update

notebooks/Spectroscopy/Analyse_EELSs.ipynb

@@ -215,13 +215,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d511383382c64143831832038a6c540e",
+       "model_id": "19a19d099fcd4206ba1d147412f71e4b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -236,8 +236,7 @@
    "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",
+    "import pyTEMlib.info_widget    filename = '../../example_data/AL-DFoffset0.00.dm3'\n",
     "infoWidget= pyTEMlib.info_widget3.EELSWidget()\n"
    ]
   },

pyTEMlib/eels_tools.py

@@ -1180,38 +1180,39 @@ def find_associated_edges(dataset: sidpy.Dataset) -> None:
                     # 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:
-        white_lines = {}
-        for index, peak in dataset.metadata['peak_fit']['peaks'].items():
-            if index.isdigit():
-                if 'associated_edge' in peak:
-                    if peak['associated_edge'][-2:] in ['L3', 'L2', 'M5', 'M4']:
-                        if peak['distance_to_onset'] < 10:
-                            area = np.sqrt(2 * np.pi) * peak['amplitude'] * np.abs(peak['width']/np.sqrt(2 * np.log(2)))
-                            if peak['associated_edge'] not in white_lines:
-                                white_lines[peak['associated_edge']] = 0.
-                            if area > 0:
-                                white_lines[peak['associated_edge']] += area  # TODO: only positive ones?
-        white_line_ratios = {}
-        white_line_sum = {}
-        for sym, area in white_lines.items():
-            if sym[-2:] in ['L2', 'M4', 'M2']:
-                if area > 0 and f"{sym[:-1]}{int(sym[-1]) + 1}" in white_lines:
-                    if white_lines[f"{sym[:-1]}{int(sym[-1]) + 1}"] > 0:
-                        white_line_ratios[f"{sym}/{sym[-2]}{int(sym[-1]) + 1}"] = area / white_lines[
-                            f"{sym[:-1]}{int(sym[-1]) + 1}"]
-                        white_line_sum[f"{sym}+{sym[-2]}{int(sym[-1]) + 1}"] = (
-                                    area + white_lines[f"{sym[:-1]}{int(sym[-1]) + 1}"])
-
-                        areal_density = 1.
-                        if 'edges' in dataset.metadata:
-                            for key, edge in dataset.metadata['edges'].items():
-                                if key.isdigit():
-                                    if edge['element'] == sym.split('-')[0]:
-                                        areal_density = edge['areal_density']
-                                        break
-                        white_line_sum[f"{sym}+{sym[-2]}{int(sym[-1]) + 1}"] /= areal_density
+
+def find_white_lines(peaks: dict) -> None:
+
+    white_lines = {}
+    for index, peak in peaks['peaks'].items():
+        if index.isdigit():
+            if 'associated_edge' in peak:
+                if peak['associated_edge'][-2:] in ['L3', 'L2', 'M5', 'M4']:
+                    if peak['distance_to_onset'] < 10:
+                        area = np.sqrt(2 * np.pi) * peak['amplitude'] * np.abs(peak['width']/np.sqrt(2 * np.log(2)))
+                        if peak['associated_edge'] not in white_lines:
+                            white_lines[peak['associated_edge']] = 0.
+                        if area > 0:
+                            white_lines[peak['associated_edge']] += area  # TODO: only positive ones?
+    white_line_ratios = {}
+    white_line_sum = {}
+    for sym, area in white_lines.items():
+        if sym[-2:] in ['L2', 'M4', 'M2']:
+            if area > 0 and f"{sym[:-1]}{int(sym[-1]) + 1}" in white_lines:
+                if white_lines[f"{sym[:-1]}{int(sym[-1]) + 1}"] > 0:
+                    white_line_ratios[f"{sym}/{sym[-2]}{int(sym[-1]) + 1}"] = area / white_lines[
+                        f"{sym[:-1]}{int(sym[-1]) + 1}"]
+                    white_line_sum[f"{sym}+{sym[-2]}{int(sym[-1]) + 1}"] = (
+                                area + white_lines[f"{sym[:-1]}{int(sym[-1]) + 1}"])
+
+                    areal_density = 1.
+                    if 'edges' in dataset.metadata:
+                        for key, edge in dataset.metadata['edges'].items():
+                            if key.isdigit():
+                                if edge['element'] == sym.split('-')[0]:
+                                    areal_density = edge['areal_density']
+                                    break
+                    white_line_sum[f"{sym}+{sym[-2]}{int(sym[-1]) + 1}"] /= areal_density
 
         dataset.metadata['peak_fit']['white_lines'] = white_lines
         dataset.metadata['peak_fit']['white_line_ratios'] = white_line_ratios
@@ -1544,6 +1545,9 @@ def make_cross_sections(edges:dict, energy_scale:np.ndarray, e_0:float, coll_ang
     """
     for key in edges:
         if str(key).isdigit():
+            print(edges[key]['z'])
+            if edges[key]['z'] <1:
+                break
             edges[key]['data'] = xsec_xrpa(energy_scale, e_0 / 1000., edges[key]['z'], coll_angle,
                                            edges[key]['chemical_shift']) / 1e10  # from barnes to 1/nm^2
             if low_loss is not None:

setup.py

@@ -14,12 +14,12 @@
 with open(os.path.join(here, 'README.rst')) as f:
     long_description = f.read()
 
-with open(os.path.join(here, 'pyTEMlib/version.py')) as f:
+with open(os.path.join(here, './version.py')) as f:
     __version__ = f.read().split("'")[1]
 
 
 setuptools.setup(
-    name="pyTEMlib",
+    name="pycrosGUI",
     version=__version__,
     description="pyTEM: TEM Data Quantification library through a model-based approach",
     long_description=long_description,
@@ -32,7 +32,6 @@
         'Operating System :: OS Independent',
         'Programming Language :: Cython',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
         'Programming Language :: Python :: 3.11',
         'Programming Language :: Python :: 3.12',
@@ -43,22 +42,20 @@
               'storage', 'hdf5', 'diffraction'],
     packages=setuptools.find_packages(exclude=["*.tests", "*.tests.*",
                                                "tests.*", "tests"]),
-    url="https://pycroscopy.github.io/pyTEMlib/about.html",
+    url="https://pycroscopy.github.io/pycrosGUI/about.html",
     license='MIT',
     author="Gerd Duscher",
     author_email="[email protected]",
 
-    install_requires=['scipy', 'numpy', 'pillow',  'ase', 'tqdm', 'plotly', 'pandas', 'requests', 'lxml', 'ipympl',  
-                      'spglib', 'scikit-image', 'scikit-learn', 'pyNSID>=0.0.7', 'sidpy>=0.12.1', 'SciFiReaders>=0.0.8'],  # 'PyQt5> 1.0'],#
+    install_requires=['pyTEMlib', 'pyqt5', 'pyqtgraph'],  # 'PyQt5> 1.0'],#
     setup_requires=['pytest-runner'],
     tests_require=['pytest'],
     platforms=['Linux', 'Mac OSX', 'Windows 11/10'],
-    package_data={"pyTEMlib": ["data/*"]},
     test_suite='pytest',
     include_package_data=True,
     entry_points={
         'console_scripts': [
-            'pyTEMlib=pyTEMlib:main',
+            'pycrosGUI=pycrosGUI:main',
         ],
     },
 )