low_contrast_module,
+        ):
+            figs[module.common_name] = module.plotly(
+                show_colorbar=show_colorbar, show_legend=show_legend, **kwargs
+            )
+        figs["MTF"] = self.spatial_resolution_module.mtf.plotly(**kwargs)
+        figs["Side View"] = self.plotly_side_view(offset=-0.5)
+
+        if show:
+            for fig in figs.values():
+                fig.show()
+        return figs
+
     def plot_analyzed_image(self, show: bool = True, **plt_kwargs) -> plt.Figure:
         """Plot the analyzed image
 
@@ -646,6 +690,10 @@ def plot_rois(self, axis: plt.Axes) -> None:
         for roi in self.rois.values():
             roi.plot2axes(axis, edgecolor="blue")
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        for name, roi in self.rois.items():
+            roi.plotly(fig, color="blue", name=name)
+
 
 class MRSlice11ModuleOutput(BaseModel):
     """This class should not be called directly. It is returned by the ``results_data()`` method.
@@ -739,6 +787,14 @@ def plot_rois(self, axis: plt.Axes) -> None:
         for roi, mtf in zip(self.rois.values(), self.rois.values()):
             roi.plot2axes(axis, edgecolor="g")
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        for name, roi in self.position_rois.items():
+            roi.plotly(fig, color="blue", name=name)
+        for name, roi in self.thickness_rois.items():
+            roi.plotly(fig, color="blue", name=name)
+        for name, roi in self.rois.items():
+            roi.plotly(fig, color="green", name=name)
+
     @property
     def bar_difference_mm(self) -> float:
         """The difference in height between the two angled bars"""
@@ -860,6 +916,11 @@ def plot_rois(self, axis: plt.Axes) -> None:
         for roi in self.ghost_rois.values():
             roi.plot2axes(axis, edgecolor="yellow")
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        super().plotly_rois(fig)
+        for name, roi in self.ghost_rois.items():
+            roi.plotly(fig, color="yellow", name=name)
+
     @property
     def percent_image_uniformity(self) -> float:
         """PIU value calculated via section 5.3 of the manual"""
@@ -1012,6 +1073,10 @@ def _setup_rois(self) -> None:
             "line": line,
         }
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        for name, profile_data in self.profiles.items():
+            profile_data["line"].plotly(fig, line_width=2, color="blue", name=name)
+
     def plot_rois(self, axis: plt.Axes):
         for name, profile_data in self.profiles.items():
             profile_data["line"].plot2axes(axis, width=2, color="blue")
@@ -1188,6 +1253,57 @@ def _select_echo_images(self, echo_number: int | None) -> None:
             del self.dicom_stack[idx]
             del self.dicom_stack.metadatas[idx]
 
+    def plotly_analyzed_image(
+        self,
+        show: bool = True,
+        show_colorbar: bool = True,
+        show_legend: bool = True,
+        **kwargs,
+    ) -> dict[str, go.Figure]:
+        """Plot the analyzed set of images to Plotly figures.
+
+        Parameters
+        ----------
+        show : bool
+            Whether to show the plot.
+        show_colorbar : bool
+            Whether to show the colorbar on the plot.
+        show_legend : bool
+            Whether to show the legend on the plot.
+        kwargs
+            Additional keyword arguments to pass to the plot.
+
+        Returns
+        -------
+        dict
+            A dictionary of the Plotly figures where the key is the name of the
+            image and the value is the figure.
+        """
+        figs = {}
+        # plot the images
+        for module in (
+            self.slice1,
+            self.geometric_distortion,
+            self.uniformity_module,
+            self.slice11,
+        ):
+            figs[module.common_name] = module.plotly(
+                show_colorbar=show_colorbar, show_legend=show_legend, **kwargs
+            )
+        # side view
+        figs["Side View"] = self.plotly_side_view(offset=-0.2)
+        # mtf
+        fig = go.Figure()
+        self.slice1.row_mtf.plotly(fig=fig, name="Row-wise rMTF")
+        figs["MTF"] = self.slice1.col_mtf.plotly(
+            fig=fig, name="Column-wise rMTF", marker_color="orange"
+        )
+
+        if show:
+            for fig in figs.values():
+                fig.show()
+        return figs
+
     def plot_analyzed_image(self, show: bool = True, **plt_kwargs) -> plt.Figure:
         """Plot the analyzed image
 

pylinac/cheese.py

@@ -7,9 +7,11 @@
 
 import numpy as np
 from matplotlib import pyplot as plt
+from plotly import graph_objects as go
 from pydantic import Field
 
 from .core import pdf
+from .core.plotly_utils import add_title
 from .core.profile import CollapsedCircleProfile
 from .core.roi import DiskROI
 from .core.scale import abs360
@@ -109,6 +111,15 @@ def plot_rois(self, axis: plt.Axes) -> None:
         for name, roi in self.rois.items():
             roi.plot2axes(axis, edgecolor="blue", text=name)
 
+    def plotly_rois(self, fig: go.Figure) -> None:
+        """Plot the ROIs to the figure. We add the ROI # to help the user differentiate"""
+        for name, roi in self.rois.items():
+            roi.plotly(
+                fig,
+                name=name,
+                color="blue",
+            )
+
 
 class TomoCheeseModule(CheeseModule):
     """The pluggable module with user-accessible holes.
@@ -292,6 +303,71 @@ def find_phantom_roll(self, func: Callable | None = None) -> float:
             )
             return 0
 
+    def plotly_analyzed_image(
+        self,
+        show: bool = True,
+        show_colorbar: bool = True,
+        show_legend: bool = True,
+        **kwargs,
+    ) -> dict[str, go.Figure]:
+        """Plot the analyzed set of images to Plotly figures.
+
+
+        Parameters
+        ----------
+        show : bool
+            Whether to show the plot.
+        show_colorbar : bool
+            Whether to show the colorbar on the plot.
+        show_legend : bool
+            Whether to show the legend on the plot.
+        kwargs
+            Additional keyword arguments to pass to the plot.
+
+        Returns
+        -------
+        dict
+            A dictionary of the Plotly figures where the key is the name of the
+            image and the value is the figure.
+        """
+        figs = {
+            self.module.common_name: self.module.plotly(
+                show_colorbar=show_colorbar, show_legend=show_legend, **kwargs
+            )
+        }
+
+        # density curve (if densities passed)
+        if self.roi_config:
+            xs = []
+            ys = []
+            for roi_num, roi_data in self.roi_config.items():
+                xs.append(roi_data["density"])
+                ys.append(self.module.rois[roi_num].pixel_value)
+            # sort by HU so it looks like a normal curve; ROI densities can be out of order
+            sorted_args = np.argsort(xs)
+            xs = np.array(xs)[sorted_args]
+            ys = np.array(ys)[sorted_args]
+            # plot
+            density_fig = go.Figure()
+            density_fig.add_scatter(
+                x=xs,
+                y=ys,
+                mode="lines+markers",
+                line_dash="dash",
+                marker_symbol="diamond",
+            )
+            density_fig.update_layout(
+                yaxis_title="HU",
+                xaxis_title="Density",
+            )
+            add_title(density_fig, "Density vs HU curve")
+            figs["Density vs HU curve"] = density_fig
+
+        if show:
+            for fig in figs.values():
+                fig.show()
+        return figs
+
     def plot_analyzed_image(self, show: bool = True, **plt_kwargs: dict) -> None:
         """Plot the images used in the calculation and summary data.