Merge pull request #193 from pycroscopy/plotly-support

update

.gitignore

@@ -258,3 +258,7 @@ example_data/sto.hf5
 example_data/sto2.hf5
 example_data/sto3.hf5
 example_data/00_sto.hf5
+example_data/00_sto-1.hf5
+example_data/00_sto-2.hf5
+example_data/00_sto-3.hf5
+example_data/2-STEM SI.dm4

notebooks/Spectroscopy/Analyse_EELSs.ipynb

@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-09-25T17:48:41.105440Z",
@@ -174,9 +174,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n",
-      "pyTEM version:  0.2024.09.1\n"
+      "You don't have igor2 installed.     If you wish to open igor files, you will need to install it     (pip install igor2) before attempting.\n",
+      "You don't have gwyfile installed.     If you wish to open .gwy files, you will need to      install it (pip install gwyfile) before attempting.\n",
+      "Symmetry functions of spglib enabled\n",
+      "Using kinematic_scattering library version {_version_ }  by G.Duscher\n",
+      "pyTEM version:  0.2025.01.0\n"
      ]
     }
    ],
@@ -219,7 +221,35 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "561a0dbaa40b4b348fe5a2b267f1e8b3",
+       "model_id": "d511383382c64143831832038a6c540e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AppLayout(children=(Tab(children=(GridspecLayout(children=(Dropdown(description='directory:', layout=Layout(gr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if 'google.colab' in sys.modules:\n",
+    "    drive.mount(\"/content/drive\")\n",
+    "import pyTEMlib.info_widget    \n",
+    "# filename = '../../example_data/AL-DFoffset0.00.dm3'\n",
+    "infoWidget= pyTEMlib.info_widget3.EELSWidget()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "802942432a224b15a984e401e278a074",
        "version_major": 2,
        "version_minor": 0
       },
@@ -233,7 +263,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d2cc9a74af445f89a3bd1e6e15034b3",
+       "model_id": "a47212009ee04ef0a1262cc8945b8bf4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1558,13 +1588,7 @@
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "if 'google.colab' in sys.modules:\n",
-    "    drive.mount(\"/content/drive\")\n",
-    "import pyTEMlib.info_widget    \n",
-    "# filename = '../../example_data/AL-DFoffset0.00.dm3'\n",
-    "infoWidget= pyTEMlib.info_widget3.EELSWidget()"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",

pyTEMlib/eels_tools.py

@@ -1153,36 +1153,32 @@ def find_all_edges(edge_onset: float, maximal_chemical_shift: float=5.0, major_e
                         else:
                             text += new_text
 
-    return text
-
+    return text         
 
 def find_associated_edges(dataset: sidpy.Dataset) -> None:
     onsets = []
     edges = []
-    if 'edges' in dataset.metadata:
-        for key, edge in dataset.metadata['edges'].items():
+    if 'core_loss' in dataset.metadata:
+        for key, edge in dataset.metadata['core_loss'].items():
             if key.isdigit():
-                element = edge['element']
-                pre_edge = 0.  # edge['onset']-edge['start_exclude']
-                post_edge = edge['end_exclude'] - edge['onset']
-
                 for sym in edge['all_edges']:  # TODO: Could be replaced with exclude
-                    onsets.append(edge['all_edges'][sym]['onset'] + edge['chemical_shift']-pre_edge)
-                    edges.append([key, f"{element}-{sym}", onsets[-1]])
-        for key, peak in dataset.metadata['peak_fit']['peaks'].items():
-            if key.isdigit():
-                distance = dataset.get_spectral_dims(return_axis=True)[0].values[-1]
-                index = -1
-                for ii, onset in enumerate(onsets):
-                    if onset < peak['position'] < onset+post_edge:
-                        if distance > np.abs(peak['position'] - onset):
-                            distance = np.abs(peak['position'] - onset)  # TODO: check whether absolute is good
-                            distance_onset = peak['position'] - onset
-                            index = ii
-                if index >= 0:
-                    peak['associated_edge'] = edges[index][1]  # check if more info is necessary
-                    peak['distance_to_onset'] = distance_onset
-
+                    onsets.append(edge['all_edges'][sym]['onset'] + edge['chemical_shift'])
+                    edges.append([key, f"{edge['element']}-{sym}", onsets[-1]])
+                dataset.metadata['core_loss'][key]['associated_peaks'] = {}
+        print(onsets)
+        print(edges)
+        if 'peak_fit' in dataset.metadata:
+            p = dataset.metadata['peak_fit']['peak_out_list']
+
+            for key, peak in enumerate(p):
+                distances  = onsets-peak[0]
+                distances[distances < -0.3] = 1e6
+                if np.min(distances) < 50:
+                    index = np.argmin(distances)
+                    dataset.metadata['core_loss'][edges[np.argmin(distances)][0]]['associated_peaks'][key] = edges[np.argmin(distances)][1]
+                        # check if more info is necessary
+                    # peak['distance_to_onset'] = np.min(distances)
+            print(dataset.metadata['core_loss'])
 
 def find_white_lines(dataset: sidpy.Dataset) -> None:
     if 'edges' in dataset.metadata:
@@ -2034,6 +2030,7 @@ def gaussian_mixture_model(dataset, p_in=None):
                 spectrum = dataset.view.get_spectrum()
             else:
                 spectrum = dataset[0,0]
+            spectrum.data_type == 'SPECTRUM'
         else:
             spectrum = dataset
         spectrum.data_type == 'SPECTRUM'