added residual plot and cleaned up a little

Makefile

@@ -0,0 +1,7 @@
+.PHONY: test
+
+test:
+	pytest test/test_core.py
+	pytest test/test_main.py
+	pytest test/test_fit.py
+	pytest test/test_cli.py

peakipy/cli/check_panel.py

@@ -56,9 +56,15 @@ def get_cluster(cluster):
     return df_pane
 
 
+@dataclass
+class PlotContainer:
+    main_figure: pn.pane.Plotly
+    residual_figure: pn.pane.Plotly
+
+
 def create_plotly_pane(cluster, plane):
     data = data_singleton()
-    fig = check(
+    fig, residual_fig = check(
         fits=data.fits_path,
         data_path=data.data_path,
         clusters=[cluster],
@@ -69,7 +75,8 @@ def create_plotly_pane(cluster, plane):
 
     fig["layout"].update(height=800, width=800)
     fig = fig.to_dict()
-    return pn.pane.Plotly(fig)
+    residual_fig = residual_fig.to_dict()
+    return pn.Column(pn.pane.Plotly(fig), pn.pane.Plotly(residual_fig))
 
 
 app = typer.Typer()
@@ -96,22 +103,23 @@ def create_check_panel(
     select_plane = pn.widgets.Select(
         name="Plane", options={f"{plane}": plane for plane in data.df.plane.unique()}
     )
-    interactive_cluster_pane = pn.bind(get_cluster, select_cluster)
+    result_table_pane = pn.bind(get_cluster, select_cluster)
     interactive_plotly_pane = pn.bind(
         create_plotly_pane, cluster=select_cluster, plane=select_plane
     )
     info_pane = pn.pane.Markdown(
         "Select a cluster and plane to look at from the dropdown menus"
     )
     check_pane = pn.Card(
-        info_pane,
-        pn.Row(select_cluster, select_plane),
+        # info_pane,
+        # pn.Row(select_cluster, select_plane),
         pn.Row(
             pn.Column(
-                pn.Card(interactive_plotly_pane, title="Fitted cluster"),
-                pn.Card(
-                    interactive_cluster_pane, title="Fitted parameters for cluster"
+                pn.Row(
+                    pn.Card(interactive_plotly_pane, title="Fitted cluster"),
+                    pn.Column(info_pane, select_cluster, select_plane),
                 ),
+                pn.Card(result_table_pane, title="Fitted parameters for cluster"),
             )
         ),
         title="Peakipy check",

peakipy/cli/edit.py

@@ -17,21 +17,14 @@
 
 import panel as pn
 
-from bokeh.io import curdoc
 from bokeh.events import ButtonClick, DoubleTap
-from bokeh.layouts import row, column, grid
-from bokeh.models import ColumnDataSource, Tabs, TabPanel, InlineStyleSheet
+from bokeh.layouts import row, column
+from bokeh.models import ColumnDataSource
 from bokeh.models.tools import HoverTool
 from bokeh.models.widgets import (
     Slider,
     Select,
     Button,
-    DataTable,
-    TableColumn,
-    NumberFormatter,
-    NumberEditor,
-    IntEditor,
-    SelectEditor,
     TextInput,
     RadioButtonGroup,
     CheckboxGroup,
@@ -136,9 +129,15 @@ def tabulator_columns(self):
         ]
         return columns
 
+    @property
+    def tabulator_non_editable_columns(self):
+        editors = {"X_RADIUS_PPM": None, "Y_RADIUS_PPM": None}
+        return editors
+
     def make_tabulator_widget(self):
         self.tablulator_widget = pn.widgets.Tabulator(
             self.peakipy_data.df[self.tabulator_columns],
+            editors=self.tabulator_non_editable_columns,
         )
         return self.tablulator_widget
 
@@ -321,10 +320,7 @@ def setup_plot(self):
         self.contour_start = TextInput(
             value="%.2e" % self.thres, title="Contour level:", width=100
         )
-        # contour_factor = Slider(title="contour factor", value=1.20, start=1., end=2.,step=0.05)
         self.contour_start.on_change("value", self.update_contour)
-        # for w in [contour_num,contour_start,contour_factor]:
-        #    w.on_change("value",update_contour)
 
         #  plot mask outlines
         el = self.p.ellipse(
@@ -540,13 +536,6 @@ def fit_selected(self, event):
         selectionIndex = self.source.selected.indices
         current = self.peakipy_data.df.iloc[selectionIndex]
 
-        # self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = (
-        #     self.slider_X_RADIUS.value
-        # )
-        # self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = (
-        #     self.slider_Y_RADIUS.value
-        # )
-
         self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = (
             current["X_RADIUS_PPM"] * 2.0
         )

peakipy/cli/edit_panel.py

@@ -102,8 +102,8 @@ def fit_peaks_button_click(event):
     button.on_click(fit_peaks_button_click)
 
     def update_source_selected_indices(event):
-        print(event)
-        print(bs.tablulator_widget.selection)
+        # print(event)
+        # print(bs.tablulator_widget.selection)
         bs.source.selected.indices = bs.tablulator_widget.selection
 
     bs.tablulator_widget.on_click(update_source_selected_indices)

peakipy/cli/main.py

@@ -215,10 +215,6 @@ def read(
 
     """
 
-    # verbose_mode = args.get("--verb")
-    # if verbose_mode:
-    #    print("Using arguments:", args)
-
     clust_args = {
         "struc_el": struc_el,
         "struc_size": struc_size,
@@ -241,7 +237,6 @@ def read(
                 posF1=y_ppm_column_name,
                 posF2=x_ppm_column_name,
             )
-            # peaks.adaptive_clusters(block_size=151,offset=0)
 
         case peaklist_format.a3:
             peaks = Peaklist(
@@ -292,9 +287,6 @@ def read(
     if fuda:
         peaks.to_fuda()
 
-    # if verbose_mode:
-    #    print(data.head())
-
     match outfmt.value:
         case "csv":
             outname = outname.with_suffix(".csv")
@@ -1053,12 +1045,10 @@ def create_matplotlib_figure(
         # axes will appear inverted
         ax.view_init(30, 120)
 
-        # names = ",".join(plane.assignment)
         title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane_lineshape_parameters.clustid.iloc[0]}"
         plt.title(title)
         print(f"[green]Plotting: {title}[/green]")
         out_str = "Volumes (Heights)\n===========\n"
-        # chi2s = []
         for _, row in plot_data.plane_lineshape_parameters.iterrows():
             out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n"
             if label:
@@ -1199,20 +1189,30 @@ def create_plotly_surfaces(plot_data: PlottingDataForPlane):
 
 
 def create_residual_contours(plot_data: PlottingDataForPlane):
-    data = []
-    contours = go.Contour(x=plot_data.x_plot, y=plot_data.y_plot, z=plot_data.residual)
-    data.append(contours)
-    return data
+    contours = go.Contour(
+        x=plot_data.x_plot[0], y=plot_data.y_plot.T[0], z=plot_data.residual
+    )
+    return contours
+
+
+def create_residual_figure(plot_data: PlottingDataForPlane):
+    data = create_residual_contours(plot_data)
+    fig = go.Figure(data=data)
+    fig.update_layout(
+        title="Fit residuals",
+        xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm",
+        yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm",
+        xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]),
+        yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]),
+    )
+    return fig
 
 
 def create_plotly_figure(plot_data: PlottingDataForPlane):
     lines = create_plotly_wireframe_lines(plot_data)
     surfaces = create_plotly_surfaces(plot_data)
     # residuals = create_residual_contours(plot_data)
     fig = go.Figure(data=lines + surfaces)
-    # layout = go.Layout(showlegend=True)
-    # fig.update_layout(layout)
-    # fig.update_traces(showlegend=True)
     fig = update_axis_ranges(fig, plot_data)
     return fig
 
@@ -1222,6 +1222,8 @@ def update_axis_ranges(fig, plot_data: PlottingDataForPlane):
         scene=dict(
             xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]),
             yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]),
+            xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm",
+            yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm",
             annotations=make_annotations(plot_data),
         )
     )
@@ -1474,41 +1476,19 @@ def check(
                 # fig = create_plotly_figure(plot_data)
                 if plotly:
                     fig = create_plotly_figure(plot_data)
-                    return fig
+                    residual_fig = create_residual_figure(plot_data)
+                    return fig, residual_fig
                 else:
                     plt = matplotlib.pyplot
                     create_matplotlib_figure(
                         plot_data, pdf, individual, label, ccpn_flag, show
                     )
-                # surf = pn.pane.plotly.Plotly(fig)
-                # app = pn.Column(surf)
-                # app.show(threaded=True)
                 if first:
                     break
 
     run_log()
 
 
-@app.command(help="Interactive Bokeh dashboard for configuring fitting parameters")
-def edit(
-    peaklist_path: Path,
-    data_path: Path,
-    test: bool = False,
-):
-    from bokeh.util.browser import view
-    from bokeh.server.server import Server
-    from .edit import BokehScript
-
-    run_log()
-    bs = BokehScript(peaklist_path=peaklist_path, data_path=data_path)
-    if not test:
-        server = Server({"/edit": bs.init})
-        server.start()
-        print("[green]Opening peakipy: Edit fits on http://localhost:5006/edit[/green]")
-        server.io_loop.add_callback(server.show, "/edit")
-        server.io_loop.start()
-
-
 def make_yaml_file(name, yaml_file=yaml_file):
     if os.path.exists(name):
         print(f"Copying {name} to {name}.bak")

peakipy/core.py

@@ -23,7 +23,7 @@
 import json
 from datetime import datetime
 from pathlib import Path
-from typing import List, Optional
+from typing import List
 from enum import Enum
 from dataclasses import dataclass, field
 
@@ -1515,28 +1515,6 @@ def clusters(
         )
         return ClustersResult(labeled_array, num_features, closed_data, peaks)
 
-    # def adaptive_clusters(self, block_size, offset, l_struc=None):
-
-    #     self.thresh = threshold_otsu(self.data[0])
-
-    #     peaks = [[y, x] for y, x in zip(self.df.Y_AXIS, self.df.X_AXIS)]
-
-    #     binary_adaptive = threshold_adaptive(
-    #         self.data[0], block_size=block_size, offset=offset
-    #     )
-
-    #     labeled_array, num_features = ndimage.label(binary_adaptive, l_struc)
-    #     # print(labeled_array, num_features)
-
-    #     self.df["CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks]
-
-    #     #  renumber "0" clusters
-    #     max_clustid = self.df["CLUSTID"].max()
-    #     n_of_zeros = len(self.df[self.df["CLUSTID"] == 0]["CLUSTID"])
-    #     self.df.loc[self.df[self.df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange(
-    #         max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int
-    #     )
-
     def mask_method(self, overlap=1.0, l_struc=None):
         """connect clusters based on overlap of fitting masks